






"""simple docstring"""





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    OpenAIGPTConfig,
							    OpenAIGPTDoubleHeadsModel,
							    OpenAIGPTForSequenceClassification,
							    OpenAIGPTLMHeadModel,
							    OpenAIGPTModel,
							)



class       __A		:

					def __init__(			self  :      Dict     ,		__snake_case  :      Dict     ,		__snake_case  :      Dict=1_3     ,		__snake_case  :      List[str]=7     ,		__snake_case  :      Optional[Any]=True     ,		__snake_case  :      int=True     ,		__snake_case  :      Dict=True     ,		__snake_case  :      List[Any]=9_9     ,		__snake_case  :      int=3_2     ,		__snake_case  :      List[Any]=5     ,		__snake_case  :      Optional[int]=4     ,		__snake_case  :      Union[str, Any]=3_7     ,		__snake_case  :      List[Any]="gelu"     ,		__snake_case  :      List[str]=0.1     ,		__snake_case  :      Union[str, Any]=0.1     ,		__snake_case  :      Optional[int]=5_1_2     ,		__snake_case  :      Tuple=1_6     ,		__snake_case  :      List[str]=2     ,		__snake_case  :      Tuple=0.02     ,		__snake_case  :      Union[str, Any]=3     ,		__snake_case  :      Tuple=4     ,		__snake_case  :      Any=None     ,		)			->							Optional[int]:
											__magic_name__:	List[str]							=       parent
											__magic_name__:	List[Any]							=       batch_size
											__magic_name__:	Optional[int]							=       seq_length
											__magic_name__:	Optional[Any]							=       is_training
											__magic_name__:	Optional[Any]							=       use_token_type_ids
											__magic_name__:	List[str]							=       use_labels
											__magic_name__:	Union[str, Any]							=       vocab_size
											__magic_name__:	List[Any]							=       hidden_size
											__magic_name__:	int							=       num_hidden_layers
											__magic_name__:	Optional[Any]							=       num_attention_heads
											__magic_name__:	List[Any]							=       intermediate_size
											__magic_name__:	Any							=       hidden_act
											__magic_name__:	Union[str, Any]							=       hidden_dropout_prob
											__magic_name__:	List[str]							=       attention_probs_dropout_prob
											__magic_name__:	Optional[Any]							=       max_position_embeddings
											__magic_name__:	Dict							=       type_vocab_size
											__magic_name__:	Union[str, Any]							=       type_sequence_label_size
											__magic_name__:	List[str]							=       initializer_range
											__magic_name__:	Any							=       num_labels
											__magic_name__:	Optional[int]							=       num_choices
											__magic_name__:	List[Any]							=       scope
											__magic_name__:	List[str]							=       self.vocab_size - 1

					def   lowerCamelCase__   (			self  :      Any					)			->							Optional[int]:
											__magic_name__:	Any							=       ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size					)

											__magic_name__:	Dict							=       None
											if self.use_token_type_ids:
																	__magic_name__:	Tuple							=       ids_tensor([self.batch_size, self.seq_length]     ,		self.type_vocab_size					)

											__magic_name__:	Dict							=       None
											__magic_name__:	Optional[Any]							=       None
											__magic_name__:	List[str]							=       None
											if self.use_labels:
																	__magic_name__:	str							=       ids_tensor([self.batch_size]     ,		self.type_sequence_label_size					)
																	__magic_name__:	List[str]							=       ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels					)
																	__magic_name__:	Dict							=       ids_tensor([self.batch_size]     ,		self.num_choices					)

											__magic_name__:	str							=       OpenAIGPTConfig(
											    vocab_size=self.vocab_size     ,		n_embd=self.hidden_size     ,		n_layer=self.num_hidden_layers     ,		n_head=self.num_attention_heads     ,		n_positions=self.max_position_embeddings     ,		pad_token_id=self.pad_token_id     ,		)

											__magic_name__:	List[Any]							=       ids_tensor([self.num_hidden_layers, self.num_attention_heads]     ,		2					)

											return (
											    config,
											    input_ids,
											    head_mask,
											    token_type_ids,
											    sequence_labels,
											    token_labels,
											    choice_labels,
											)

					def   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Dict     ,		__snake_case  :      str     ,		__snake_case  :      Dict     ,		*__snake_case  :      Optional[int]					)			->							Tuple:
											__magic_name__:	Tuple							=       OpenAIGPTModel(config=__snake_case					)
											model.to(__snake_case					)
											model.eval()

