






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
							import torch




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__=14				,    lowerCamelCase__=7				,    lowerCamelCase__=True				,    lowerCamelCase__=True				,    lowerCamelCase__=False				,    lowerCamelCase__=True				,    lowerCamelCase__=99				,    lowerCamelCase__=32				,    lowerCamelCase__=4				,    lowerCamelCase__=4				,    lowerCamelCase__=[[["global", "local"], 2]]				,    lowerCamelCase__=37				,    lowerCamelCase__="gelu"				,    lowerCamelCase__=0.1				,    lowerCamelCase__=0.1				,    lowerCamelCase__=512				,    lowerCamelCase__=7				,    lowerCamelCase__=0.02				,    ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=parent
									__UpperCamelCase :   List[str]									=batch_size
									__UpperCamelCase :   Union[str, Any]									=seq_length
									__UpperCamelCase :   int									=is_training
									__UpperCamelCase :   Dict									=use_input_mask
									__UpperCamelCase :   List[str]									=use_token_type_ids
									__UpperCamelCase :   List[Any]									=use_labels
									__UpperCamelCase :   Tuple									=vocab_size
									__UpperCamelCase :   Union[str, Any]									=hidden_size
									__UpperCamelCase :   str									=num_hidden_layers
									__UpperCamelCase :   int									=num_attention_heads
									__UpperCamelCase :   List[Any]									=attention_types
									__UpperCamelCase :   str									=intermediate_size
									__UpperCamelCase :   List[str]									=hidden_act
									__UpperCamelCase :   str									=hidden_dropout_prob
									__UpperCamelCase :   List[str]									=attention_probs_dropout_prob
									__UpperCamelCase :   int									=max_position_embeddings
									__UpperCamelCase :   Dict									=window_size
									__UpperCamelCase :   Tuple									=initializer_range
									__UpperCamelCase :   int									=None
									__UpperCamelCase :   Union[str, Any]									=vocab_size - 1
									__UpperCamelCase :   str									=vocab_size - 1
									__UpperCamelCase :   Tuple									=vocab_size - 1


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size       )

									__UpperCamelCase :   Tuple									=None
									if self.use_input_mask:
														__UpperCamelCase :   Any									=random_attention_mask([self.batch_size, self.seq_length]       )

									__UpperCamelCase :   str									=GPTNeoConfig(
									    vocab_size=self.vocab_size				,    hidden_size=self.hidden_size				,    num_layers=self.num_hidden_layers				,    num_heads=self.num_attention_heads				,    max_position_embeddings=self.max_position_embeddings				,    use_cache=_A				,    bos_token_id=self.bos_token_id				,    eos_token_id=self.eos_token_id				,    pad_token_id=self.pad_token_id				,    window_size=self.window_size				,    attention_types=self.attention_types				,    )

									return (config, input_ids, input_mask)


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=self.prepare_config_and_inputs()
									__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Union[str, Any]									=config_and_inputs
									__UpperCamelCase :   Union[str, Any]									={'input_ids': input_ids, 'attention_mask': attention_mask}
									return config, inputs_dict


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Dict									=20
									__UpperCamelCase :   int									=model_class_name(_A       )

									__UpperCamelCase :   List[Any]									=model.init_cache(input_ids.shape[0]				,    _A       )
									__UpperCamelCase :   Union[str, Any]									=jnp.ones((input_ids.shape[0], max_decoder_length)				,    dtype='i4'       )

									__UpperCamelCase :   Tuple									=jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1       )[None, :]				,    (input_ids.shape[0], input_ids.shape[-1] - 1)       )
									__UpperCamelCase :   List[str]									=model(
									    input_ids[:, :-1]				,    attention_mask=_A				,    past_key_values=_A				,    position_ids=_A				,    )

									__UpperCamelCase :   Union[str, Any]									=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,    dtype='i4'       )
									__UpperCamelCase :   Tuple									=model(
									    input_ids[:, -1:]				,    attention_mask=_A				,    past_key_values=outputs_cache.past_key_values				,    position_ids=_A				,    )

