

'''simple docstring'''


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	__SCREAMING_SNAKE_CASE  =			'platform'

	import jax
	import jax.numpy as jnp

	from transformers import AutoTokenizer
	from transformers.models.mbart.modeling_flax_mbart import (
	    FlaxMBartForConditionalGeneration,
	    FlaxMBartForQuestionAnswering,
	    FlaxMBartForSequenceClassification,
	    FlaxMBartModel,
	    shift_tokens_right,
	)
def 				__magic_name__(					UpperCAmelCase__   :					Tuple       ,							UpperCAmelCase__   :					List[str]       ,							UpperCAmelCase__   :					Any=None       ,							UpperCAmelCase__   :					Optional[Any]=None       ,							UpperCAmelCase__   :					int=None       ,							UpperCAmelCase__   :					int=None       ,							UpperCAmelCase__   :					Optional[Any]=None       ,							UpperCAmelCase__   :					List[str]=None       ,							):



						if attention_mask is None:
												UpperCamelCase_    :  str				    =    np.where(input_ids != config.pad_token_id       ,							1       ,							0  )
						if decoder_attention_mask is None:
												UpperCamelCase_    :  Tuple				    =    np.where(decoder_input_ids != config.pad_token_id       ,							1       ,							0  )
						if head_mask is None:
												UpperCamelCase_    :  Optional[Any]				    =    np.ones((config.encoder_layers, config.encoder_attention_heads)  )
						if decoder_head_mask is None:
												UpperCamelCase_    :  Union[str, Any]				    =    np.ones((config.decoder_layers, config.decoder_attention_heads)  )
						if cross_attn_head_mask is None:
												UpperCamelCase_    :  str				    =    np.ones((config.decoder_layers, config.decoder_attention_heads)  )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						}




class 						__snake_case (						unittest.TestCase  ):




		def __init__(     self				:str ,						_SCREAMING_SNAKE_CASE				:int ,						_SCREAMING_SNAKE_CASE				:List[Any]=1_3 ,						_SCREAMING_SNAKE_CASE				:List[str]=7 ,						_SCREAMING_SNAKE_CASE				:Dict=True ,						_SCREAMING_SNAKE_CASE				:Dict=False ,						_SCREAMING_SNAKE_CASE				:str=9_9 ,						_SCREAMING_SNAKE_CASE				:Tuple=1_6 ,						_SCREAMING_SNAKE_CASE				:List[str]=2 ,						_SCREAMING_SNAKE_CASE				:List[str]=4 ,						_SCREAMING_SNAKE_CASE				:Optional[int]=4 ,						_SCREAMING_SNAKE_CASE				:List[str]="gelu" ,						_SCREAMING_SNAKE_CASE				:List[str]=0.1 ,						_SCREAMING_SNAKE_CASE				:Optional[Any]=0.1 ,						_SCREAMING_SNAKE_CASE				:List[str]=3_2 ,						_SCREAMING_SNAKE_CASE				:Optional[Any]=2 ,						_SCREAMING_SNAKE_CASE				:int=1 ,						_SCREAMING_SNAKE_CASE				:Tuple=0 ,						_SCREAMING_SNAKE_CASE				:List[str]=2 ,						_SCREAMING_SNAKE_CASE				:List[Any]=0.02 ,						)				-> Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_    :  Any				    =    parent
								UpperCamelCase_    :  Optional[Any]				    =    batch_size
								UpperCamelCase_    :  List[str]				    =    seq_length
								UpperCamelCase_    :  int				    =    is_training
								UpperCamelCase_    :  str				    =    use_labels
								UpperCamelCase_    :  str				    =    vocab_size
								UpperCamelCase_    :  Dict				    =    hidden_size
								UpperCamelCase_    :  Optional[Any]				    =    num_hidden_layers
								UpperCamelCase_    :  int				    =    num_attention_heads
								UpperCamelCase_    :  str				    =    intermediate_size
								UpperCamelCase_    :  int				    =    hidden_act
								UpperCamelCase_    :  str				    =    hidden_dropout_prob
								UpperCamelCase_    :  Union[str, Any]				    =    attention_probs_dropout_prob
								UpperCamelCase_    :  int				    =    max_position_embeddings
								UpperCamelCase_    :  Optional[int]				    =    eos_token_id
								UpperCamelCase_    :  Union[str, Any]				    =    pad_token_id
								UpperCamelCase_    :  int				    =    bos_token_id
								UpperCamelCase_    :  List[str]				    =    decoder_start_token_id
								UpperCamelCase_    :  List[Any]				    =    initializer_range




		def   _snake_case  (     self				:Tuple)				-> Optional[int]:






								'''simple docstring'''






								UpperCamelCase_    :  Union[str, Any]				    =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,						self.vocab_size) ,						3 ,						self.vocab_size)
								UpperCamelCase_    :  List[str]				    =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,						dtype=np.intaa)) ,						-1)

								UpperCamelCase_    :  Dict				    =    shift_tokens_right(_SCREAMING_SNAKE_CASE ,						1)