											__magic_name__:	Dict							=       model(__snake_case     ,		token_type_ids=__snake_case     ,		head_mask=__snake_case					)
											__magic_name__:	Tuple							=       model(__snake_case     ,		token_type_ids=__snake_case					)
											__magic_name__:	List[Any]							=       model(__snake_case					)

											self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)					)

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      Dict     ,		__snake_case  :      Dict     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      List[Any]     ,		*__snake_case  :      List[Any]					)			->							Tuple:
											__magic_name__:	Optional[Any]							=       OpenAIGPTLMHeadModel(__snake_case					)
											model.to(__snake_case					)
											model.eval()

											__magic_name__:	List[str]							=       model(__snake_case     ,		token_type_ids=__snake_case     ,		labels=__snake_case					)
											self.parent.assertEqual(result.loss.shape     ,		()					)
											self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size)					)

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Tuple     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Any     ,		*__snake_case  :      int					)			->							Optional[int]:
											__magic_name__:	Any							=       OpenAIGPTDoubleHeadsModel(__snake_case					)
											model.to(__snake_case					)
											model.eval()

											__magic_name__:	List[str]							=       model(__snake_case     ,		token_type_ids=__snake_case     ,		labels=__snake_case					)
											self.parent.assertEqual(result.loss.shape     ,		()					)
											self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size)					)

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Tuple     ,		__snake_case  :      Any     ,		__snake_case  :      List[str]     ,		*__snake_case  :      List[Any]					)			->							List[str]:
											__magic_name__:	int							=       self.num_labels
											__magic_name__:	int							=       OpenAIGPTForSequenceClassification(__snake_case					)
											model.to(__snake_case					)
											model.eval()

											__magic_name__:	Dict							=       ids_tensor([self.batch_size]     ,		self.type_sequence_label_size					)
											__magic_name__:	Tuple							=       model(__snake_case     ,		token_type_ids=__snake_case     ,		labels=__snake_case					)
											self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)					)






					def   lowerCamelCase__   (			self  :      str					)			->							int:
											__magic_name__:	List[Any]							=       self.prepare_config_and_inputs()
											(
											    (
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			
											):	str							=       config_and_inputs
											__magic_name__:	List[Any]							=       {
											    """input_ids""": input_ids,
											    """token_type_ids""": token_type_ids,
											    """head_mask""": head_mask,
											}

											return config, inputs_dict



@require_torch
class       __A		(   SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				(
					    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
					    if is_torch_available()
					    else ()
					)
					UpperCAmelCase__								=				(
					    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
					)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
					UpperCAmelCase__								=				(
					    {
					        "feature-extraction": OpenAIGPTModel,
					        "text-classification": OpenAIGPTForSequenceClassification,
					        "text-generation": OpenAIGPTLMHeadModel,
					        "zero-shot": OpenAIGPTForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      str     ,		__snake_case  :      int     ,		__snake_case  :      Tuple     ,		__snake_case  :      int     ,		__snake_case  :      str					)			->							Optional[Any]:
											if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																	# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																	# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
																	# tiny config could not be created.
																	return True

											return False

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Dict     ,		__snake_case  :      List[Any]     ,		__snake_case  :      Optional[int]=False					)			->							Optional[int]:
											__magic_name__:	str							=       super()._prepare_for_class(__snake_case     ,		__snake_case     ,		return_labels=__snake_case					)

											if return_labels:
																	if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																							__magic_name__:	Dict							=       torch.zeros(
																							    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)     ,		dtype=torch.long     ,		device=__snake_case     ,		)
																							__magic_name__:	int							=       inputs_dict["""labels"""]
																							__magic_name__:	Union[str, Any]							=       inputs_dict["""labels"""]
																							__magic_name__:	str							=       torch.zeros(
																							    (self.model_tester.batch_size, self.model_tester.num_choices)     ,		dtype=torch.long     ,		device=__snake_case     ,		)
																							__magic_name__:	List[str]							=       torch.zeros(
																							    self.model_tester.batch_size     ,		dtype=torch.long     ,		device=__snake_case					)
											return inputs_dict

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Union[str, Any]:
											__magic_name__:	List[str]							=       OpenAIGPTModelTester(self					)
											__magic_name__:	int							=       ConfigTester(self     ,		config_class=__snake_case     ,		n_embd=3_7					)

