code: 
  
  
  '''simple docstring'''
  
  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							:     str								=			'platform'
  
   import jax
   import jax.numpy as jnp
  
   from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A=None						,				A=None ) -> int:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if attention_mask is None:
                lowercase__       =				np.where(input_ids != config.pad_token_id						,				1						,				0 )
         return {
             "input_ids": input_ids,
             "attention_mask": attention_mask,
         }
  @require_flax
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __init__(self					:					List[Any]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					int=13     ,		UpperCamelCase					:					List[str]=7     ,		UpperCamelCase					:					List[str]=True     ,		UpperCamelCase					:					Dict=False     ,		UpperCamelCase					:					str=99     ,		UpperCamelCase					:					List[Any]=16     ,		UpperCamelCase					:					Optional[int]=2     ,		UpperCamelCase					:					Any=4     ,		UpperCamelCase					:					Optional[int]=4     ,		UpperCamelCase					:					List[str]="gelu"     ,		UpperCamelCase					:					str=0.1     ,		UpperCamelCase					:					int=0.1     ,		UpperCamelCase					:					int=20     ,		UpperCamelCase					:					Any=2     ,		UpperCamelCase					:					Union[str, Any]=1     ,		UpperCamelCase					:					Optional[Any]=0     ,		UpperCamelCase					:					Any=16     ,		UpperCamelCase					:					Any=16     ,		UpperCamelCase					:					Any=0.02     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				parent
               lowercase__       =				batch_size
               lowercase__       =				seq_length
               lowercase__       =				is_training
               lowercase__       =				use_labels
               lowercase__       =				vocab_size
               lowercase__       =				hidden_size
               lowercase__       =				num_hidden_layers
               lowercase__       =				num_attention_heads
               lowercase__       =				intermediate_size
               lowercase__       =				hidden_act
               lowercase__       =				hidden_dropout_prob
               lowercase__       =				attention_probs_dropout_prob
               lowercase__       =				max_position_embeddings
               lowercase__       =				eos_token_id
               lowercase__       =				pad_token_id
               lowercase__       =				bos_token_id
               lowercase__       =				embed_dim
               lowercase__       =				word_embed_proj_dim
               lowercase__       =				initializer_range
               lowercase__       =				False
  
  
  
        def    UpperCamelCase__	(self					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,		self.vocab_size				)     ,		3     ,		self.vocab_size				)
               lowercase__       =				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,		dtype=np.intaa				))     ,		-1				)
  
               lowercase__       =				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     ,		)
               lowercase__       =				prepare_opt_inputs_dict(UpperCamelCase     ,		UpperCamelCase				)
               return config, inputs_dict
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__    ,lowercase__       =				self.prepare_config_and_inputs()
               return config, inputs_dict
  
  
  
        def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					Optional[Any]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				20
               lowercase__       =				model_class_name(UpperCamelCase				)
  
               lowercase__       =				inputs_dict['''input_ids''']
               lowercase__       =				inputs_dict['''attention_mask''']
  
               lowercase__       =				model.init_cache(input_ids.shape[0]     ,		UpperCamelCase				)
               lowercase__       =				jnp.ones((input_ids.shape[0], max_length)     ,		dtype='''i4'''				)
  
               lowercase__       =				jnp.broadcast_to(
                   jnp.arange(input_ids.shape[-1] - 1				)[None, :]     ,		(input_ids.shape[0], input_ids.shape[-1] - 1)     ,		)
               lowercase__       =				model(
                   input_ids[:, :-1]     ,		attention_mask=UpperCamelCase     ,		past_key_values=UpperCamelCase     ,		position_ids=UpperCamelCase     ,		)
  
               lowercase__       =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,		dtype='''i4'''				)
               lowercase__       =				model(
                   input_ids[:, -1:]     ,		attention_mask=UpperCamelCase     ,		past_key_values=outputs_cache.past_key_values     ,		position_ids=UpperCamelCase     ,		)
  
               lowercase__       =				model(UpperCamelCase				)
  
               lowercase__       =				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    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					Union[str, Any]     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				20
               lowercase__       =				model_class_name(UpperCamelCase				)
  
               lowercase__    ,lowercase__       =				(
                   inputs_dict['''input_ids'''],
                   inputs_dict['''attention_mask'''],
               )
  
               lowercase__       =				jnp.concatenate(
                   [
                       attention_mask,
                       jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])				),
                   ]     ,		axis=-1     ,		)
  
               lowercase__       =				model.init_cache(input_ids.shape[0]     ,		UpperCamelCase				)
               lowercase__       =				jnp.broadcast_to(
                   jnp.arange(input_ids.shape[-1] - 1				)[None, :]     ,		(input_ids.shape[0], input_ids.shape[-1] - 1)     ,		)
  
