




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
	__A						:		Optional[int]          =						"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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			)     ->     str:







				'''simple docstring'''


				if attention_mask is None:
								__lowerCAmelCase					=  np.where(input_ids != config.pad_token_id						,			1						,			0   )
				if decoder_attention_mask is None:
								__lowerCAmelCase					=  np.where(decoder_input_ids != config.pad_token_id						,			1						,			0   )
				if head_mask is None:
								__lowerCAmelCase					=  np.ones((config.encoder_layers, config.encoder_attention_heads)   )
				if decoder_head_mask is None:
								__lowerCAmelCase					=  np.ones((config.decoder_layers, config.decoder_attention_heads)   )
				if cross_attn_head_mask is None:
								__lowerCAmelCase					=  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 		lowercase_       (	unittest.TestCase				):




					def __init__(   self:		Dict,     _lowercase:		List[Any],     _lowercase:		Any=13,     _lowercase:		int=7,     _lowercase:		str=True,     _lowercase:		List[str]=False,     _lowercase:		Any=99,     _lowercase:		str=16,     _lowercase:		Optional[int]=2,     _lowercase:		List[str]=4,     _lowercase:		List[str]=4,     _lowercase:		Optional[int]="gelu",     _lowercase:		Any=0.1,     _lowercase:		Union[str, Any]=0.1,     _lowercase:		Dict=32,     _lowercase:		int=2,     _lowercase:		Union[str, Any]=1,     _lowercase:		Any=0,     _lowercase:		int=2,     _lowercase:		List[Any]=0.02,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  seq_length
									__lowerCAmelCase					=  is_training
									__lowerCAmelCase					=  use_labels
									__lowerCAmelCase					=  vocab_size
									__lowerCAmelCase					=  hidden_size
									__lowerCAmelCase					=  num_hidden_layers
									__lowerCAmelCase					=  num_attention_heads
									__lowerCAmelCase					=  intermediate_size
									__lowerCAmelCase					=  hidden_act
									__lowerCAmelCase					=  hidden_dropout_prob
									__lowerCAmelCase					=  attention_probs_dropout_prob
									__lowerCAmelCase					=  max_position_embeddings
									__lowerCAmelCase					=  eos_token_id
									__lowerCAmelCase					=  pad_token_id
									__lowerCAmelCase					=  bos_token_id
									__lowerCAmelCase					=  decoder_start_token_id
									__lowerCAmelCase					=  initializer_range




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1],     self.vocab_size),     3,     self.vocab_size)
									__lowerCAmelCase					=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),     dtype=np.intaa)),     -1)

									__lowerCAmelCase					=  shift_tokens_right(_lowercase,     1)

									__lowerCAmelCase					=  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=_lowercase,     )
									__lowerCAmelCase					=  prepare_mbart_inputs_dict(_lowercase,     _lowercase,     _lowercase)
									return config, inputs_dict




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.prepare_config_and_inputs()
									return config, inputs_dict




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		Union[str, Any],     _lowercase:		Dict,     _lowercase:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  20
									__lowerCAmelCase					=  model_class_name(_lowercase)

									__lowerCAmelCase					=  model.encode(inputs_dict["""input_ids"""])

									__lowerCAmelCase	,				__lowerCAmelCase					=  (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									__lowerCAmelCase					=  model.init_cache(decoder_input_ids.shape[0],     _lowercase,     _lowercase)
									__lowerCAmelCase					=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length),     dtype="""i4""")

									__lowerCAmelCase					=  jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),     )
									__lowerCAmelCase					=  model.decode(
									    decoder_input_ids[:, :-1],     _lowercase,     decoder_attention_mask=_lowercase,     past_key_values=_lowercase,     decoder_position_ids=_lowercase,     )

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

									__lowerCAmelCase					=  model.decode(_lowercase,     _lowercase)

									__lowerCAmelCase					=  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     _lowercase						(   self:		List[str],     _lowercase:		int,     _lowercase:		str,     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  20
									__lowerCAmelCase					=  model_class_name(_lowercase)