								UpperCamelCase_    :  Dict				    =    MBartConfig(
								    vocab_size=self.vocab_size ,						d_model=self.hidden_size ,						encoder_layers=self.num_hidden_layers ,						decoder_layers=self.num_hidden_layers ,						encoder_attention_heads=self.num_attention_heads ,						decoder_attention_heads=self.num_attention_heads ,						encoder_ffn_dim=self.intermediate_size ,						decoder_ffn_dim=self.intermediate_size ,						dropout=self.hidden_dropout_prob ,						attention_dropout=self.attention_probs_dropout_prob ,						max_position_embeddings=self.max_position_embeddings ,						eos_token_id=self.eos_token_id ,						bos_token_id=self.bos_token_id ,						pad_token_id=self.pad_token_id ,						decoder_start_token_id=self.decoder_start_token_id ,						initializer_range=self.initializer_range ,						use_cache=_SCREAMING_SNAKE_CASE ,						)
								UpperCamelCase_    :  Optional[Any]				    =    prepare_mbart_inputs_dict(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
								return config, inputs_dict




		def   _snake_case  (     self				:Any)				-> Any:






								'''simple docstring'''






								UpperCamelCase_    ,UpperCamelCase_    :  Any				    =    self.prepare_config_and_inputs()
								return config, inputs_dict




		def   _snake_case  (     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:List[str] ,						_SCREAMING_SNAKE_CASE				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:List[Any])				-> List[str]:






								'''simple docstring'''






								UpperCamelCase_    :  Union[str, Any]				    =    2_0
								UpperCamelCase_    :  Optional[Any]				    =    model_class_name(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  int				    =    model.encode(inputs_dict["""input_ids"""])

								UpperCamelCase_    ,UpperCamelCase_    :  Tuple				    =    (
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

								UpperCamelCase_    :  int				    =    model.init_cache(decoder_input_ids.shape[0] ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Optional[int]				    =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,						dtype="""i4""")

								UpperCamelCase_    :  Union[str, Any]				    =    jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,						)
								UpperCamelCase_    :  Tuple				    =    model.decode(
								    decoder_input_ids[:, :-1] ,						_SCREAMING_SNAKE_CASE ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE ,						past_key_values=_SCREAMING_SNAKE_CASE ,						decoder_position_ids=_SCREAMING_SNAKE_CASE ,						)

								UpperCamelCase_    :  List[Any]				    =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,						dtype="""i4""")
								UpperCamelCase_    :  List[Any]				    =    model.decode(
								    decoder_input_ids[:, -1:] ,						_SCREAMING_SNAKE_CASE ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE ,						past_key_values=outputs_cache.past_key_values ,						decoder_position_ids=_SCREAMING_SNAKE_CASE ,						)

								UpperCamelCase_    :  str				    =    model.decode(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  int				    =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
								self.parent.assertTrue(diff < 1E-3 ,						msg=F"""Max diff is {diff}""")





		def   _snake_case  (     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Tuple ,						_SCREAMING_SNAKE_CASE				:Optional[int] ,						_SCREAMING_SNAKE_CASE				:Optional[Any])				-> List[str]:






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    2_0
								UpperCamelCase_    :  str				    =    model_class_name(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  List[Any]				    =    model.encode(inputs_dict["""input_ids"""])

								UpperCamelCase_    ,UpperCamelCase_    :  List[Any]				    =    (
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

								UpperCamelCase_    :  List[Any]				    =    jnp.concatenate(
								    [
								        decoder_attention_mask,
								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
								    ] ,						axis=-1 ,						)

								UpperCamelCase_    :  List[Any]				    =    model.init_cache(decoder_input_ids.shape[0] ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Dict				    =    jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,						)

								UpperCamelCase_    :  List[str]				    =    model.decode(
								    decoder_input_ids[:, :-1] ,						_SCREAMING_SNAKE_CASE ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE ,						past_key_values=_SCREAMING_SNAKE_CASE ,						decoder_position_ids=_SCREAMING_SNAKE_CASE ,						)
								UpperCamelCase_    :  Optional[Any]				    =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,						dtype="""i4""")
								UpperCamelCase_    :  Optional[Any]				    =    model.decode(
								    decoder_input_ids[:, -1:] ,						_SCREAMING_SNAKE_CASE ,						past_key_values=outputs_cache.past_key_values ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE ,						decoder_position_ids=_SCREAMING_SNAKE_CASE ,						)

								UpperCamelCase_    :  Any				    =    model.decode(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_    :  List[str]				    =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
								self.parent.assertTrue(diff < 1E-3 ,						msg=F"""Max diff is {diff}""")







@require_flax
class 						__snake_case (						unittest.TestCase  ):
		a_        =				99




		def   _snake_case  (     self				:Optional[Any])				-> Tuple:






								'''simple docstring'''






								UpperCamelCase_    :  Dict				    =    np.array(
								    [
								        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
								        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
								        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
								        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
								        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
								        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
								        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
								        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
								        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
								        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
								        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
								        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
								        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
								    ] ,						dtype=np.intaa ,						)

