code: 
  
  
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import OPTConfig, is_flax_available
  from transformers.testing_utils import require_flax, require_sentencepiece, slow
  
  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       					=		'''platform'''
  
  	import jax
  	import jax.numpy as jnp
  
  	from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
  
  
  
  def  __a  (					lowerCAmelCase_     :      List[Any]    ,lowerCAmelCase_     :      Union[str, Any]    ,lowerCAmelCase_     :      Optional[Any]=None    ,lowerCAmelCase_     :      Any=None       )   ->				Any:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						if attention_mask is None:
  												UpperCAmelCase_=     np.where(input_ids != config.pad_token_id    ,1    ,0       )
  						return {
  						    "input_ids": input_ids,
  						    "attention_mask": attention_mask,
  						}
  
  
  
  
  
  
  @require_flax
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		Any		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Optional[Any]=13		,						__UpperCAmelCase			:		List[Any]=7		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		List[Any]=99		,						__UpperCAmelCase			:		Union[str, Any]=16		,						__UpperCAmelCase			:		str=2		,						__UpperCAmelCase			:		Optional[Any]=4		,						__UpperCAmelCase			:		List[str]=4		,						__UpperCAmelCase			:		str="gelu"		,						__UpperCAmelCase			:		List[str]=0.1		,						__UpperCAmelCase			:		List[str]=0.1		,						__UpperCAmelCase			:		Dict=20		,						__UpperCAmelCase			:		Tuple=2		,						__UpperCAmelCase			:		Optional[int]=1		,						__UpperCAmelCase			:		Any=0		,						__UpperCAmelCase			:		List[Any]=16		,						__UpperCAmelCase			:		Optional[Any]=16		,						__UpperCAmelCase			:		Dict=0.02		,						)    ->				Optional[Any]:
  							UpperCAmelCase_=     parent
  							UpperCAmelCase_=     batch_size
  							UpperCAmelCase_=     seq_length
  							UpperCAmelCase_=     is_training
  							UpperCAmelCase_=     use_labels
  							UpperCAmelCase_=     vocab_size
  							UpperCAmelCase_=     hidden_size
  							UpperCAmelCase_=     num_hidden_layers
  							UpperCAmelCase_=     num_attention_heads
  							UpperCAmelCase_=     intermediate_size
  							UpperCAmelCase_=     hidden_act
  							UpperCAmelCase_=     hidden_dropout_prob
  							UpperCAmelCase_=     attention_probs_dropout_prob
  							UpperCAmelCase_=     max_position_embeddings
  							UpperCAmelCase_=     eos_token_id
  							UpperCAmelCase_=     pad_token_id
  							UpperCAmelCase_=     bos_token_id
  							UpperCAmelCase_=     embed_dim
  							UpperCAmelCase_=     word_embed_proj_dim
  							UpperCAmelCase_=     initializer_range
  							UpperCAmelCase_=     False
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Any:
  							UpperCAmelCase_=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,						self.vocab_size  )		,						3		,						self.vocab_size  )
  							UpperCAmelCase_=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,						dtype=np.intaa  ))		,						-1  )
  
  							UpperCAmelCase_=     OPTConfig(
  							    vocab_size=self.vocab_size		,						hidden_size=self.hidden_size		,						num_hidden_layers=self.num_hidden_layers		,						num_attention_heads=self.num_attention_heads		,						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		,						embed_dim=self.embed_dim		,						is_encoder_decoder=__UpperCAmelCase		,						word_embed_proj_dim=self.word_embed_proj_dim		,						initializer_range=self.initializer_range		,						use_cache=__UpperCAmelCase		,						)
  							UpperCAmelCase_=     prepare_opt_inputs_dict(__UpperCAmelCase		,						__UpperCAmelCase  )
  							return config, inputs_dict
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Optional[Any]:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.prepare_config_and_inputs()
  							return config, inputs_dict
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		List[Any]  )    ->				str:
  							UpperCAmelCase_=     20
  							UpperCAmelCase_=     model_class_name(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     inputs_dict["""input_ids"""]
  							UpperCAmelCase_=     inputs_dict["""attention_mask"""]
  
  							UpperCAmelCase_=     model.init_cache(input_ids.shape[0]		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     jnp.ones((input_ids.shape[0], max_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			:		str		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		Any  )    ->				Any:
  							UpperCAmelCase_=     20
  							UpperCAmelCase_=     model_class_name(__UpperCAmelCase  )
  