               lowercase__       =				model(
                   input_ids[:, :-1]     ,		attention_mask=UpperCamelCase     ,		past_key_values=UpperCamelCase     ,		position_ids=UpperCamelCase     ,		)
               lowercase__       =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,		dtype='''i4'''				)
               lowercase__       =				model(
                   input_ids[:, -1:]     ,		past_key_values=outputs_cache.past_key_values     ,		attention_mask=UpperCamelCase     ,		position_ids=UpperCamelCase     ,		)
  
               lowercase__       =				model(UpperCamelCase     ,		attention_mask=UpperCamelCase				)
  
               lowercase__       =				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		(lowercase_						,  unittest.TestCase						,  lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  Optional[int]						      =							(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
        lowerCAmelCase__      :  Optional[int]						      =							() if is_flax_available() else ()
  
  
  
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				FlaxOPTModelTester(self				)
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__    ,lowercase__       =				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    UpperCamelCase__	(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__    ,lowercase__       =				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    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               for model_class_name in self.all_model_classes:
                      lowercase__       =				model_class_name.from_pretrained('''facebook/opt-125m'''				)
                      lowercase__       =				np.ones((1, 1)				) * model.config.eos_token_id
                      lowercase__       =				model(UpperCamelCase				)
                      self.assertIsNotNone(UpperCamelCase				)
  
  
  
  
  
  
  @require_sentencepiece
  @require_flax
  class 						__lowerCAmelCase		(unittest.TestCase  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        @slow
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				FlaxOPTModel.from_pretrained('''facebook/opt-350m'''				)
               lowercase__       =				jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				)
               lowercase__       =				model(input_ids=UpperCamelCase				).last_hidden_state
               lowercase__       =				(1, 11, 512)
               self.assertEqual(output.shape     ,		UpperCamelCase				)
               lowercase__       =				jnp.array(
                   [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]				)
               self.assertTrue(jnp.allclose(output[:, :3, :3]     ,		UpperCamelCase     ,		atol=4E-2				)				)
  
  
  
  
  
  
  @require_flax
  @slow
  class 						__lowerCAmelCase		(unittest.TestCase  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().setUp()
               lowercase__       =				'''facebook/opt-350m'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				FlaxOPTForCausalLM.from_pretrained(self.path_model				)
               lowercase__       =				GPTaTokenizer.from_pretrained(self.path_model				)
  
               lowercase__       =				[
                   '''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
               lowercase__       =				tokenizer(UpperCamelCase     ,		return_tensors='''jax'''     ,		padding=UpperCamelCase     ,		add_special_tokens=UpperCamelCase				)
               lowercase__       =				model(inputs.input_ids     ,		attention_mask=inputs.attention_mask				)[0].mean(axis=-1				)
               lowercase__       =				jnp.array(
                   [
                       [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                       [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                       [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                       [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
                   ]				)
               self.assertTrue(jnp.allclose(UpperCamelCase     ,		UpperCamelCase     ,		atol=4E-2				)				)
  
               lowercase__       =				jax.jit(UpperCamelCase				)
               lowercase__       =				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 						__lowerCAmelCase		(unittest.TestCase  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        @property
        def    UpperCamelCase__	(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    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				'''facebook/opt-125m'''
  
               lowercase__       =				[
                   '''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''',
               ]
  
               lowercase__       =				[]
  
               lowercase__       =				FlaxOPTForCausalLM.from_pretrained(UpperCamelCase				)
               lowercase__       =				GPTaTokenizer.from_pretrained(UpperCamelCase				)
  
               for prompt in self.prompts:
                      lowercase__       =				tokenizer(UpperCamelCase     ,		return_tensors='''jax'''				).input_ids
  
                      lowercase__       =				model.generate(UpperCamelCase     ,		max_length=10				)
                      lowercase__       =				generated_ids[0]
  
                      lowercase__       =				tokenizer.batch_decode(UpperCamelCase     ,		skip_special_tokens=UpperCamelCase				)
                      predicted_outputs += generated_string
  
               self.assertListEqual(UpperCamelCase     ,		UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				'''facebook/opt-350m'''
  
               lowercase__       =				[
                   '''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''',
               ]
  
               lowercase__       =				[]
               lowercase__       =				FlaxOPTForCausalLM.from_pretrained(UpperCamelCase				)
               lowercase__       =				GPTaTokenizer.from_pretrained(UpperCamelCase				)
  
               for prompt in self.prompts:
                      lowercase__       =				tokenizer(UpperCamelCase     ,		return_tensors='''jax'''				).input_ids
  
