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
  						__lowerCamelCase       :       Optional[int]				   =			"platform"
  
  						import jax
  						import jax.numpy as jnp
  
  						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_=None,	snake_case_=None	)			->							Tuple:
  				if attention_mask is None:
  								A__     :							Optional[Any]   				=np.where(input_ids != config.pad_token_id,	1,	0	)
  				return {
  				    "input_ids": input_ids,
  				    "attention_mask": attention_mask,
  				}
  
  
  
  @require_flax
  class 			a      :
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase=13	,     __UpperCamelCase=7	,     __UpperCamelCase=True	,     __UpperCamelCase=False	,     __UpperCamelCase=99	,     __UpperCamelCase=16	,     __UpperCamelCase=2	,     __UpperCamelCase=4	,     __UpperCamelCase=4	,     __UpperCamelCase="gelu"	,     __UpperCamelCase=0.1	,     __UpperCamelCase=0.1	,     __UpperCamelCase=20	,     __UpperCamelCase=2	,     __UpperCamelCase=1	,     __UpperCamelCase=0	,     __UpperCamelCase=16	,     __UpperCamelCase=16	,     __UpperCamelCase=0.02	,     )->			Any:
  						'''simple docstring'''
  
  
  						A__     :							Union[str, Any]   				=parent
  						A__     :							Any   				=batch_size
  						A__     :							Optional[int]   				=seq_length
  						A__     :							Tuple   				=is_training
  						A__     :							List[Any]   				=use_labels
  						A__     :							List[Any]   				=vocab_size
  						A__     :							int   				=hidden_size
  						A__     :							Union[str, Any]   				=num_hidden_layers
  						A__     :							str   				=num_attention_heads
  						A__     :							Tuple   				=intermediate_size
  						A__     :							List[Any]   				=hidden_act
  						A__     :							Union[str, Any]   				=hidden_dropout_prob
  						A__     :							Dict   				=attention_probs_dropout_prob
  						A__     :							Optional[int]   				=max_position_embeddings
  						A__     :							int   				=eos_token_id
  						A__     :							Optional[int]   				=pad_token_id
  						A__     :							Tuple   				=bos_token_id
  						A__     :							List[str]   				=embed_dim
  						A__     :							Tuple   				=word_embed_proj_dim
  						A__     :							Dict   				=initializer_range
  						A__     :							Optional[int]   				=False
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						A__     :							Union[str, Any]   				=np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,     self.vocab_size							)	,     3	,     self.vocab_size							)
  						A__     :							List[Any]   				=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,     dtype=np.intaa							))	,     -1							)
  
  						A__     :							Union[str, Any]   				=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	,     )
  						A__     :							Union[str, Any]   				=prepare_opt_inputs_dict(__UpperCamelCase	,     __UpperCamelCase							)
  						return config, inputs_dict
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							Tuple   				=self.prepare_config_and_inputs()
  						return config, inputs_dict
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				=20
  						A__     :							Optional[Any]   				=model_class_name(__UpperCamelCase							)
  
  						A__     :							List[Any]   				=inputs_dict['''input_ids''']
  						A__     :							Any   				=inputs_dict['''attention_mask''']
  
  						A__     :							List[str]   				=model.init_cache(input_ids.shape[0]	,     __UpperCamelCase							)
  						A__     :							int   				=jnp.ones((input_ids.shape[0], max_length)	,     dtype='''i4'''							)
  
  						A__     :							int   				=jnp.broadcast_to(
  						    jnp.arange(input_ids.shape[-1] - 1							)[None, :]	,     (input_ids.shape[0], input_ids.shape[-1] - 1)	,     )
  						A__     :							int   				=model(
  						    input_ids[:, :-1]	,     attention_mask=__UpperCamelCase	,     past_key_values=__UpperCamelCase	,     position_ids=__UpperCamelCase	,     )
  
  						A__     :							str   				=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,     dtype='''i4'''							)
  						A__     :							Tuple   				=model(
  						    input_ids[:, -1:]	,     attention_mask=__UpperCamelCase	,     past_key_values=outputs_cache.past_key_values	,     position_ids=__UpperCamelCase	,     )
  
  						A__     :							Dict   				=model(__UpperCamelCase							)
  
  						A__     :							Union[str, 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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			Tuple:
  						'''simple docstring'''
  
  
  						A__     :							int   				=20
  						A__     :							Dict   				=model_class_name(__UpperCamelCase							)
  
  						A__				,  A__     :							Union[str, Any]   				=(
  						    inputs_dict['''input_ids'''],
  						    inputs_dict['''attention_mask'''],
  						)
  
  						A__     :							Optional[Any]   				=jnp.concatenate(
  						    [
  						        attention_mask,
  						        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])							),
  						    ]	,     axis=-1	,     )
  