									__UpperCamelCase :   int									=model(_A       )

									__UpperCamelCase :   Optional[Any]									=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				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Dict									=20
									__UpperCamelCase :   Tuple									=model_class_name(_A       )

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

									__UpperCamelCase :   List[str]									=model.init_cache(input_ids.shape[0]				,    _A       )
									__UpperCamelCase :   Dict									=jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1       )[None, :]				,    (input_ids.shape[0], input_ids.shape[-1] - 1)       )

									__UpperCamelCase :   Dict									=model(
									    input_ids[:, :-1]				,    attention_mask=_A				,    past_key_values=_A				,    position_ids=_A				,    )
									__UpperCamelCase :   Union[str, Any]									=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,    dtype='i4'       )
									__UpperCamelCase :   str									=model(
									    input_ids[:, -1:]				,    past_key_values=outputs_cache.past_key_values				,    attention_mask=_A				,    position_ids=_A				,    )

									__UpperCamelCase :   Union[str, Any]									=model(_A				,    attention_mask=_A       )

									__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 							__A	(     a ,	a ,	unittest.TestCase					):





				"""simple docstring"""







				UpperCamelCase__					: Union[str, Any]     					=(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
				UpperCamelCase__					: List[str]     					=(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   int									=FlaxGPTNeoModelTester(self       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									for model_class_name in self.all_model_classes:
														__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Union[str, Any]									=self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(_A				,    _A				,    _A				,    _A       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									for model_class_name in self.all_model_classes:
														__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Union[str, Any]									=self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    _A				,    _A				,    _A				,    _A       )


				@slow
				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Union[str, Any]									=GPTaTokenizer.from_pretrained('gpt2'				,    pad_token='<|endoftext|>'				,    padding_side='left'       )
									__UpperCamelCase :   int									=tokenizer(['Hello this is a long string', 'Hey']				,    return_tensors='np'				,    padding=_A				,    truncation=_A       )

									__UpperCamelCase :   List[Any]									=FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'       )
									__UpperCamelCase :   List[str]									=False
									__UpperCamelCase :   Union[str, Any]									=model.config.eos_token_id

									__UpperCamelCase :   Any									=jax.jit(model.generate       )

									__UpperCamelCase :   List[str]									=jit_generate(
									    inputs['input_ids']				,    attention_mask=inputs['attention_mask']				,    pad_token_id=tokenizer.pad_token_id       ).sequences

									__UpperCamelCase :   Any									=tokenizer.batch_decode(_A				,    skip_special_tokens=_A       )

									__UpperCamelCase :   str									=[
									    'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
									    'Hey, I\'m a little late to the party. I\'m going to',
									]

									self.assertListEqual(_A				,    _A       )


				@is_pt_flax_cross_test
				def  __lowercase				(					self       ):




									"""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__       ):
																			# prepare inputs
																			__UpperCamelCase :   Any									=self._prepare_for_class(_A				,    _A       )
																			__UpperCamelCase :   Dict									={k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			__UpperCamelCase :   Optional[int]									=model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			__UpperCamelCase :   List[str]									=getattr(_A				,    _A       )

																			__UpperCamelCase						,							__UpperCamelCase :   Union[str, Any]									=pt_inputs['input_ids'].shape
																			__UpperCamelCase :   Any									=np.random.randint(0				,    seq_length - 1				,    size=(batch_size,)       )
																			for batch_idx, start_index in enumerate(_A       ):
																								__UpperCamelCase :   Dict									=0
																								__UpperCamelCase :   Optional[Any]									=1
																								__UpperCamelCase :   str									=0
																								__UpperCamelCase :   Any									=1
																			__UpperCamelCase :   Optional[Any]									=pt_model_class(_A       ).eval()
																			__UpperCamelCase :   int									=model_class(_A				,    dtype=jnp.floataa       )

																			__UpperCamelCase :   Any									=convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,    _A       )
																			__UpperCamelCase :   List[str]									=fx_state

																			with torch.no_grad():
																								__UpperCamelCase :   int									=pt_model(**_A       ).to_tuple()