					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[int]:
											self.config_tester.run_common_tests()

					def   lowerCamelCase__   (			self  :      List[str]					)			->							Optional[Any]:
											__magic_name__:	Any							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_openai_gpt_model(*__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Any:
											__magic_name__:	List[str]							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_lm_head_model(*__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Optional[Any]:
											__magic_name__:	int							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_double_lm_head_model(*__snake_case					)

					def   lowerCamelCase__   (			self  :      Any					)			->							List[Any]:
											__magic_name__:	Tuple							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__snake_case					)






					@slow
					def   lowerCamelCase__   (			self  :      Tuple					)			->							Union[str, Any]:
											for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__magic_name__:	Tuple							=       OpenAIGPTModel.from_pretrained(__snake_case					)
																	self.assertIsNotNone(__snake_case					)



@require_torch
class       __A		(   unittest.TestCase			):

					@slow
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Optional[int]:
											__magic_name__:	Dict							=       OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt"""					)
											model.to(__snake_case					)
											__magic_name__:	str							=       torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]     ,		dtype=torch.long     ,		device=__snake_case					)  # the president is
											__magic_name__:	Dict							=       [
											    4_8_1,
											    4_7_3_5,
											    5_4_4,
											    2_4_6,
											    9_6_3,
											    8_7_0,
											    7_6_2,
											    2_3_9,
											    2_4_4,
											    4_0_4_7_7,
											    2_4_4,
											    2_4_9,
											    7_1_9,
											    8_8_1,
											    4_8_7,
											    5_4_4,
											    2_4_0,
											    2_4_4,
											    6_0_3,
											    4_8_1,
											]  # the president is a very good man. " \n " i\'m sure he is, " said the

											__magic_name__:	int							=       model.generate(__snake_case     ,		do_sample=__snake_case					)
											self.assertListEqual(output_ids[0].tolist()     ,		__snake_case					)










import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
    import torch

class   lowerCAmelCase				:
 def __init__( self    : List[str]     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : str=14     ,	UpperCAmelCase    : Any=7     ,	UpperCAmelCase    : Dict=True     ,	UpperCAmelCase    : List[str]=True     ,	UpperCAmelCase    : Any=False     ,	UpperCAmelCase    : Tuple=True     ,	UpperCAmelCase    : int=99     ,	UpperCAmelCase    : str=32     ,	UpperCAmelCase    : int=4     ,	UpperCAmelCase    : Optional[Any]=4     ,	UpperCAmelCase    : Any=4     ,	UpperCAmelCase    : Optional[int]=37     ,	UpperCAmelCase    : Union[str, Any]="gelu"     ,	UpperCAmelCase    : List[Any]=0.1     ,	UpperCAmelCase    : Dict=0.1     ,	UpperCAmelCase    : Optional[int]=512     ,	UpperCAmelCase    : List[Any]=0.0_2     ,	)     ->      List[Any]:
     lowerCamelCase__       : Tuple   					=						parent
     lowerCamelCase__       : Tuple   					=						batch_size
     lowerCamelCase__       : Optional[Any]   					=						seq_length
     lowerCamelCase__       : str   					=						is_training
     lowerCamelCase__       : Any   					=						use_input_mask
     lowerCamelCase__       : Optional[Any]   					=						use_token_type_ids
     lowerCamelCase__       : Optional[Any]   					=						use_labels
     lowerCamelCase__       : Optional[Any]   					=						vocab_size
     lowerCamelCase__       : Optional[int]   					=						hidden_size
     lowerCamelCase__       : Optional[Any]   					=						rotary_dim
     lowerCamelCase__       : Optional[Any]   					=						num_hidden_layers
     lowerCamelCase__       : Optional[Any]   					=						num_attention_heads
     lowerCamelCase__       : Tuple   					=						intermediate_size
     lowerCamelCase__       : Union[str, Any]   					=						hidden_act
     lowerCamelCase__       : List[Any]   					=						hidden_dropout_prob
     lowerCamelCase__       : int   					=						attention_probs_dropout_prob
     lowerCamelCase__       : Dict   					=						max_position_embeddings
     lowerCamelCase__       : Tuple   					=						initializer_range
     lowerCamelCase__       : Optional[int]   					=						None
     lowerCamelCase__       : int   					=						vocab_size - 1
     lowerCamelCase__       : str   					=						vocab_size - 1
     lowerCamelCase__       : str   					=						vocab_size - 1
 def 	A_      ( self    : str      )     ->      int:
     lowerCamelCase__       : Optional[int]   					=						ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size      )