                      lowercase__       =				model.generate(UpperCamelCase     ,		max_length=10				)
                      lowercase__       =				generated_ids[0]
  
                      lowercase__       =				tokenizer.batch_decode(UpperCamelCase     ,		skip_special_tokens=UpperCamelCase				)
                      predicted_outputs += generated_string
  
               self.assertListEqual(UpperCamelCase     ,		UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				'''facebook/opt-125m'''
               lowercase__       =				[
                   '''Today is a beautiful day and I want to thank''',
                   '''In the city of Rome Canaver Canaver Canaver Canaver''',
               ]
               lowercase__       =				FlaxOPTForCausalLM.from_pretrained(UpperCamelCase				)
               lowercase__       =				GPTaTokenizer.from_pretrained(UpperCamelCase				)
               lowercase__       =				tokenizer(
                   [
                       '''Today is a beautiful day and I want to''',
                       '''In the city of''',
                   ]     ,		return_tensors='''jax'''     ,		padding=UpperCamelCase     ,		)
  
               lowercase__       =				jax.jit(model.generate				)
  
               lowercase__       =				jit_generate(inputs['''input_ids''']     ,		attention_mask=inputs['''attention_mask''']				).sequences
  
               lowercase__       =				tokenizer.batch_decode(UpperCamelCase     ,		skip_special_tokens=UpperCamelCase				)
  
               self.assertIsNotNone(UpperCamelCase     ,		UpperCamelCase				)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				'''facebook/opt-350m'''
  
               lowercase__       =				GPTaTokenizer.from_pretrained(UpperCamelCase				)
               lowercase__       =				FlaxOPTForCausalLM.from_pretrained(UpperCamelCase				)
  
               lowercase__       =				'''left'''
  
               # use different length sentences to test batching
               lowercase__       =				[
                   '''Hello, my dog is a little''',
                   '''Today, I''',
               ]
  
               lowercase__       =				tokenizer(UpperCamelCase     ,		return_tensors='''jax'''     ,		padding=UpperCamelCase				)
               lowercase__       =				inputs['''input_ids''']
  
               lowercase__       =				model.generate(input_ids=UpperCamelCase     ,		attention_mask=inputs['''attention_mask''']     ,		trace=UpperCamelCase				)
  
               lowercase__       =				tokenizer(sentences[0]     ,		return_tensors='''jax'''				).input_ids
               lowercase__       =				model.generate(input_ids=UpperCamelCase				)
  
               lowercase__       =				inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
               lowercase__       =				tokenizer(sentences[1]     ,		return_tensors='''jax'''				).input_ids
               lowercase__       =				model.generate(input_ids=UpperCamelCase     ,		max_length=model.config.max_length - num_paddings				)
  
               lowercase__       =				tokenizer.batch_decode(outputs[0]     ,		skip_special_tokens=UpperCamelCase				)
               lowercase__       =				tokenizer.decode(output_non_padded[0][0]     ,		skip_special_tokens=UpperCamelCase				)
               lowercase__       =				tokenizer.decode(output_padded[0][0]     ,		skip_special_tokens=UpperCamelCase				)
  
               lowercase__       =				[
                   '''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: 
  
  
  '''simple docstring'''
  
  import torch
  
  from accelerate import Accelerator, DistributedType
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __enter__(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if self.enabled:
                      lowercase__       =				self.model.no_sync()
                      self.model_sync_obj.__enter__()
  
               return self
  
  
  
        def __exit__(self					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if self.enabled:
                      # Average all models on exit
                      self._sync_and_avg_model_params()
                      self.model_sync_obj.__exit__(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)
  
  
  
        def __init__(self					:					Dict     ,		UpperCamelCase					:					Accelerator     ,		UpperCamelCase					:					torch.nn.Module     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					bool = True				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if accelerator.distributed_type not in [
                   DistributedType.NO,
                   DistributedType.MULTI_CPU,
                   DistributedType.MULTI_GPU,
               ]:
                      raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''				)
               lowercase__       =				enabled and accelerator.distributed_type != DistributedType.NO
               lowercase__       =				0
               if self.enabled:
                      lowercase__       =				accelerator
                      lowercase__       =				model
                      lowercase__       =				local_sgd_steps
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.num_steps += 1
               if not self.enabled:
                      return
  
               if self.num_steps % self.local_sgd_steps == 0:
                      self._sync_and_avg_model_params()
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.accelerator.wait_for_everyone()
               with self.accelerator.autocast():
                      for param in self.model.parameters():
                             lowercase__       =				self.accelerator.reduce(param.data     ,		reduction='''mean'''				)
  
  
  
  
  