code: 
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __A       					=		{
      '''configuration_decision_transformer''': [
          '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
          '''DecisionTransformerConfig''',
      ],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A       					=		[
  	    '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
  	    '''DecisionTransformerGPT2Model''',
  	    '''DecisionTransformerGPT2PreTrainedModel''',
  	    '''DecisionTransformerModel''',
  	    '''DecisionTransformerPreTrainedModel''',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_decision_transformer import (
  	    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
  	    DecisionTransformerConfig,
  	)
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_decision_transformer import (
  		    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    DecisionTransformerGPTaModel,
  		    DecisionTransformerGPTaPreTrainedModel,
  		    DecisionTransformerModel,
  		    DecisionTransformerPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__A       					=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
style_context: 
  
  
  
  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 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		int		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Optional[int]=14		,						__UpperCAmelCase			:		Tuple=7		,						__UpperCAmelCase			:		Union[str, Any]=True		,						__UpperCAmelCase			:		Any=True		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=True		,						__UpperCAmelCase			:		Optional[Any]=99		,						__UpperCAmelCase			:		str=32		,						__UpperCAmelCase			:		Any=4		,						__UpperCAmelCase			:		Optional[Any]=4		,						__UpperCAmelCase			:		Union[str, Any]=[[["global", "local"], 2]]		,						__UpperCAmelCase			:		int=37		,						__UpperCAmelCase			:		str="gelu"		,						__UpperCAmelCase			:		int=0.1		,						__UpperCAmelCase			:		Tuple=0.1		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Optional[Any]=7		,						__UpperCAmelCase			:		List[str]=0.02		,						)    ->				Union[str, Any]:
  							UpperCAmelCase_=     parent
  							UpperCAmelCase_=     batch_size
  							UpperCAmelCase_=     seq_length
  							UpperCAmelCase_=     is_training
  							UpperCAmelCase_=     use_input_mask
  							UpperCAmelCase_=     use_token_type_ids
  							UpperCAmelCase_=     use_labels
  							UpperCAmelCase_=     vocab_size
  							UpperCAmelCase_=     hidden_size
  							UpperCAmelCase_=     num_hidden_layers
  							UpperCAmelCase_=     num_attention_heads
  							UpperCAmelCase_=     attention_types
  							UpperCAmelCase_=     intermediate_size
  							UpperCAmelCase_=     hidden_act
  							UpperCAmelCase_=     hidden_dropout_prob
  							UpperCAmelCase_=     attention_probs_dropout_prob
  							UpperCAmelCase_=     max_position_embeddings
  							UpperCAmelCase_=     window_size
  							UpperCAmelCase_=     initializer_range
  							UpperCAmelCase_=     None
  							UpperCAmelCase_=     vocab_size - 1
  							UpperCAmelCase_=     vocab_size - 1
  							UpperCAmelCase_=     vocab_size - 1
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Optional[Any]:
  							UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size  )
  
  							UpperCAmelCase_=     None
  							if self.use_input_mask:
  													UpperCAmelCase_=     random_attention_mask([self.batch_size, self.seq_length]  )
  
  							UpperCAmelCase_=     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=__UpperCAmelCase		,						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 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Dict:
  							UpperCAmelCase_=     self.prepare_config_and_inputs()
  							UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     config_and_inputs
  							UpperCAmelCase_=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
  							return config, inputs_dict
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Union[str, Any]  )    ->				Dict:
  							UpperCAmelCase_=     20
  							UpperCAmelCase_=     model_class_name(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     model.init_cache(input_ids.shape[0]		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     jnp.ones((input_ids.shape[0], max_decoder_length)		,						dtype="""i4"""  )
  
  							UpperCAmelCase_=     jnp.broadcast_to(
  							    jnp.arange(input_ids.shape[-1] - 1  )[None, :]		,						(input_ids.shape[0], input_ids.shape[-1] - 1)  )
  							UpperCAmelCase_=     model(
  							    input_ids[:, :-1]		,						attention_mask=__UpperCAmelCase		,						past_key_values=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,						dtype="""i4"""  )
  							UpperCAmelCase_=     model(
  							    input_ids[:, -1:]		,						attention_mask=__UpperCAmelCase		,						past_key_values=outputs_cache.past_key_values		,						position_ids=__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     model(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		Dict  )    ->				List[Any]:
  							UpperCAmelCase_=     20
  							UpperCAmelCase_=     model_class_name(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     jnp.concatenate(
  							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]		,						axis=-1		,						)
  
  							UpperCAmelCase_=     model.init_cache(input_ids.shape[0]		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     jnp.broadcast_to(
  							    jnp.arange(input_ids.shape[-1] - 1  )[None, :]		,						(input_ids.shape[0], input_ids.shape[-1] - 1)  )
  