     lowerCamelCase__       : Optional[Any]   					=						None
     if self.use_input_mask:
         lowerCamelCase__       : List[str]   					=						random_attention_mask([self.batch_size, self.seq_length]      )

     lowerCamelCase__       : Tuple   					=						GPTJConfig(
         vocab_size=self.vocab_size     ,	n_embd=self.hidden_size     ,	n_layer=self.num_hidden_layers     ,	n_head=self.num_attention_heads     ,	n_positions=self.max_position_embeddings     ,	use_cache=UpperCAmelCase     ,	bos_token_id=self.bos_token_id     ,	eos_token_id=self.eos_token_id     ,	pad_token_id=self.pad_token_id     ,	rotary_dim=self.rotary_dim     ,	)

     return (config, input_ids, input_mask)
 def 	A_      ( self    : Optional[Any]      )     ->      Union[str, Any]:
     lowerCamelCase__       : Tuple   					=						self.prepare_config_and_inputs()
     lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : List[Any]   					=						config_and_inputs
     lowerCamelCase__       : List[Any]   					=						{'input_ids': input_ids, 'attention_mask': attention_mask}
     return config, inputs_dict
 def 	A_      ( self    : int     ,	UpperCAmelCase    : Optional[Any]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : Optional[Any]      )     ->      Tuple:
     lowerCamelCase__       : Tuple   					=						20
     lowerCamelCase__       : Dict   					=						model_class_name(UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						model.init_cache(input_ids.shape[0]     ,	UpperCAmelCase      )
     lowerCamelCase__       : int   					=						jnp.ones((input_ids.shape[0], max_decoder_length)     ,	dtype='i4'      )

     lowerCamelCase__       : List[str]   					=						jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1      )[None, :]     ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      )
     lowerCamelCase__       : Optional[int]   					=						model(
         input_ids[:, :-1]     ,	attention_mask=UpperCAmelCase     ,	past_key_values=UpperCAmelCase     ,	position_ids=UpperCAmelCase     ,	)

     lowerCamelCase__       : List[str]   					=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,	dtype='i4'      )
     lowerCamelCase__       : List[str]   					=						model(
         input_ids[:, -1:]     ,	attention_mask=UpperCAmelCase     ,	past_key_values=outputs_cache.past_key_values     ,	position_ids=UpperCAmelCase     ,	)

     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase      )

     lowerCamelCase__       : 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 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : List[str]      )     ->      Optional[Any]:
     lowerCamelCase__       : Any   					=						20
     lowerCamelCase__       : Any   					=						model_class_name(UpperCAmelCase      )

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

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

     lowerCamelCase__       : List[Any]   					=						model(
         input_ids[:, :-1]     ,	attention_mask=UpperCAmelCase     ,	past_key_values=UpperCAmelCase     ,	position_ids=UpperCAmelCase     ,	)
     lowerCamelCase__       : Optional[Any]   					=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,	dtype='i4'      )
     lowerCamelCase__       : List[Any]   					=						model(
         input_ids[:, -1:]     ,	past_key_values=outputs_cache.past_key_values     ,	attention_mask=UpperCAmelCase     ,	position_ids=UpperCAmelCase     ,	)

     lowerCamelCase__       : List[Any]   					=						model(UpperCAmelCase     ,	attention_mask=UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						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   lowerCAmelCase				(   __UpperCamelCase,    __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 UpperCAmelCase__						=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()
 def 	A_      ( self    : Optional[Any]      )     ->      Tuple:
     lowerCamelCase__       : Optional[Any]   					=						FlaxGPTJModelTester(self      )
 def 	A_      ( self    : Tuple      )     ->      Union[str, Any]:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Optional[int]   					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]      )     ->      Tuple:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : str   					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase      )
 @tooslow
 def 	A_      ( self    : Dict      )     ->      Tuple:
     lowerCamelCase__       : str   					=						GPTaTokenizer.from_pretrained('gpt2'     ,	pad_token='<|endoftext|>'     ,	padding_side='left'      )
     lowerCamelCase__       : Dict   					=						tokenizer(['Hello this is a long string', 'Hey']     ,	return_tensors='np'     ,	padding=UpperCAmelCase     ,	truncation=UpperCAmelCase      )

     lowerCamelCase__       : str   					=						FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      )
     lowerCamelCase__       : Optional[int]   					=						False
     lowerCamelCase__       : Optional[int]   					=						model.config.eos_token_id

