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_   =   "platform"
  
     import jax
     import jax.numpy as jnp
  
     from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=None  )			->						Optional[int]:
         if attention_mask is None:
                _snake_case      :							Optional[int]     				=						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     ,  lowercase_     ,  lowercase_=13     ,  lowercase_=7     ,  lowercase_=True     ,  lowercase_=False     ,  lowercase_=99     ,  lowercase_=16     ,  lowercase_=2     ,  lowercase_=4     ,  lowercase_=4     ,  lowercase_="gelu"     ,  lowercase_=0.1     ,  lowercase_=0.1     ,  lowercase_=20     ,  lowercase_=2     ,  lowercase_=1     ,  lowercase_=0     ,  lowercase_=16     ,  lowercase_=16     ,  lowercase_=0.02     ,  )      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						parent
               _snake_case      :							Optional[int]     				=						batch_size
               _snake_case      :							Any     				=						seq_length
               _snake_case      :							Any     				=						is_training
               _snake_case      :							Tuple     				=						use_labels
               _snake_case      :							Dict     				=						vocab_size
               _snake_case      :							str     				=						hidden_size
               _snake_case      :							Tuple     				=						num_hidden_layers
               _snake_case      :							Dict     				=						num_attention_heads
               _snake_case      :							str     				=						intermediate_size
               _snake_case      :							Any     				=						hidden_act
               _snake_case      :							Tuple     				=						hidden_dropout_prob
               _snake_case      :							List[str]     				=						attention_probs_dropout_prob
               _snake_case      :							Optional[Any]     				=						max_position_embeddings
               _snake_case      :							int     				=						eos_token_id
               _snake_case      :							Any     				=						pad_token_id
               _snake_case      :							List[Any]     				=						bos_token_id
               _snake_case      :							Dict     				=						embed_dim
               _snake_case      :							List[Any]     				=						word_embed_proj_dim
               _snake_case      :							str     				=						initializer_range
               _snake_case      :							Tuple     				=						False
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,  self.vocab_size					)     ,  3     ,  self.vocab_size					)
               _snake_case      :							Union[str, Any]     				=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,  dtype=np.intaa					))     ,  -1					)
  
               _snake_case      :							List[str]     				=						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=lowercase_     ,  word_embed_proj_dim=self.word_embed_proj_dim     ,  initializer_range=self.initializer_range     ,  use_cache=lowercase_     ,  )
               _snake_case      :							Optional[int]     				=						prepare_opt_inputs_dict(lowercase_     ,  lowercase_					)
               return config, inputs_dict
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case				,    _snake_case      :							str     				=						self.prepare_config_and_inputs()
               return config, inputs_dict
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							str     				=						20
               _snake_case      :							Any     				=						model_class_name(lowercase_					)
  
               _snake_case      :							Tuple     				=						inputs_dict['''input_ids''']
               _snake_case      :							List[Any]     				=						inputs_dict['''attention_mask''']
  
               _snake_case      :							str     				=						model.init_cache(input_ids.shape[0]     ,  lowercase_					)
               _snake_case      :							str     				=						jnp.ones((input_ids.shape[0], max_length)     ,  dtype='''i4'''					)
  
               _snake_case      :							int     				=						jnp.broadcast_to(
                   jnp.arange(input_ids.shape[-1] - 1					)[None, :]     ,  (input_ids.shape[0], input_ids.shape[-1] - 1)     ,  )
               _snake_case      :							List[str]     				=						model(
                   input_ids[:, :-1]     ,  attention_mask=lowercase_     ,  past_key_values=lowercase_     ,  position_ids=lowercase_     ,  )
  
               _snake_case      :							List[str]     				=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,  dtype='''i4'''					)
               _snake_case      :							Any     				=						model(
                   input_ids[:, -1:]     ,  attention_mask=lowercase_     ,  past_key_values=outputs_cache.past_key_values     ,  position_ids=lowercase_     ,  )
  
               _snake_case      :							List[Any]     				=						model(lowercase_					)
  
               _snake_case      :							List[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     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						20
               _snake_case      :							Optional[int]     				=						model_class_name(lowercase_					)
  
               _snake_case				,    _snake_case      :							Optional[Any]     				=						(
                   inputs_dict['''input_ids'''],
                   inputs_dict['''attention_mask'''],
               )
  
               _snake_case      :							List[str]     				=						jnp.concatenate(
                   [
                       attention_mask,
                       jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])					),
                   ]     ,  axis=-1     ,  )
  