									__lowerCAmelCase					=  model.encode(inputs_dict["""input_ids"""])

									__lowerCAmelCase	,				__lowerCAmelCase					=  (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									__lowerCAmelCase					=  jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
									    ],     axis=-1,     )

									__lowerCAmelCase					=  model.init_cache(decoder_input_ids.shape[0],     _lowercase,     _lowercase)
									__lowerCAmelCase					=  jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),     )

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

									__lowerCAmelCase					=  model.decode(_lowercase,     _lowercase,     decoder_attention_mask=_lowercase)

									__lowerCAmelCase					=  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 		lowercase_       (	unittest.TestCase				):
					__UpperCamelCase     	= 99




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  np.array(
									    [
									        [71, 82, 18, 33, 46, 91, 2],
									        [68, 34, 26, 58, 30, 82, 2],
									        [5, 97, 17, 39, 94, 40, 2],
									        [76, 83, 94, 25, 70, 78, 2],
									        [87, 59, 41, 35, 48, 66, 2],
									        [55, 13, 16, 58, 5, 2, 1],  # note padding
									        [64, 27, 31, 51, 12, 75, 2],
									        [52, 64, 86, 17, 83, 39, 2],
									        [48, 61, 9, 24, 71, 82, 2],
									        [26, 1, 60, 48, 22, 13, 2],
									        [21, 5, 62, 28, 14, 76, 2],
									        [45, 98, 37, 86, 59, 48, 2],
									        [70, 70, 50, 9, 28, 0, 2],
									    ],     dtype=np.intaa,     )

									__lowerCAmelCase					=  input_ids.shape[0]
									__lowerCAmelCase					=  MBartConfig(
									    vocab_size=self.vocab_size,     d_model=24,     encoder_layers=2,     decoder_layers=2,     encoder_attention_heads=2,     decoder_attention_heads=2,     encoder_ffn_dim=32,     decoder_ffn_dim=32,     max_position_embeddings=48,     eos_token_id=2,     pad_token_id=1,     bos_token_id=0,     )
									return config, input_ids, batch_size




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  self._get_config_and_data()
									__lowerCAmelCase					=  FlaxMBartForSequenceClassification(_lowercase)
									__lowerCAmelCase					=  model(input_ids=_lowercase,     decoder_input_ids=_lowercase)
									__lowerCAmelCase					=  (batch_size, config.num_labels)
									self.assertEqual(outputs["""logits"""].shape,     _lowercase)




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  self._get_config_and_data()
									__lowerCAmelCase					=  FlaxMBartForQuestionAnswering(_lowercase)
									__lowerCAmelCase					=  model(input_ids=_lowercase)

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




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  self._get_config_and_data()
									__lowerCAmelCase					=  FlaxMBartForConditionalGeneration(_lowercase)
									__lowerCAmelCase					=  lm_model(input_ids=_lowercase)
									__lowerCAmelCase					=  (batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs["""logits"""].shape,     _lowercase)




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  MBartConfig(
									    vocab_size=self.vocab_size,     d_model=14,     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=48,     )
									__lowerCAmelCase					=  FlaxMBartForConditionalGeneration(_lowercase)
									__lowerCAmelCase					=  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],     dtype=np.intaa)
									__lowerCAmelCase					=  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],     dtype=np.intaa)
									__lowerCAmelCase					=  lm_model(input_ids=_lowercase,     decoder_input_ids=_lowercase)
									__lowerCAmelCase					=  (*summary.shape, config.vocab_size)
									self.assertEqual(outputs["""logits"""].shape,     _lowercase)