  							UpperCAmelCase_,     UpperCAmelCase_=     (
  							    inputs_dict["""input_ids"""],
  							    inputs_dict["""attention_mask"""],
  							)
  
  							UpperCAmelCase_=     jnp.concatenate(
  							    [
  							        attention_mask,
  							        jnp.zeros((attention_mask.shape[0], max_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__				,					unittest.TestCase				,					snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      List[Any]	     =							(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  	a__   :      Union[str, Any]	     =							() if is_flax_available() else ()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				int:
  							UpperCAmelCase_=     FlaxOPTModelTester(self  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Optional[Any]:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							for model_class in self.all_model_classes:
  													self.model_tester.check_use_cache_forward(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				List[str]:
  							UpperCAmelCase_,     UpperCAmelCase_=     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(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				int:
  							for model_class_name in self.all_model_classes:
  													UpperCAmelCase_=     model_class_name.from_pretrained("""facebook/opt-125m"""  )
  													UpperCAmelCase_=     np.ones((1, 1)  ) * model.config.eos_token_id
  													UpperCAmelCase_=     model(__UpperCAmelCase  )
  													self.assertIsNotNone(__UpperCAmelCase  )
  
  
  
  
  
  
  @require_sentencepiece
  @require_flax
  class 	lowercase		(				unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Tuple:
  							UpperCAmelCase_=     FlaxOPTModel.from_pretrained("""facebook/opt-350m"""  )
  							UpperCAmelCase_=     jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
  							UpperCAmelCase_=     model(input_ids=__UpperCAmelCase  ).last_hidden_state
  							UpperCAmelCase_=     (1, 11, 512)
  							self.assertEqual(output.shape		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     jnp.array(
  							    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]  )
  							self.assertTrue(jnp.allclose(output[:, :3, :3]		,						__UpperCAmelCase		,						atol=4E-2  )  )
  
  
  
  
  
  
  @require_flax
  @slow
  class 	lowercase		(				unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				int:
  							super().setUp()
  							UpperCAmelCase_=     """facebook/opt-350m"""
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				List[str]:
  							UpperCAmelCase_=     FlaxOPTForCausalLM.from_pretrained(self.path_model  )
  							UpperCAmelCase_=     GPTaTokenizer.from_pretrained(self.path_model  )
  
  							UpperCAmelCase_=     [
  							    """Today is a beautiful day and I want to""",
  							    """In the city of""",
  							    """Paris is the capital of France and""",
  							    """Computers and mobile phones have taken""",
  							]
  							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
  							UpperCAmelCase_=     tokenizer(__UpperCAmelCase		,						return_tensors="""jax"""		,						padding=__UpperCAmelCase		,						add_special_tokens=__UpperCAmelCase  )
  							UpperCAmelCase_=     model(inputs.input_ids		,						attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
  							UpperCAmelCase_=     jnp.array(
  							    [
  							        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
  							        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
  							        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
  							        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
  							    ]  )
  							self.assertTrue(jnp.allclose(__UpperCAmelCase		,						__UpperCAmelCase		,						atol=4E-2  )  )
  
  							UpperCAmelCase_=     jax.jit(__UpperCAmelCase  )
  							UpperCAmelCase_=     model(inputs.input_ids		,						attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
  							self.assertTrue(jnp.allclose(__UpperCAmelCase		,						__UpperCAmelCase		,						atol=4E-2  )  )
  
  
  
  
  
  
  @require_flax
  @slow
  class 	lowercase		(				unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				List[Any]:
  							return [
  							    "Today is a beautiful day and I want",
  							    "In the city of",
  							    "Paris is the capital of France and",
  							    "Computers and mobile phones have taken",
  							]
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Optional[Any]:
  							UpperCAmelCase_=     """facebook/opt-125m"""
  
  							UpperCAmelCase_=     [
  							    """Today is a beautiful day and I want to""",
  							    """In the city of New York, the city""",
  							    """Paris is the capital of France and the capital""",
  							    """Computers and mobile phones have taken over the""",
  							]
  
  							UpperCAmelCase_=     []
  
  							UpperCAmelCase_=     FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase  )
  							UpperCAmelCase_=     GPTaTokenizer.from_pretrained(__UpperCAmelCase  )
  
  							for prompt in self.prompts:
  													UpperCAmelCase_=     tokenizer(__UpperCAmelCase		,						return_tensors="""jax"""  ).input_ids
  