								UpperCamelCase_    :  List[str]				    =    input_ids.shape[0]
								UpperCamelCase_    :  List[Any]				    =    MBartConfig(
								    vocab_size=self.vocab_size ,						d_model=2_4 ,						encoder_layers=2 ,						decoder_layers=2 ,						encoder_attention_heads=2 ,						decoder_attention_heads=2 ,						encoder_ffn_dim=3_2 ,						decoder_ffn_dim=3_2 ,						max_position_embeddings=4_8 ,						eos_token_id=2 ,						pad_token_id=1 ,						bos_token_id=0 ,						)
								return config, input_ids, batch_size




		def   _snake_case  (     self				:Tuple)				-> List[str]:






								'''simple docstring'''






								UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    :  int				    =    self._get_config_and_data()
								UpperCamelCase_    :  Union[str, Any]				    =    FlaxMBartForSequenceClassification(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  List[str]				    =    model(input_ids=_SCREAMING_SNAKE_CASE ,						decoder_input_ids=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  List[Any]				    =    (batch_size, config.num_labels)
								self.assertEqual(outputs["""logits"""].shape ,						_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:List[Any])				-> Optional[int]:






								'''simple docstring'''






								UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    :  str				    =    self._get_config_and_data()
								UpperCamelCase_    :  int				    =    FlaxMBartForQuestionAnswering(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Tuple				    =    model(input_ids=_SCREAMING_SNAKE_CASE)

								self.assertEqual(outputs["""start_logits"""].shape ,						input_ids.shape)
								self.assertEqual(outputs["""end_logits"""].shape ,						input_ids.shape)




		def   _snake_case  (     self				:Tuple)				-> Dict:






								'''simple docstring'''






								UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    :  List[Any]				    =    self._get_config_and_data()
								UpperCamelCase_    :  Optional[Any]				    =    FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  List[Any]				    =    lm_model(input_ids=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  str				    =    (batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs["""logits"""].shape ,						_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:Dict)				-> List[str]:






								'''simple docstring'''






								UpperCamelCase_    :  Optional[int]				    =    MBartConfig(
								    vocab_size=self.vocab_size ,						d_model=1_4 ,						encoder_layers=2 ,						decoder_layers=2 ,						encoder_attention_heads=2 ,						decoder_attention_heads=2 ,						encoder_ffn_dim=8 ,						decoder_ffn_dim=8 ,						max_position_embeddings=4_8 ,						)
								UpperCamelCase_    :  List[str]				    =    FlaxMBartForConditionalGeneration(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Optional[int]				    =    np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] ,						dtype=np.intaa)
								UpperCamelCase_    :  List[Any]				    =    np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] ,						dtype=np.intaa)
								UpperCamelCase_    :  List[str]				    =    lm_model(input_ids=_SCREAMING_SNAKE_CASE ,						decoder_input_ids=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_    :  Optional[int]				    =    (*summary.shape, config.vocab_size)
								self.assertEqual(outputs["""logits"""].shape ,						_SCREAMING_SNAKE_CASE)





		def   _snake_case  (     self				:int)				-> List[str]:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] ,						dtype=np.intaa)
								UpperCamelCase_    :  Tuple				    =    shift_tokens_right(_SCREAMING_SNAKE_CASE ,						1)
								UpperCamelCase_    :  List[str]				    =    np.equal(_SCREAMING_SNAKE_CASE ,						1).astype(np.floataa).sum()
								UpperCamelCase_    :  Optional[int]				    =    np.equal(_SCREAMING_SNAKE_CASE ,						1).astype(np.floataa).sum()
								self.assertEqual(shifted.shape ,						input_ids.shape)
								self.assertEqual(_SCREAMING_SNAKE_CASE ,						n_pad_before - 1)
								self.assertTrue(np.equal(shifted[:, 0] ,						2).all())







@require_flax
class 						__snake_case (						UpperCAmelCase_				, unittest.TestCase				, UpperCAmelCase_  ):
		a_        =				True
		a_        =				(
		    (
		        FlaxMBartModel,
		        FlaxMBartForConditionalGeneration,
		        FlaxMBartForSequenceClassification,
		        FlaxMBartForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)
		a_        =				(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




		def   _snake_case  (     self				:Union[str, Any])				-> Optional[int]:






								'''simple docstring'''






								UpperCamelCase_    :  Union[str, Any]				    =    FlaxMBartModelTester(self)




		def   _snake_case  (     self				:Any)				-> Dict:






								'''simple docstring'''






								UpperCamelCase_    ,UpperCamelCase_    :  int				    =    self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:int)				-> Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_    ,UpperCamelCase_    :  Dict				    =    self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward_with_attn_mask(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:Dict)				-> List[Any]:






								'''simple docstring'''






								UpperCamelCase_    ,UpperCamelCase_    :  List[Any]				    =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__):
																				UpperCamelCase_    :  List[Any]				    =    self._prepare_for_class(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_    :  List[str]				    =    model_class(_SCREAMING_SNAKE_CASE)