					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									__lowerCAmelCase					=  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],     dtype=np.intaa)
									__lowerCAmelCase					=  shift_tokens_right(_lowercase,     1)
									__lowerCAmelCase					=  np.equal(_lowercase,     1).astype(np.floataa).sum()
									__lowerCAmelCase					=  np.equal(_lowercase,     1).astype(np.floataa).sum()
									self.assertEqual(shifted.shape,     input_ids.shape)
									self.assertEqual(_lowercase,     n_pad_before - 1)
									self.assertTrue(np.equal(shifted[:, 0],     2).all())





@require_flax
class 		lowercase_       (	lowerCAmelCase__						,							unittest.TestCase						,							lowerCAmelCase__				):
					__UpperCamelCase     	= True
					__UpperCamelCase     	= (
					    (
					        FlaxMBartModel,
					        FlaxMBartForConditionalGeneration,
					        FlaxMBartForSequenceClassification,
					        FlaxMBartForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					__UpperCamelCase     	= (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  FlaxMBartModelTester(self)




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  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(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__):
																	__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
																	__lowerCAmelCase					=  model_class(_lowercase)

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

																	with self.subTest("""JIT Enabled"""):
																					__lowerCAmelCase					=  encode_jitted(**_lowercase).to_tuple()

																	with self.subTest("""JIT Disabled"""):
																					with jax.disable_jit():
																									__lowerCAmelCase					=  encode_jitted(**_lowercase).to_tuple()

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




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__):
																	__lowerCAmelCase					=  model_class(_lowercase)
																	__lowerCAmelCase					=  model.encode(inputs_dict["""input_ids"""],     inputs_dict["""attention_mask"""])

																	__lowerCAmelCase					=  {
																	    """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(_lowercase:		Union[str, Any],     _lowercase:		str,     _lowercase:		Optional[Any]):
																					return model.decode(
																					    decoder_input_ids=_lowercase,     decoder_attention_mask=_lowercase,     encoder_outputs=_lowercase,     )

																	with self.subTest("""JIT Enabled"""):
																					__lowerCAmelCase					=  decode_jitted(**_lowercase).to_tuple()

																	with self.subTest("""JIT Disabled"""):
																					with jax.disable_jit():
																									__lowerCAmelCase					=  decode_jitted(**_lowercase).to_tuple()

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







					@slow
					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


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





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




					@cached_property
					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									return AutoTokenizer.from_pretrained(self.model_name)




					@cached_property
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name,     from_pt=_lowercase)
									return model




					def     _lowercase						(   self:		Tuple,     **_lowercase:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  self.translate_src_text(**_lowercase)
									self.assertListEqual(self.expected_text,     _lowercase)




					def     _lowercase						(   self:		Optional[Any],     **_lowercase:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.tokenizer(self.src_text,     **_lowercase,     return_tensors="""np""")
									__lowerCAmelCase					=  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=_lowercase,     num_beams=2,     ).sequences
									__lowerCAmelCase					=  self.tokenizer.batch_decode(_lowercase,     skip_special_tokens=_lowercase)
									return generated_words







					@slow
					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									self._assert_generated_batch_equal_expected()






'''simple docstring'''

import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


A_	   = collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)


def        UpperCAmelCase__   (				_lowerCAmelCase    :			str)    ->		CheckpointState:



						"""simple docstring"""

						print(f"""Reading saved model from {model_file}""")
						snake_case :       Optional[Any]				=    torch.load(_lowerCAmelCase,      map_location=lambda _lowerCAmelCase,      _lowerCAmelCase: default_restore_location(_lowerCAmelCase,      "cpu"))
						return CheckpointState(**_lowerCAmelCase)