																			__UpperCamelCase :   Dict									=fx_model(**_A       ).to_tuple()
																			self.assertEqual(len(_A       )				,    len(_A       )				,    'Output lengths differ between Flax and PyTorch'       )
																			for fx_output, pt_output in zip(_A				,    _A       ):
																								self.assert_almost_equals(fx_output[:, -1]				,    pt_output[:, -1].numpy()				,    4E-2       )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																								pt_model.save_pretrained(_A       )
																								__UpperCamelCase :   Dict									=model_class.from_pretrained(_A				,    from_pt=_A       )

																			__UpperCamelCase :   str									=fx_model_loaded(**_A       ).to_tuple()
																			self.assertEqual(
																			    len(_A       )				,    len(_A       )				,    'Output lengths differ between Flax and PyTorch'       )
																			for fx_output_loaded, pt_output in zip(_A				,    _A       ):
																								self.assert_almost_equals(fx_output_loaded[:, -1]				,    pt_output[:, -1].numpy()				,    4E-2       )


				@is_pt_flax_cross_test
				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase						,							__UpperCamelCase :   Optional[int]									=self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__       ):
																			# prepare inputs
																			__UpperCamelCase :   Tuple									=self._prepare_for_class(_A				,    _A       )
																			__UpperCamelCase :   List[Any]									={k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			__UpperCamelCase :   Optional[int]									=model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			__UpperCamelCase :   int									=getattr(_A				,    _A       )

																			__UpperCamelCase :   List[Any]									=pt_model_class(_A       ).eval()
																			__UpperCamelCase :   Dict									=model_class(_A				,    dtype=jnp.floataa       )

																			__UpperCamelCase :   str									=load_flax_weights_in_pytorch_model(_A				,    fx_model.params       )
																			__UpperCamelCase						,							__UpperCamelCase :   int									=pt_inputs['input_ids'].shape
																			__UpperCamelCase :   Tuple									=np.random.randint(0				,    seq_length - 1				,    size=(batch_size,)       )
																			for batch_idx, start_index in enumerate(_A       ):
																								__UpperCamelCase :   Optional[int]									=0
																								__UpperCamelCase :   Optional[Any]									=1
																								__UpperCamelCase :   Dict									=0
																								__UpperCamelCase :   int									=1

																			# make sure weights are tied in PyTorch
																			pt_model.tie_weights()

																			with torch.no_grad():
																								__UpperCamelCase :   Optional[int]									=pt_model(**_A       ).to_tuple()

																			__UpperCamelCase :   Tuple									=fx_model(**_A       ).to_tuple()
																			self.assertEqual(len(_A       )				,    len(_A       )				,    'Output lengths differ between Flax and PyTorch'       )
																			for fx_output, pt_output in zip(_A				,    _A       ):
																								self.assert_almost_equals(fx_output[:, -1]				,    pt_output[:, -1].numpy()				,    4E-2       )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																								fx_model.save_pretrained(_A       )
																								__UpperCamelCase :   str									=pt_model_class.from_pretrained(_A				,    from_flax=_A       )

																			with torch.no_grad():
																								__UpperCamelCase :   Tuple									=pt_model_loaded(**_A       ).to_tuple()

																			self.assertEqual(
																			    len(_A       )				,    len(_A       )				,    'Output lengths differ between Flax and PyTorch'       )
																			for fx_output, pt_output in zip(_A				,    _A       ):
																								self.assert_almost_equals(fx_output[:, -1]				,    pt_output[:, -1].numpy()				,    4E-2       )







				@slow
				def  __lowercase				(					self       ):




									"""simple docstring"""

									for model_class_name in self.all_model_classes:
														__UpperCamelCase :   Optional[int]									=model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'       )
														__UpperCamelCase :   str									=model(np.ones((1, 1)       )       )
														self.assertIsNotNone(_A       )



'''simple docstring'''





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import (
     AudioLDMPipeline,
 )
else:
 from .pipeline_audioldm import AudioLDMPipeline