																				@jax.jit
																				def encode_jitted(_SCREAMING_SNAKE_CASE				:List[str] ,						_SCREAMING_SNAKE_CASE				:Optional[int]=None ,						**_SCREAMING_SNAKE_CASE				:Optional[Any]):
																										return model.encode(input_ids=_SCREAMING_SNAKE_CASE ,						attention_mask=_SCREAMING_SNAKE_CASE)

																				with self.subTest("""JIT Enabled"""):
																										UpperCamelCase_    :  Optional[int]				    =    encode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																				with self.subTest("""JIT Disabled"""):
																										with jax.disable_jit():
																																UpperCamelCase_    :  List[Any]				    =    encode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																				self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,						len(_SCREAMING_SNAKE_CASE))
																				for jitted_output, output in zip(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE):
																										self.assertEqual(jitted_output.shape ,						output.shape)




		def   _snake_case  (     self				:Any)				-> int:






								'''simple docstring'''






								UpperCamelCase_    ,UpperCamelCase_    :  Union[str, Any]				    =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__):
																				UpperCamelCase_    :  List[Any]				    =    model_class(_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_    :  Optional[int]				    =    model.encode(inputs_dict["""input_ids"""] ,						inputs_dict["""attention_mask"""])

																				UpperCamelCase_    :  int				    =    {
																				    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																				    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																				    """encoder_outputs""": encoder_outputs,
																				}

																				@jax.jit
																				def decode_jitted(_SCREAMING_SNAKE_CASE				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:Dict):
																										return model.decode(
																										    decoder_input_ids=_SCREAMING_SNAKE_CASE ,						decoder_attention_mask=_SCREAMING_SNAKE_CASE ,						encoder_outputs=_SCREAMING_SNAKE_CASE ,						)

																				with self.subTest("""JIT Enabled"""):
																										UpperCamelCase_    :  List[str]				    =    decode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																				with self.subTest("""JIT Disabled"""):
																										with jax.disable_jit():
																																UpperCamelCase_    :  Any				    =    decode_jitted(**_SCREAMING_SNAKE_CASE).to_tuple()

																				self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,						len(_SCREAMING_SNAKE_CASE))
																				for jitted_output, output in zip(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE):
																										self.assertEqual(jitted_output.shape ,						output.shape)





		@slow
		def   _snake_case  (     self				:str)				-> str:






								'''simple docstring'''






								for model_class_name in self.all_model_classes:
														UpperCamelCase_    :  Dict				    =    model_class_name.from_pretrained("""facebook/mbart-large-cc25""" ,						from_pt=_SCREAMING_SNAKE_CASE)
														# FlaxMBartForSequenceClassification expects eos token in input_ids
														UpperCamelCase_    :  str				    =    np.ones((1, 1)) * model.config.eos_token_id
														UpperCamelCase_    :  Tuple				    =    model(_SCREAMING_SNAKE_CASE)
														self.assertIsNotNone(_SCREAMING_SNAKE_CASE)







@require_flax
@require_sentencepiece
@require_tokenizers
class 						__snake_case (						unittest.TestCase  ):
		a_        =				[
		    " UN Chief Says There Is No Military Solution in Syria",
		]
		a_        =				[
		    "Şeful ONU declară că nu există o soluţie militară în Siria",
		]
		a_        =				"facebook/mbart-large-en-ro"




		@cached_property
		def   _snake_case  (     self				:Any)				-> int:






								'''simple docstring'''






								return AutoTokenizer.from_pretrained(self.model_name)




		@cached_property
		def   _snake_case  (     self				:Optional[Any])				-> Dict:






								'''simple docstring'''






								UpperCamelCase_    :  Dict				    =    FlaxMBartForConditionalGeneration.from_pretrained(self.model_name ,						from_pt=_SCREAMING_SNAKE_CASE)
								return model




		def   _snake_case  (     self				:int ,						**_SCREAMING_SNAKE_CASE				:List[Any])				-> str:






								'''simple docstring'''






								UpperCamelCase_    :  Dict				    =    self.translate_src_text(**_SCREAMING_SNAKE_CASE)
								self.assertListEqual(self.expected_text ,						_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:List[str] ,						**_SCREAMING_SNAKE_CASE				:Any)				-> Any:






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    self.tokenizer(self.src_text ,						**_SCREAMING_SNAKE_CASE ,						return_tensors="""np""")
								UpperCamelCase_    :  Union[str, Any]				    =    self.model.generate(
								    model_inputs.input_ids ,						attention_mask=model_inputs.attention_mask ,						decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""] ,						early_stopping=_SCREAMING_SNAKE_CASE ,						num_beams=2 ,						).sequences
								UpperCamelCase_    :  str				    =    self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE ,						skip_special_tokens=_SCREAMING_SNAKE_CASE)
								return generated_words





		@slow
		def   _snake_case  (     self				:Any)				-> List[str]:






								'''simple docstring'''






								self._assert_generated_batch_equal_expected()






'''simple docstring'''


import argparse

import torch

from diffusers import UNetaDConditionModel
def 				__magic_name__(					UpperCAmelCase__   :					Optional[int]       ,							UpperCAmelCase__   :					Union[str, Any]       ,							UpperCAmelCase__   :					Optional[int]       ,							UpperCAmelCase__   :					Optional[int]=None       ,							UpperCAmelCase__   :					Any=None       ,							UpperCAmelCase__   :					Optional[Any]=None  ):
						assert isinstance(UpperCAmelCase__       ,							UpperCAmelCase__  ), "Paths should be a list of dicts containing 'old' and 'new' keys."