               _snake_case      :							int     				=						model.init_cache(input_ids.shape[0]     ,  lowercase_					)
               _snake_case      :							str     				=						jnp.broadcast_to(
                   jnp.arange(input_ids.shape[-1] - 1					)[None, :]     ,  (input_ids.shape[0], input_ids.shape[-1] - 1)     ,  )
  
               _snake_case      :							Optional[int]     				=						model(
                   input_ids[:, :-1]     ,  attention_mask=lowercase_     ,  past_key_values=lowercase_     ,  position_ids=lowercase_     ,  )
               _snake_case      :							str     				=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,  dtype='''i4'''					)
               _snake_case      :							List[str]     				=						model(
                   input_ids[:, -1:]     ,  past_key_values=outputs_cache.past_key_values     ,  attention_mask=lowercase_     ,  position_ids=lowercase_     ,  )
  
               _snake_case      :							Optional[Any]     				=						model(lowercase_     ,  attention_mask=lowercase_					)
  
               _snake_case      :							Optional[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				):
        _SCREAMING_SNAKE_CASE					=							(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
        _SCREAMING_SNAKE_CASE					=							() if is_flax_available() else ()
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						FlaxOPTModelTester(self					)
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case				,    _snake_case      :							Optional[Any]     				=						self.model_tester.prepare_config_and_inputs()
               for model_class in self.all_model_classes:
                      self.model_tester.check_use_cache_forward(lowercase_     ,  lowercase_     ,  lowercase_					)
  
  
  
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case				,    _snake_case      :							int     				=						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(lowercase_     ,  lowercase_     ,  lowercase_					)
  
  
  
        @slow
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               for model_class_name in self.all_model_classes:
                      _snake_case      :							int     				=						model_class_name.from_pretrained('''facebook/opt-125m'''					)
                      _snake_case      :							List[str]     				=						np.ones((1, 1)					) * model.config.eos_token_id
                      _snake_case      :							Tuple     				=						model(lowercase_					)
                      self.assertIsNotNone(lowercase_					)
  
  
  @require_sentencepiece
  @require_flax
  class 				A    (unittest.TestCase				):
  
  
  
        @slow
        def    __a (					self					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						FlaxOPTModel.from_pretrained('''facebook/opt-350m'''					)
               _snake_case      :							List[Any]     				=						jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
               _snake_case      :							Tuple     				=						model(input_ids=lowercase_					).last_hidden_state
               _snake_case      :							int     				=						(1, 11, 512)
               self.assertEqual(output.shape     ,  lowercase_					)
               _snake_case      :							List[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]     ,  lowercase_     ,  atol=4E-2					)					)
  
  
  @require_flax
  @slow
  class 				A    (unittest.TestCase				):
  
  
  
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               super().setUp()
               _snake_case      :							Optional[int]     				=						'''facebook/opt-350m'''
  
  
  
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						FlaxOPTForCausalLM.from_pretrained(self.path_model					)
               _snake_case      :							List[Any]     				=						GPTaTokenizer.from_pretrained(self.path_model					)
  
               _snake_case      :							Dict     				=						[
                   '''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
               _snake_case      :							Optional[Any]     				=						tokenizer(lowercase_     ,  return_tensors='''jax'''     ,  padding=lowercase_     ,  add_special_tokens=lowercase_					)
               _snake_case      :							Optional[int]     				=						model(inputs.input_ids     ,  attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
               _snake_case      :							Optional[int]     				=						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(lowercase_     ,  lowercase_     ,  atol=4E-2					)					)
  
               _snake_case      :							Optional[int]     				=						jax.jit(lowercase_					)
               _snake_case      :							Tuple     				=						model(inputs.input_ids     ,  attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
               self.assertTrue(jnp.allclose(lowercase_     ,  lowercase_     ,  atol=4E-2					)					)
  
  
  @require_flax
  @slow
  class 				A    (unittest.TestCase				):
  
  
  
        @property
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''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    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						'''facebook/opt-125m'''
  
               _snake_case      :							List[Any]     				=						[
                   '''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''',
               ]
  
               _snake_case      :							Union[str, Any]     				=						[]
  
               _snake_case      :							Dict     				=						FlaxOPTForCausalLM.from_pretrained(lowercase_					)
               _snake_case      :							int     				=						GPTaTokenizer.from_pretrained(lowercase_					)
  
               for prompt in self.prompts:
                      _snake_case      :							int     				=						tokenizer(lowercase_     ,  return_tensors='''jax'''					).input_ids
  
                      _snake_case      :							Optional[int]     				=						model.generate(lowercase_     ,  max_length=10					)
                      _snake_case      :							Dict     				=						generated_ids[0]
  