  													UpperCAmelCase_=     model.generate(__UpperCAmelCase		,						max_length=10  )
  													UpperCAmelCase_=     generated_ids[0]
  
  													UpperCAmelCase_=     tokenizer.batch_decode(__UpperCAmelCase		,						skip_special_tokens=__UpperCAmelCase  )
  													predicted_outputs += generated_string
  
  							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				List[Any]:
  							UpperCAmelCase_=     """facebook/opt-350m"""
  
  							UpperCAmelCase_=     [
  							    """Today is a beautiful day and I want to""",
  							    """In the city of San Francisco, the city""",
  							    """Paris is the capital of France and the capital""",
  							    """Computers and mobile phones have taken over the""",
  							]
  
  							UpperCAmelCase_=     []
  							UpperCAmelCase_=     FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase  )
  							UpperCAmelCase_=     GPTaTokenizer.from_pretrained(__UpperCAmelCase  )
  
  							for prompt in self.prompts:
  													UpperCAmelCase_=     tokenizer(__UpperCAmelCase		,						return_tensors="""jax"""  ).input_ids
  
  													UpperCAmelCase_=     model.generate(__UpperCAmelCase		,						max_length=10  )
  													UpperCAmelCase_=     generated_ids[0]
  
  													UpperCAmelCase_=     tokenizer.batch_decode(__UpperCAmelCase		,						skip_special_tokens=__UpperCAmelCase  )
  													predicted_outputs += generated_string
  
  							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				int:
  							UpperCAmelCase_=     """facebook/opt-125m"""
  							UpperCAmelCase_=     [
  							    """Today is a beautiful day and I want to thank""",
  							    """In the city of Rome Canaver Canaver Canaver Canaver""",
  							]
  							UpperCAmelCase_=     FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase  )
  							UpperCAmelCase_=     GPTaTokenizer.from_pretrained(__UpperCAmelCase  )
  							UpperCAmelCase_=     tokenizer(
  							    [
  							        """Today is a beautiful day and I want to""",
  							        """In the city of""",
  							    ]		,						return_tensors="""jax"""		,						padding=__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     jax.jit(model.generate  )
  
  							UpperCAmelCase_=     jit_generate(inputs["""input_ids"""]		,						attention_mask=inputs["""attention_mask"""]  ).sequences
  
  							UpperCAmelCase_=     tokenizer.batch_decode(__UpperCAmelCase		,						skip_special_tokens=__UpperCAmelCase  )
  
  							self.assertIsNotNone(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Tuple:
  							UpperCAmelCase_=     """facebook/opt-350m"""
  
  							UpperCAmelCase_=     GPTaTokenizer.from_pretrained(__UpperCAmelCase  )
  							UpperCAmelCase_=     FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     """left"""
  
  							# use different length sentences to test batching
  							UpperCAmelCase_=     [
  							    """Hello, my dog is a little""",
  							    """Today, I""",
  							]
  
  							UpperCAmelCase_=     tokenizer(__UpperCAmelCase		,						return_tensors="""jax"""		,						padding=__UpperCAmelCase  )
  							UpperCAmelCase_=     inputs["""input_ids"""]
  
  							UpperCAmelCase_=     model.generate(input_ids=__UpperCAmelCase		,						attention_mask=inputs["""attention_mask"""]		,						trace=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     tokenizer(sentences[0]		,						return_tensors="""jax"""  ).input_ids
  							UpperCAmelCase_=     model.generate(input_ids=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
  							UpperCAmelCase_=     tokenizer(sentences[1]		,						return_tensors="""jax"""  ).input_ids
  							UpperCAmelCase_=     model.generate(input_ids=__UpperCAmelCase		,						max_length=model.config.max_length - num_paddings  )
  
  							UpperCAmelCase_=     tokenizer.batch_decode(outputs[0]		,						skip_special_tokens=__UpperCAmelCase  )
  							UpperCAmelCase_=     tokenizer.decode(output_non_padded[0][0]		,						skip_special_tokens=__UpperCAmelCase  )
  							UpperCAmelCase_=     tokenizer.decode(output_padded[0][0]		,						skip_special_tokens=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     [
  							    """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
  							    """Today, I was in the middle of a conversation with a friend about the""",
  							]
  							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )
  							self.assertListEqual(__UpperCAmelCase		,						[non_padded_sentence, padded_sentence]  )
  
  
  
  
  
style_context: 
  
  
  
  from .prime_check import Test
  
  Test()
  
  
  
  
  