						# Splits the attention layers into three variables.
						if attention_paths_to_split is not None:
												for path, path_map in attention_paths_to_split.items():
																		UpperCamelCase_    :  Union[str, Any]				    =    old_checkpoint[path]
																		UpperCamelCase_    :  Any				    =    old_tensor.shape[0] // 3

																		UpperCamelCase_    :  Union[str, Any]				    =    (-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

																		UpperCamelCase_    :  List[Any]				    =    old_tensor.shape[0] // config["""num_head_channels"""] // 3

																		UpperCamelCase_    :  int				    =    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]  )
																		UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    :  str				    =    old_tensor.split(channels // num_heads       ,							dim=1  )

																		UpperCamelCase_    :  Tuple				    =    query.reshape(UpperCAmelCase__  )
																		UpperCamelCase_    :  List[str]				    =    key.reshape(UpperCAmelCase__  )
																		UpperCamelCase_    :  List[str]				    =    value.reshape(UpperCAmelCase__  )

						for path in paths:
												UpperCamelCase_    :  Union[str, Any]				    =    path["""new"""]

												# These have already been assigned
												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								UpperCamelCase_    :  Union[str, Any]				    =    new_path.replace(replacement["""old"""]       ,							replacement["""new"""]  )

        # proj_attn.weight has to be converted from conv 1D to linear
												UpperCamelCase_    :  List[str]				    =    old_checkpoint[path["""old"""]]
												UpperCamelCase_    :  List[str]				    =    ["""proj_attn.weight"""]
												UpperCamelCase_    :  int				    =    ["""proj_out.weight""", """proj_in.weight"""]
												if any(k in new_path for k in names  ):
																		UpperCamelCase_    :  Dict				    =    weight[:, :, 0]
												elif any(k in new_path for k in names_a  ) and len(weight.shape  ) > 2 and ".attentions." not in new_path:
																		UpperCamelCase_    :  Any				    =    weight[:, :, 0]
												else:
																		UpperCamelCase_    :  int				    =    weight
def 				__magic_name__(					UpperCAmelCase__   :					Tuple       ,							UpperCAmelCase__   :					Any=0  ):
						UpperCamelCase_    :  Any				    =    []
						for old_item in old_list:
												UpperCamelCase_    :  Tuple				    =    old_item

												#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
												#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

												#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
												#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

												#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

												mapping.append({"""old""": old_item, """new""": new_item}  )

						return mapping
def 				__magic_name__(					UpperCAmelCase__   :					Any       ,							UpperCAmelCase__   :					int=1  ):
						if n_shave_prefix_segments >= 0:
												return ".".join(path.split("""."""  )[n_shave_prefix_segments:]  )
						else:
												return ".".join(path.split("""."""  )[:n_shave_prefix_segments]  )
def 				__magic_name__(					UpperCAmelCase__   :					str       ,							UpperCAmelCase__   :					Dict=0  ):
						UpperCamelCase_    :  str				    =    []
						for old_item in old_list:
												mapping.append({"""old""": old_item, """new""": old_item}  )

						return mapping
def 				__magic_name__(					UpperCAmelCase__   :					List[Any]       ,							UpperCAmelCase__   :					Any=0  ):
						UpperCamelCase_    :  List[str]				    =    []
						for old_item in old_list:
												UpperCamelCase_    :  Tuple				    =    old_item.replace("""in_layers.0"""       ,							"""norm1"""  )
												UpperCamelCase_    :  List[str]				    =    new_item.replace("""in_layers.2"""       ,							"""conv1"""  )

												UpperCamelCase_    :  Any				    =    new_item.replace("""out_layers.0"""       ,							"""norm2"""  )
												UpperCamelCase_    :  Optional[Any]				    =    new_item.replace("""out_layers.3"""       ,							"""conv2"""  )

												UpperCamelCase_    :  int				    =    new_item.replace("""emb_layers.1"""       ,							"""time_emb_proj"""  )
												UpperCamelCase_    :  int				    =    new_item.replace("""skip_connection"""       ,							"""conv_shortcut"""  )

												UpperCamelCase_    :  Union[str, Any]				    =    shave_segments(UpperCAmelCase__       ,							n_shave_prefix_segments=UpperCAmelCase__  )

												if "temopral_conv" not in old_item:
																		mapping.append({"""old""": old_item, """new""": new_item}  )

						return mapping
def 				__magic_name__(					UpperCAmelCase__   :					List[str]       ,							UpperCAmelCase__   :					Union[str, Any]       ,							UpperCAmelCase__   :					Union[str, Any]=None       ,							UpperCAmelCase__   :					int=False  ):
						UpperCamelCase_    :  List[str]				    =    {}
						UpperCamelCase_    :  Dict				    =    list(checkpoint.keys()  )

						UpperCamelCase_    :  int				    =    """model.diffusion_model."""

						# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
						if sum(k.startswith("""model_ema"""  ) for k in keys  ) > 100 and extract_ema:
												print(f"""Checkpoint {path} has both EMA and non-EMA weights."""  )
												print(
												    """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
												    """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""  )
												for key in keys:
																		if key.startswith("""model.diffusion_model"""  ):
																								UpperCamelCase_    :  Optional[int]				    =    """model_ema.""" + """""".join(key.split("""."""  )[1:]  )
																								UpperCamelCase_    :  Dict				    =    checkpoint.pop(UpperCAmelCase__  )
						else:
												if sum(k.startswith("""model_ema"""  ) for k in keys  ) > 100:
																		print(
																		    """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
																		    """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""  )

												for key in keys:
																		UpperCamelCase_    :  Optional[Any]				    =    checkpoint.pop(UpperCAmelCase__  )

						UpperCamelCase_    :  str				    =    {}

						UpperCamelCase_    :  Union[str, Any]				    =    unet_state_dict["""time_embed.0.weight"""]
						UpperCamelCase_    :  Optional[int]				    =    unet_state_dict["""time_embed.0.bias"""]
						UpperCamelCase_    :  List[Any]				    =    unet_state_dict["""time_embed.2.weight"""]
						UpperCamelCase_    :  Dict				    =    unet_state_dict["""time_embed.2.bias"""]

						if config["class_embed_type"] is None:
												# No parameters to port
												...
						elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
												UpperCamelCase_    :  Optional[Any]				    =    unet_state_dict["""label_emb.0.0.weight"""]
												UpperCamelCase_    :  Any				    =    unet_state_dict["""label_emb.0.0.bias"""]
												UpperCamelCase_    :  Union[str, Any]				    =    unet_state_dict["""label_emb.0.2.weight"""]
												UpperCamelCase_    :  Any				    =    unet_state_dict["""label_emb.0.2.bias"""]
						else:
												raise NotImplementedError(f"""Not implemented `class_embed_type`: {config['class_embed_type']}"""  )

						UpperCamelCase_    :  Optional[Any]				    =    unet_state_dict["""input_blocks.0.0.weight"""]
						UpperCamelCase_    :  Dict				    =    unet_state_dict["""input_blocks.0.0.bias"""]

						UpperCamelCase_    :  List[Any]				    =    [v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""  )]
						UpperCamelCase_    :  Any				    =    renew_attention_paths(UpperCAmelCase__  )
						UpperCamelCase_    :  int				    =    {"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
						assign_to_checkpoint(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

						UpperCamelCase_    :  List[Any]				    =    unet_state_dict["""out.0.weight"""]
						UpperCamelCase_    :  Tuple				    =    unet_state_dict["""out.0.bias"""]
						UpperCamelCase_    :  Any				    =    unet_state_dict["""out.2.weight"""]
						UpperCamelCase_    :  str				    =    unet_state_dict["""out.2.bias"""]

						# Retrieves the keys for the input blocks only
						UpperCamelCase_    :  str				    =    len({""".""".join(layer.split("""."""  )[:2]  ) for layer in unet_state_dict if """input_blocks""" in layer}  )
						UpperCamelCase_    :  str				    =    {
						    layer_id: [key for key in unet_state_dict if f"""input_blocks.{layer_id}""" in key]
						    for layer_id in range(UpperCAmelCase__  )
						}

						# Retrieves the keys for the middle blocks only
						UpperCamelCase_    :  Optional[int]				    =    len({""".""".join(layer.split("""."""  )[:2]  ) for layer in unet_state_dict if """middle_block""" in layer}  )
						UpperCamelCase_    :  Optional[Any]				    =    {
						    layer_id: [key for key in unet_state_dict if f"""middle_block.{layer_id}""" in key]
						    for layer_id in range(UpperCAmelCase__  )
						}

						# Retrieves the keys for the output blocks only
						UpperCamelCase_    :  Dict				    =    len({""".""".join(layer.split("""."""  )[:2]  ) for layer in unet_state_dict if """output_blocks""" in layer}  )
						UpperCamelCase_    :  List[Any]				    =    {
						    layer_id: [key for key in unet_state_dict if f"""output_blocks.{layer_id}""" in key]
						    for layer_id in range(UpperCAmelCase__  )
						}

						for i in range(1       ,							UpperCAmelCase__  ):
												UpperCamelCase_    :  Tuple				    =    (i - 1) // (config["""layers_per_block"""] + 1)
												UpperCamelCase_    :  List[Any]				    =    (i - 1) % (config["""layers_per_block"""] + 1)