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    src_file



			def   lowerCamelCase (							self					):
									raise NotImplementedError






			@staticmethod
			def   lowerCamelCase (							snake_case_				,			*snake_case_				,			**snake_case_					):
									if comp_type.startswith("c"					):
															return DPRContextEncoderState(*snake_case_				,			**snake_case_					)
									if comp_type.startswith("q"					):
															return DPRQuestionEncoderState(*snake_case_				,			**snake_case_					)
									if comp_type.startswith("r"					):
															return DPRReaderState(*snake_case_				,			**snake_case_					)
									else:
															raise ValueError("Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'."					)







class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"					)[0]					)					)
									print(F"""Loading DPR biencoder from {self.src_file}"""					)
									snake_case :       List[Any]				=    load_states_from_checkpoint(self.src_file					)
									snake_case				,					snake_case :       List[str]				=    model.ctx_encoder, "ctx_model."
									# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
									snake_case :       List[str]				=    {"bert_model.embeddings.position_ids": model.ctx_encoder.bert_model.embeddings.position_ids}
									for key, value in saved_state.model_dict.items():
															if key.startswith(snake_case_					):
																					snake_case :       str				=    key[len(snake_case_					) :]
																					if not key.startswith("encode_proj."					):
																											snake_case :       int				=    "bert_model." + key
																					snake_case :       Union[str, Any]				=    value
									encoder.load_state_dict(snake_case_					)
									return model







class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       Any				=    DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"					)[0]					)					)
									print(F"""Loading DPR biencoder from {self.src_file}"""					)
									snake_case :       Dict				=    load_states_from_checkpoint(self.src_file					)
									snake_case				,					snake_case :       Tuple				=    model.question_encoder, "question_model."
									# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
									snake_case :       Dict				=    {"bert_model.embeddings.position_ids": model.question_encoder.bert_model.embeddings.position_ids}
									for key, value in saved_state.model_dict.items():
															if key.startswith(snake_case_					):
																					snake_case :       Optional[int]				=    key[len(snake_case_					) :]
																					if not key.startswith("encode_proj."					):
																											snake_case :       List[str]				=    "bert_model." + key
																					snake_case :       Union[str, Any]				=    value
									encoder.load_state_dict(snake_case_					)
									return model







class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"					)[0]					)					)
									print(F"""Loading DPR reader from {self.src_file}"""					)
									snake_case :       List[str]				=    load_states_from_checkpoint(self.src_file					)
									# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
									snake_case :       List[str]				=    {
									    "encoder.bert_model.embeddings.position_ids": model.span_predictor.encoder.bert_model.embeddings.position_ids
									}
									for key, value in saved_state.model_dict.items():
															if key.startswith("encoder."					) and not key.startswith("encoder.encode_proj"					):
																					snake_case :       List[Any]				=    "encoder.bert_model." + key[len("encoder."					) :]
															snake_case :       Union[str, Any]				=    value
									model.span_predictor.load_state_dict(snake_case_					)
									return model







def        UpperCAmelCase__   (				_lowerCAmelCase    :			str,      _lowerCAmelCase    :			Path,      _lowerCAmelCase    :			Path)    ->		List[Any]:



						"""simple docstring"""

						snake_case :       Any				=    Path(_lowerCAmelCase)
						dest_dir.mkdir(exist_ok=_lowerCAmelCase)

						snake_case :       Tuple				=    DPRState.from_type(_lowerCAmelCase,      src_file=_lowerCAmelCase)
						snake_case :       Optional[int]				=    dpr_state.load_dpr_model()
						model.save_pretrained(_lowerCAmelCase)
						model.from_pretrained(_lowerCAmelCase)  # sanity check


if __name__ == "__main__":
						A_	   = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--type", type=str, help="Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
						)
						parser.add_argument(
						    "--src",
						    type=str,
						    help=(
						        "Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"
						        " https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"
						        " 'retriever' checkpoints."
						    ),
						)
						parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model directory.")
						A_	   = parser.parse_args()

						A_	   = Path(args.src)
						A_	   = f"converted-{src_file.name}" if args.dest is None else args.dest
						A_	   = Path(dest_dir)
						assert src_file.exists()
						assert (
						    args.type is not None
						), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
						convert(args.type, src_file, dest_dir)