  						A__     :							Tuple   				=model.init_cache(input_ids.shape[0]	,     __UpperCamelCase							)
  						A__     :							Union[str, Any]   				=jnp.broadcast_to(
  						    jnp.arange(input_ids.shape[-1] - 1							)[None, :]	,     (input_ids.shape[0], input_ids.shape[-1] - 1)	,     )
  
  						A__     :							Dict   				=model(
  						    input_ids[:, :-1]	,     attention_mask=__UpperCamelCase	,     past_key_values=__UpperCamelCase	,     position_ids=__UpperCamelCase	,     )
  						A__     :							Union[str, Any]   				=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,     dtype='''i4'''							)
  						A__     :							str   				=model(
  						    input_ids[:, -1:]	,     past_key_values=outputs_cache.past_key_values	,     attention_mask=__UpperCamelCase	,     position_ids=__UpperCamelCase	,     )
  
  						A__     :							Optional[Any]   				=model(__UpperCamelCase	,     attention_mask=__UpperCamelCase							)
  
  						A__     :							int   				=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      (      UpperCamelCase_      ,unittest.TestCase      ,UpperCamelCase_   ):
  		__lowercase													=		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  		__lowercase													=		() if is_flax_available() else ()
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						A__     :							Union[str, Any]   				=FlaxOPTModelTester(self							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							Optional[int]   				=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       lowerCAmelCase_  (  self							)->			Tuple:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							Dict   				=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       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						for model_class_name in self.all_model_classes:
  										A__     :							List[str]   				=model_class_name.from_pretrained('''facebook/opt-125m'''							)
  										A__     :							int   				=np.ones((1, 1)							) * model.config.eos_token_id
  										A__     :							Any   				=model(__UpperCamelCase							)
  										self.assertIsNotNone(__UpperCamelCase							)
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_flax
  class 			a      (      unittest.TestCase   ):
  
  
  
  
  
  
  		@slow
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=FlaxOPTModel.from_pretrained('''facebook/opt-350m'''							)
  						A__     :							Optional[int]   				=jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]							)
  						A__     :							List[str]   				=model(input_ids=__UpperCamelCase							).last_hidden_state
  						A__     :							str   				=(1, 11, 5_12)
  						self.assertEqual(output.shape	,     __UpperCamelCase							)
  						A__     :							Any   				=jnp.array(
  						    [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]							)
  						self.assertTrue(jnp.allclose(output[:, :3, :3]	,     __UpperCamelCase	,     atol=4E-2							)							)
  
  
  
  
  
  
  
  @require_flax
  @slow
  class 			a      (      unittest.TestCase   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Tuple:
  						'''simple docstring'''
  
  
  						super().setUp()
  						A__     :							Union[str, Any]   				='''facebook/opt-350m'''
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=FlaxOPTForCausalLM.from_pretrained(self.path_model							)
  						A__     :							Union[str, Any]   				=GPTaTokenizer.from_pretrained(self.path_model							)
  
  						A__     :							Union[str, Any]   				=[
  						    '''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
  						A__     :							int   				=tokenizer(__UpperCamelCase	,     return_tensors='''jax'''	,     padding=__UpperCamelCase	,     add_special_tokens=__UpperCamelCase							)
  						A__     :							Optional[Any]   				=model(inputs.input_ids	,     attention_mask=inputs.attention_mask							)[0].mean(axis=-1							)
  						A__     :							Optional[Any]   				=jnp.array(
  						    [
  						        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
  						        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
  						        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
  						        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
  						    ]							)
  						self.assertTrue(jnp.allclose(__UpperCamelCase	,     __UpperCamelCase	,     atol=4E-2							)							)
  
  						A__     :							Union[str, Any]   				=jax.jit(__UpperCamelCase							)
  						A__     :							str   				=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 			a      (      unittest.TestCase   ):
  
  
  
  
  
  
  		@property
  		def       lowerCAmelCase_  (  self							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						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       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							int   				='''facebook/opt-125m'''
  
  						A__     :							Dict   				=[
  						    '''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''',
  						]
  
  						A__     :							Union[str, Any]   				=[]
  
  						A__     :							Optional[int]   				=FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase							)
  						A__     :							Optional[int]   				=GPTaTokenizer.from_pretrained(__UpperCamelCase							)
  
  						for prompt in self.prompts:
  										A__     :							str   				=tokenizer(__UpperCamelCase	,     return_tensors='''jax'''							).input_ids
  
  										A__     :							Optional[int]   				=model.generate(__UpperCamelCase	,     max_length=10							)
  										A__     :							Optional[int]   				=generated_ids[0]
  
  										A__     :							int   				=tokenizer.batch_decode(__UpperCamelCase	,     skip_special_tokens=__UpperCamelCase							)
  										predicted_outputs += generated_string
  
  						self.assertListEqual(__UpperCamelCase	,     __UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				='''facebook/opt-350m'''
  