												UpperCamelCase_    :  List[Any]				    =    [
												    key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key and f"""input_blocks.{i}.0.op""" not in key
												]
												UpperCamelCase_    :  int				    =    [key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]
												UpperCamelCase_    :  int				    =    [key for key in input_blocks[i] if f"""input_blocks.{i}.2""" in key]

												if f"""input_blocks.{i}.op.weight""" in unet_state_dict:
																		UpperCamelCase_    :  int				    =    unet_state_dict.pop(
																		    f"""input_blocks.{i}.op.weight"""  )
																		UpperCamelCase_    :  Union[str, Any]				    =    unet_state_dict.pop(
																		    f"""input_blocks.{i}.op.bias"""  )

												UpperCamelCase_    :  Optional[int]				    =    renew_resnet_paths(UpperCAmelCase__  )
												UpperCamelCase_    :  Any				    =    {"""old""": f"""input_blocks.{i}.0""", """new""": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												assign_to_checkpoint(
												    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

												UpperCamelCase_    :  Tuple				    =    [key for key in resnets if """temopral_conv""" in key]
												UpperCamelCase_    :  List[Any]				    =    renew_temp_conv_paths(UpperCAmelCase__  )
												UpperCamelCase_    :  Dict				    =    {
												    """old""": f"""input_blocks.{i}.0.temopral_conv""",
												    """new""": f"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
												}
												assign_to_checkpoint(
												    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

												if len(UpperCAmelCase__  ):
																		UpperCamelCase_    :  Any				    =    renew_attention_paths(UpperCAmelCase__  )
																		UpperCamelCase_    :  Optional[int]				    =    {"""old""": f"""input_blocks.{i}.1""", """new""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
																		assign_to_checkpoint(
																		    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

												if len(UpperCAmelCase__  ):
																		UpperCamelCase_    :  List[str]				    =    renew_attention_paths(UpperCAmelCase__  )
																		UpperCamelCase_    :  Optional[Any]				    =    {
																		    """old""": f"""input_blocks.{i}.2""",
																		    """new""": f"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																		}
																		assign_to_checkpoint(
																		    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

						UpperCamelCase_    :  str				    =    middle_blocks[0]
						UpperCamelCase_    :  Optional[Any]				    =    [key for key in resnet_a if """temopral_conv""" in key]
						UpperCamelCase_    :  int				    =    middle_blocks[1]
						UpperCamelCase_    :  Union[str, Any]				    =    middle_blocks[2]
						UpperCamelCase_    :  int				    =    middle_blocks[3]
						UpperCamelCase_    :  List[Any]				    =    [key for key in resnet_a if """temopral_conv""" in key]

						UpperCamelCase_    :  Any				    =    renew_resnet_paths(UpperCAmelCase__  )
						UpperCamelCase_    :  Union[str, Any]				    =    {"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
						assign_to_checkpoint(
						    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							config=UpperCAmelCase__       ,							additional_replacements=[meta_path]  )

						UpperCamelCase_    :  int				    =    renew_temp_conv_paths(UpperCAmelCase__  )
						UpperCamelCase_    :  int				    =    {"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
						assign_to_checkpoint(
						    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							config=UpperCAmelCase__       ,							additional_replacements=[meta_path]  )

						UpperCamelCase_    :  Optional[int]				    =    renew_resnet_paths(UpperCAmelCase__  )
						UpperCamelCase_    :  Union[str, Any]				    =    {"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
						assign_to_checkpoint(
						    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							config=UpperCAmelCase__       ,							additional_replacements=[meta_path]  )

						UpperCamelCase_    :  List[str]				    =    renew_temp_conv_paths(UpperCAmelCase__  )
						UpperCamelCase_    :  Optional[Any]				    =    {"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
						assign_to_checkpoint(
						    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							config=UpperCAmelCase__       ,							additional_replacements=[meta_path]  )

						UpperCamelCase_    :  Optional[int]				    =    renew_attention_paths(UpperCAmelCase__  )
						UpperCamelCase_    :  List[str]				    =    {"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
						assign_to_checkpoint(
						    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

						UpperCamelCase_    :  Optional[int]				    =    renew_attention_paths(UpperCAmelCase__  )
						UpperCamelCase_    :  Tuple				    =    {"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
						assign_to_checkpoint(
						    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

						for i in range(UpperCAmelCase__  ):
												UpperCamelCase_    :  Optional[Any]				    =    i // (config["""layers_per_block"""] + 1)
												UpperCamelCase_    :  List[str]				    =    i % (config["""layers_per_block"""] + 1)
												UpperCamelCase_    :  List[str]				    =    [shave_segments(UpperCAmelCase__       ,							2  ) for name in output_blocks[i]]
												UpperCamelCase_    :  List[Any]				    =    {}

												for layer in output_block_layers:
																		UpperCamelCase_    ,UpperCamelCase_    :  List[Any]				    =    layer.split("""."""  )[0], shave_segments(UpperCAmelCase__       ,							1  )
																		if layer_id in output_block_list:
																								output_block_list[layer_id].append(UpperCAmelCase__  )
																		else:
																								UpperCamelCase_    :  Optional[Any]				    =    [layer_name]

												if len(UpperCAmelCase__  ) > 1:
																		UpperCamelCase_    :  Union[str, Any]				    =    [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
																		UpperCamelCase_    :  Optional[int]				    =    [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]
																		UpperCamelCase_    :  Dict				    =    [key for key in output_blocks[i] if f"""output_blocks.{i}.2""" in key]