     lowerCamelCase__       : Union[str, Any]   					=						jax.jit(model.generate      )

     lowerCamelCase__       : Optional[Any]   					=						jit_generate(
         inputs['input_ids']     ,	attention_mask=inputs['attention_mask']     ,	pad_token_id=tokenizer.pad_token_id      ).sequences

     lowerCamelCase__       : Optional[int]   					=						tokenizer.batch_decode(UpperCAmelCase     ,	skip_special_tokens=UpperCAmelCase      )

     lowerCamelCase__       : int   					=						[
         '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(UpperCAmelCase     ,	UpperCAmelCase      )
 @is_pt_flax_cross_test
 def 	A_      ( self    : List[str]      )     ->      Union[str, Any]:
     lowerCamelCase__					,				lowerCamelCase__       : 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
             lowerCamelCase__       : Dict   					=						self._prepare_for_class(UpperCAmelCase     ,	UpperCAmelCase      )
             lowerCamelCase__       : Optional[int]   					=						{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCamelCase__       : Any   					=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCamelCase__       : Union[str, Any]   					=						getattr(UpperCAmelCase     ,	UpperCAmelCase      )

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

             lowerCamelCase__       : str   					=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,	UpperCAmelCase      )
             lowerCamelCase__       : Tuple   					=						fx_state

             with torch.no_grad():
                 lowerCamelCase__       : List[str]   					=						pt_model(**UpperCAmelCase      ).to_tuple()

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                 pt_model.save_pretrained(UpperCAmelCase      )
                 lowerCamelCase__       : Tuple   					=						model_class.from_pretrained(UpperCAmelCase     ,	from_pt=UpperCAmelCase      )

             lowerCamelCase__       : int   					=						fx_model_loaded(**UpperCAmelCase      ).to_tuple()
             self.assertEqual(
                 len(UpperCAmelCase      )     ,	len(UpperCAmelCase      )     ,	'Output lengths differ between Flax and PyTorch'      )
             for fx_output_loaded, pt_output in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
                 self.assert_almost_equals(fx_output_loaded[:, -1]     ,	pt_output[:, -1].numpy()     ,	4e-2      )
 @is_pt_flax_cross_test
 def 	A_      ( self    : Union[str, Any]      )     ->      int:
     lowerCamelCase__					,				lowerCamelCase__       : Dict   					=						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
             lowerCamelCase__       : Tuple   					=						self._prepare_for_class(UpperCAmelCase     ,	UpperCAmelCase      )
             lowerCamelCase__       : Tuple   					=						{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCamelCase__       : Union[str, Any]   					=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCamelCase__       : int   					=						getattr(UpperCAmelCase     ,	UpperCAmelCase      )

             lowerCamelCase__       : List[Any]   					=						pt_model_class(UpperCAmelCase      ).eval()
             lowerCamelCase__       : str   					=						model_class(UpperCAmelCase     ,	dtype=jnp.floataa      )

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

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

             with torch.no_grad():
                 lowerCamelCase__       : Any   					=						pt_model(**UpperCAmelCase      ).to_tuple()

             lowerCamelCase__       : List[Any]   					=						fx_model(**UpperCAmelCase      ).to_tuple()
             self.assertEqual(len(UpperCAmelCase      )     ,	len(UpperCAmelCase      )     ,	'Output lengths differ between Flax and PyTorch'      )
             for fx_output, pt_output in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
                 self.assert_almost_equals(fx_output[:, -1]     ,	pt_output[:, -1].numpy()     ,	4e-2      )

             with tempfile.TemporaryDirectory() as tmpdirname:
                 fx_model.save_pretrained(UpperCAmelCase      )
                 lowerCamelCase__       : Tuple   					=						pt_model_class.from_pretrained(UpperCAmelCase     ,	from_flax=UpperCAmelCase      )

             with torch.no_grad():
                 lowerCamelCase__       : Optional[int]   					=						pt_model_loaded(**UpperCAmelCase      ).to_tuple()

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







 @tooslow
 def 	A_      ( self    : List[str]      )     ->      List[Any]:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__       : Union[str, Any]   					=						model_class_name.from_pretrained('EleutherAI/gpt-j-6B'      )
         lowerCamelCase__       : Any   					=						model(np.ones((1, 1)      )      )
         self.assertIsNotNone(UpperCAmelCase      )