  						A__     :							List[str]   				=[
  						    '''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''',
  						]
  
  						A__     :							int   				=[]
  						A__     :							Union[str, Any]   				=FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase							)
  						A__     :							Tuple   				=GPTaTokenizer.from_pretrained(__UpperCamelCase							)
  
  						for prompt in self.prompts:
  										A__     :							Any   				=tokenizer(__UpperCamelCase	,     return_tensors='''jax'''							).input_ids
  
  										A__     :							Union[str, Any]   				=model.generate(__UpperCamelCase	,     max_length=10							)
  										A__     :							int   				=generated_ids[0]
  
  										A__     :							Optional[Any]   				=tokenizer.batch_decode(__UpperCamelCase	,     skip_special_tokens=__UpperCamelCase							)
  										predicted_outputs += generated_string
  
  						self.assertListEqual(__UpperCamelCase	,     __UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				='''facebook/opt-125m'''
  						A__     :							int   				=[
  						    '''Today is a beautiful day and I want to thank''',
  						    '''In the city of Rome Canaver Canaver Canaver Canaver''',
  						]
  						A__     :							Optional[Any]   				=FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase							)
  						A__     :							List[Any]   				=GPTaTokenizer.from_pretrained(__UpperCamelCase							)
  						A__     :							Union[str, Any]   				=tokenizer(
  						    [
  						        '''Today is a beautiful day and I want to''',
  						        '''In the city of''',
  						    ]	,     return_tensors='''jax'''	,     padding=__UpperCamelCase	,     )
  
  						A__     :							Dict   				=jax.jit(model.generate							)
  
  						A__     :							Optional[int]   				=jit_generate(inputs['''input_ids''']	,     attention_mask=inputs['''attention_mask''']							).sequences
  
  						A__     :							List[str]   				=tokenizer.batch_decode(__UpperCamelCase	,     skip_special_tokens=__UpperCamelCase							)
  
  						self.assertIsNotNone(__UpperCamelCase	,     __UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				='''facebook/opt-350m'''
  
  						A__     :							Union[str, Any]   				=GPTaTokenizer.from_pretrained(__UpperCamelCase							)
  						A__     :							Tuple   				=FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase							)
  
  						A__     :							Any   				='''left'''
  
  						# use different length sentences to test batching
  						A__     :							str   				=[
  						    '''Hello, my dog is a little''',
  						    '''Today, I''',
  						]
  
  						A__     :							Union[str, Any]   				=tokenizer(__UpperCamelCase	,     return_tensors='''jax'''	,     padding=__UpperCamelCase							)
  						A__     :							str   				=inputs['''input_ids''']
  
  						A__     :							int   				=model.generate(input_ids=__UpperCamelCase	,     attention_mask=inputs['''attention_mask''']	,     trace=__UpperCamelCase							)
  
  						A__     :							int   				=tokenizer(sentences[0]	,     return_tensors='''jax'''							).input_ids
  						A__     :							Union[str, Any]   				=model.generate(input_ids=__UpperCamelCase							)
  
  						A__     :							int   				=inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
  						A__     :							Optional[Any]   				=tokenizer(sentences[1]	,     return_tensors='''jax'''							).input_ids
  						A__     :							Dict   				=model.generate(input_ids=__UpperCamelCase	,     max_length=model.config.max_length - num_paddings							)
  
  						A__     :							Union[str, Any]   				=tokenizer.batch_decode(outputs[0]	,     skip_special_tokens=__UpperCamelCase							)
  						A__     :							str   				=tokenizer.decode(output_non_padded[0][0]	,     skip_special_tokens=__UpperCamelCase							)
  						A__     :							Union[str, Any]   				=tokenizer.decode(output_padded[0][0]	,     skip_special_tokens=__UpperCamelCase							)
  
  						A__     :							Any   				=[
  						    '''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: 
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				if number != int(snake_case_	):
  								raise ValueError('''factorial() only accepts integral values'''	)
  				if number < 0:
  								raise ValueError('''factorial() not defined for negative values'''	)
  				A__     :							Union[str, Any]   				=1
  				for i in range(1,	number + 1	):
  								value *= i
  				return value
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				if not isinstance(snake_case_,	snake_case_	):
  								raise ValueError('''factorial() only accepts integral values'''	)
  				if n < 0:
  								raise ValueError('''factorial() not defined for negative values'''	)
  				return 1 if n == 0 or n == 1 else n * factorial(n - 1	)
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  						__lowerCamelCase       :       Any				   =			int(input("Enter a positive integer: ").strip() or 0)
  						print(F"factorial{n} is {factorial(n)}")
  
  
  
  