																		UpperCamelCase_    :  str				    =    renew_resnet_paths(UpperCAmelCase__  )
																		UpperCamelCase_    :  Dict				    =    renew_resnet_paths(UpperCAmelCase__  )

																		UpperCamelCase_    :  int				    =    {"""old""": f"""output_blocks.{i}.0""", """new""": f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
																		assign_to_checkpoint(
																		    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

																		UpperCamelCase_    :  Any				    =    [key for key in resnets if """temopral_conv""" in key]
																		UpperCamelCase_    :  int				    =    renew_temp_conv_paths(UpperCAmelCase__  )
																		UpperCamelCase_    :  List[str]				    =    {
																		    """old""": f"""output_blocks.{i}.0.temopral_conv""",
																		    """new""": f"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
																		}
																		assign_to_checkpoint(
																		    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

																		UpperCamelCase_    :  str				    =    {k: sorted(UpperCAmelCase__  ) for k, v in output_block_list.items()}
																		if ["conv.bias", "conv.weight"] in output_block_list.values():
																								UpperCamelCase_    :  Union[str, Any]				    =    list(output_block_list.values()  ).index(["""conv.bias""", """conv.weight"""]  )
																								UpperCamelCase_    :  Optional[int]				    =    unet_state_dict[
																								    f"""output_blocks.{i}.{index}.conv.weight"""
																								]
																								UpperCamelCase_    :  Optional[Any]				    =    unet_state_dict[
																								    f"""output_blocks.{i}.{index}.conv.bias"""
																								]

																								# Clear attentions as they have been attributed above.
																								if len(UpperCAmelCase__  ) == 2:
																														UpperCamelCase_    :  Tuple				    =    []

																		if len(UpperCAmelCase__  ):
																								UpperCamelCase_    :  int				    =    renew_attention_paths(UpperCAmelCase__  )
																								UpperCamelCase_    :  int				    =    {
																								    """old""": f"""output_blocks.{i}.1""",
																								    """new""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																								}
																								assign_to_checkpoint(
																								    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )

																		if len(UpperCAmelCase__  ):
																								UpperCamelCase_    :  Any				    =    renew_attention_paths(UpperCAmelCase__  )
																								UpperCamelCase_    :  Optional[int]				    =    {
																								    """old""": f"""output_blocks.{i}.2""",
																								    """new""": f"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
																								}
																								assign_to_checkpoint(
																								    UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							additional_replacements=[meta_path]       ,							config=UpperCAmelCase__  )
												else:
																		UpperCamelCase_    :  Optional[Any]				    =    renew_resnet_paths(UpperCAmelCase__       ,							n_shave_prefix_segments=1  )
																		for path in resnet_0_paths:
																								UpperCamelCase_    :  Union[str, Any]				    =    """.""".join(["""output_blocks""", str(UpperCAmelCase__  ), path["""old"""]]  )
																								UpperCamelCase_    :  List[str]				    =    """.""".join(["""up_blocks""", str(UpperCAmelCase__  ), """resnets""", str(UpperCAmelCase__  ), path["""new"""]]  )
																								UpperCamelCase_    :  List[Any]				    =    unet_state_dict[old_path]

																		UpperCamelCase_    :  Optional[Any]				    =    [l for l in output_block_layers if """temopral_conv""" in l]
																		for path in temopral_conv_paths:
																								UpperCamelCase_    :  Any				    =    path.split("""temopral_conv."""  )[-1]
																								UpperCamelCase_    :  Union[str, Any]				    =    """.""".join(["""output_blocks""", str(UpperCAmelCase__  ), str(UpperCAmelCase__  ), """temopral_conv""", pruned_path]  )
																								UpperCamelCase_    :  Dict				    =    """.""".join(["""up_blocks""", str(UpperCAmelCase__  ), """temp_convs""", str(UpperCAmelCase__  ), pruned_path]  )
																								UpperCamelCase_    :  int				    =    unet_state_dict[old_path]

						return new_checkpoint


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE  =			argparse.ArgumentParser()

	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
	)
	parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
	__SCREAMING_SNAKE_CASE  =			parser.parse_args()

	__SCREAMING_SNAKE_CASE  =			torch.load(args.checkpoint_path, map_location="cpu")
	__SCREAMING_SNAKE_CASE  =			UNetaDConditionModel()

	__SCREAMING_SNAKE_CASE  =			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

	__SCREAMING_SNAKE_CASE  =			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
	__SCREAMING_SNAKE_CASE  =			set(converted_ckpt.keys()) - set(unet.state_dict().keys())

	assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

	# load state_dict
	unet.load_state_dict(converted_ckpt)

	unet.save_pretrained(args.dump_path)

	# -- finish converting the unet --