  							UpperCAmelCase_=     model(
  							    input_ids[:, :-1]		,						attention_mask=__UpperCAmelCase		,						past_key_values=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)
  							UpperCAmelCase_=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,						dtype="""i4"""  )
  							UpperCAmelCase_=     model(
  							    input_ids[:, -1:]		,						past_key_values=outputs_cache.past_key_values		,						attention_mask=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     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		(				snake_case__				,					snake_case__				,					unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      List[Any]	     =							(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
  	a__   :      List[Any]	     =							(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Any:
  							UpperCAmelCase_=     FlaxGPTNeoModelTester(self  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				str:
  							for model_class_name in self.all_model_classes:
  													UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  													self.model_tester.check_use_cache_forward(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Any:
  							for model_class_name in self.all_model_classes:
  													UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  													self.model_tester.check_use_cache_forward_with_attn_mask(
  													    __UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     GPTaTokenizer.from_pretrained("""gpt2"""		,						pad_token="""<|endoftext|>"""		,						padding_side="""left"""  )
  							UpperCAmelCase_=     tokenizer(["""Hello this is a long string""", """Hey"""]		,						return_tensors="""np"""		,						padding=__UpperCAmelCase		,						truncation=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""  )
  							UpperCAmelCase_=     False
  							UpperCAmelCase_=     model.config.eos_token_id
  
  							UpperCAmelCase_=     jax.jit(model.generate  )
  
  							UpperCAmelCase_=     jit_generate(
  							    inputs["""input_ids"""]		,						attention_mask=inputs["""attention_mask"""]		,						pad_token_id=tokenizer.pad_token_id  ).sequences
  
  							UpperCAmelCase_=     tokenizer.batch_decode(__UpperCAmelCase		,						skip_special_tokens=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     [
  							    """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 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Union[str, Any]:
  							UpperCAmelCase_,     UpperCAmelCase_=     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_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
  																			UpperCAmelCase_=     {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}
  
  																			# load corresponding PyTorch class
  																			UpperCAmelCase_=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
  																			UpperCAmelCase_=     getattr(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  																			UpperCAmelCase_,     UpperCAmelCase_=     pt_inputs["""input_ids"""].shape
  																			UpperCAmelCase_=     np.random.randint(0		,						seq_length - 1		,						size=(batch_size,)  )
  																			for batch_idx, start_index in enumerate(__UpperCAmelCase  ):
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  																			UpperCAmelCase_=     pt_model_class(__UpperCAmelCase  ).eval()
  																			UpperCAmelCase_=     model_class(__UpperCAmelCase		,						dtype=jnp.floataa  )
  
  																			UpperCAmelCase_=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,						__UpperCAmelCase  )
  																			UpperCAmelCase_=     fx_state
  
  																			with torch.no_grad():
  																									UpperCAmelCase_=     pt_model(**__UpperCAmelCase  ).to_tuple()
  
  																			UpperCAmelCase_=     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  )
  																									UpperCAmelCase_=     model_class.from_pretrained(__UpperCAmelCase		,						from_pt=__UpperCAmelCase  )
  
  																			UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Dict:
  							UpperCAmelCase_,     UpperCAmelCase_=     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_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
  																			UpperCAmelCase_=     {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}
  
  																			# load corresponding PyTorch class
  																			UpperCAmelCase_=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
  																			UpperCAmelCase_=     getattr(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  																			UpperCAmelCase_=     pt_model_class(__UpperCAmelCase  ).eval()
  																			UpperCAmelCase_=     model_class(__UpperCAmelCase		,						dtype=jnp.floataa  )
  
  																			UpperCAmelCase_=     load_flax_weights_in_pytorch_model(__UpperCAmelCase		,						fx_model.params  )
  																			UpperCAmelCase_,     UpperCAmelCase_=     pt_inputs["""input_ids"""].shape
  																			UpperCAmelCase_=     np.random.randint(0		,						seq_length - 1		,						size=(batch_size,)  )
  																			for batch_idx, start_index in enumerate(__UpperCAmelCase  ):
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  
  																			# make sure weights are tied in PyTorch
  																			pt_model.tie_weights()
  
  																			with torch.no_grad():
  																									UpperCAmelCase_=     pt_model(**__UpperCAmelCase  ).to_tuple()
  
  																			UpperCAmelCase_=     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  )
  																									UpperCAmelCase_=     pt_model_class.from_pretrained(__UpperCAmelCase		,						from_flax=__UpperCAmelCase  )
  
  																			with torch.no_grad():
  																									UpperCAmelCase_=     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  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Dict:
  							for model_class_name in self.all_model_classes:
  													UpperCAmelCase_=     model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""  )
  													UpperCAmelCase_=     model(np.ones((1, 1)  )  )
  													self.assertIsNotNone(__UpperCAmelCase  )
  
  
  
  
  