                      _snake_case      :							str     				=						tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                      predicted_outputs += generated_string
  
               self.assertListEqual(lowercase_     ,  lowercase_					)
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							int     				=						'''facebook/opt-350m'''
  
               _snake_case      :							List[Any]     				=						[
                   '''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''',
               ]
  
               _snake_case      :							Union[str, Any]     				=						[]
               _snake_case      :							Any     				=						FlaxOPTForCausalLM.from_pretrained(lowercase_					)
               _snake_case      :							Optional[Any]     				=						GPTaTokenizer.from_pretrained(lowercase_					)
  
               for prompt in self.prompts:
                      _snake_case      :							str     				=						tokenizer(lowercase_     ,  return_tensors='''jax'''					).input_ids
  
                      _snake_case      :							int     				=						model.generate(lowercase_     ,  max_length=10					)
                      _snake_case      :							Any     				=						generated_ids[0]
  
                      _snake_case      :							Tuple     				=						tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                      predicted_outputs += generated_string
  
               self.assertListEqual(lowercase_     ,  lowercase_					)
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						'''facebook/opt-125m'''
               _snake_case      :							Any     				=						[
                   '''Today is a beautiful day and I want to thank''',
                   '''In the city of Rome Canaver Canaver Canaver Canaver''',
               ]
               _snake_case      :							Tuple     				=						FlaxOPTForCausalLM.from_pretrained(lowercase_					)
               _snake_case      :							Any     				=						GPTaTokenizer.from_pretrained(lowercase_					)
               _snake_case      :							Any     				=						tokenizer(
                   [
                       '''Today is a beautiful day and I want to''',
                       '''In the city of''',
                   ]     ,  return_tensors='''jax'''     ,  padding=lowercase_     ,  )
  
               _snake_case      :							Any     				=						jax.jit(model.generate					)
  
               _snake_case      :							List[str]     				=						jit_generate(inputs['''input_ids''']     ,  attention_mask=inputs['''attention_mask''']					).sequences
  
               _snake_case      :							List[Any]     				=						tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
  
               self.assertIsNotNone(lowercase_     ,  lowercase_					)
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							str     				=						'''facebook/opt-350m'''
  
               _snake_case      :							Optional[Any]     				=						GPTaTokenizer.from_pretrained(lowercase_					)
               _snake_case      :							Union[str, Any]     				=						FlaxOPTForCausalLM.from_pretrained(lowercase_					)
  
               _snake_case      :							int     				=						'''left'''
  
               # use different length sentences to test batching
               _snake_case      :							Tuple     				=						[
                   '''Hello, my dog is a little''',
                   '''Today, I''',
               ]
  
               _snake_case      :							str     				=						tokenizer(lowercase_     ,  return_tensors='''jax'''     ,  padding=lowercase_					)
               _snake_case      :							List[str]     				=						inputs['''input_ids''']
  
               _snake_case      :							Union[str, Any]     				=						model.generate(input_ids=lowercase_     ,  attention_mask=inputs['''attention_mask''']     ,  trace=lowercase_					)
  
               _snake_case      :							List[Any]     				=						tokenizer(sentences[0]     ,  return_tensors='''jax'''					).input_ids
               _snake_case      :							int     				=						model.generate(input_ids=lowercase_					)
  
               _snake_case      :							List[str]     				=						inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
               _snake_case      :							Any     				=						tokenizer(sentences[1]     ,  return_tensors='''jax'''					).input_ids
               _snake_case      :							Optional[Any]     				=						model.generate(input_ids=lowercase_     ,  max_length=model.config.max_length - num_paddings					)
  
               _snake_case      :							int     				=						tokenizer.batch_decode(outputs[0]     ,  skip_special_tokens=lowercase_					)
               _snake_case      :							List[str]     				=						tokenizer.decode(output_non_padded[0][0]     ,  skip_special_tokens=lowercase_					)
               _snake_case      :							List[Any]     				=						tokenizer.decode(output_padded[0][0]     ,  skip_special_tokens=lowercase_					)
  
               _snake_case      :							List[str]     				=						[
                   '''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(lowercase_     ,  lowercase_					)
               self.assertListEqual(lowercase_     ,  [non_padded_sentence, padded_sentence]					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def 						A_			(  lowercase_  )			->						list:
         if len(lowercase_  ) <= 1:
                return data
         else:
                return [
                    *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                    data[0],
                    *quick_sort([e for e in data[1:] if e > data[0]]  ),
                ]
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  
  
  
  
  
  
  