




'''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
      lowercase	:      Optional[int]       	=       "platform"

      import jax
      import jax.numpy as jnp

      from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase=None   ,				lowerCamelCase=None      )				->Tuple:






       '''simple docstring'''




       if attention_mask is None:
              lowercase  :		Optional[Any]						=      np.where(input_ids != config.pad_token_id   ,				1   ,				0      )
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
       }

@require_flax
class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self   ,		A   ,		A=13   ,		A=7   ,		A=True   ,		A=False   ,		A=99   ,		A=16   ,		A=2   ,		A=4   ,		A=4   ,		A="gelu"   ,		A=0.1   ,		A=0.1   ,		A=20   ,		A=2   ,		A=1   ,		A=0   ,		A=16   ,		A=16   ,		A=0.0_2   ,		):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      parent
        lowercase  :		Any						=      batch_size
        lowercase  :		Optional[int]						=      seq_length
        lowercase  :		Tuple						=      is_training
        lowercase  :		List[Any]						=      use_labels
        lowercase  :		List[Any]						=      vocab_size
        lowercase  :		int						=      hidden_size
        lowercase  :		Union[str, Any]						=      num_hidden_layers
        lowercase  :		str						=      num_attention_heads
        lowercase  :		Tuple						=      intermediate_size
        lowercase  :		List[Any]						=      hidden_act
        lowercase  :		Union[str, Any]						=      hidden_dropout_prob
        lowercase  :		Dict						=      attention_probs_dropout_prob
        lowercase  :		Optional[int]						=      max_position_embeddings
        lowercase  :		int						=      eos_token_id
        lowercase  :		Optional[int]						=      pad_token_id
        lowercase  :		Tuple						=      bos_token_id
        lowercase  :		List[str]						=      embed_dim
        lowercase  :		Tuple						=      word_embed_proj_dim
        lowercase  :		Dict						=      initializer_range
        lowercase  :		Optional[int]						=      False



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,		self.vocab_size )   ,		3   ,		self.vocab_size )
        lowercase  :		List[Any]						=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,		dtype=np.intaa ))   ,		-1 )

        lowercase  :		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   ,		)
        lowercase  :		Union[str, Any]						=      prepare_opt_inputs_dict(__UpperCamelCase   ,		__UpperCamelCase )
        return config, inputs_dict



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      self.prepare_config_and_inputs()
        return config, inputs_dict



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		List[str]						=      20
        lowercase  :		Optional[Any]						=      model_class_name(__UpperCamelCase )

        lowercase  :		List[Any]						=      inputs_dict['''input_ids''']
        lowercase  :		Any						=      inputs_dict['''attention_mask''']

        lowercase  :		List[str]						=      model.init_cache(input_ids.shape[0]   ,		__UpperCamelCase )
        lowercase  :		int						=      jnp.ones((input_ids.shape[0], max_length)   ,		dtype='''i4''' )

        lowercase  :		int						=      jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1 )[None, :]   ,		(input_ids.shape[0], input_ids.shape[-1] - 1)   ,		)
        lowercase  :		int						=      model(
            input_ids[:, :-1]   ,		attention_mask=__UpperCamelCase   ,		past_key_values=__UpperCamelCase   ,		position_ids=__UpperCamelCase   ,		)

        lowercase  :		str						=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,		dtype='''i4''' )
        lowercase  :		Tuple						=      model(
            input_ids[:, -1:]   ,		attention_mask=__UpperCamelCase   ,		past_key_values=outputs_cache.past_key_values   ,		position_ids=__UpperCamelCase   ,		)

        lowercase  :		Dict						=      model(__UpperCamelCase )

        lowercase  :		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    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		int						=      20
        lowercase  :		Dict						=      model_class_name(__UpperCamelCase )

        lowercase  :		Union[str, Any]						=      (
            inputs_dict['''input_ids'''],
            inputs_dict['''attention_mask'''],
        )

        lowercase  :		Optional[Any]						=      jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1]) ),
            ]   ,		axis=-1   ,		)

        lowercase  :		Tuple						=      model.init_cache(input_ids.shape[0]   ,		__UpperCamelCase )
        lowercase  :		Union[str, Any]						=      jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1 )[None, :]   ,		(input_ids.shape[0], input_ids.shape[-1] - 1)   ,		)

        lowercase  :		Dict						=      model(
            input_ids[:, :-1]   ,		attention_mask=__UpperCamelCase   ,		past_key_values=__UpperCamelCase   ,		position_ids=__UpperCamelCase   ,		)
        lowercase  :		Union[str, Any]						=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,		dtype='''i4''' )
        lowercase  :		str						=      model(
            input_ids[:, -1:]   ,		past_key_values=outputs_cache.past_key_values   ,		attention_mask=__UpperCamelCase   ,		position_ids=__UpperCamelCase   ,		)

        lowercase  :		Optional[Any]						=      model(__UpperCamelCase   ,		attention_mask=__UpperCamelCase )

        lowercase  :		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   SCREAMING_SNAKE_CASE_		(       UpperCamelCase_		,     unittest.TestCase		,     UpperCamelCase_				):
 __snake_case	:     str			   =      (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
 __snake_case	:     Optional[int]			   =      () if is_flax_available() else ()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      FlaxOPTModelTester(self )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        for model_class_name in self.all_model_classes:
               lowercase  :		List[str]						=      model_class_name.from_pretrained('''facebook/opt-125m''' )
               lowercase  :		int						=      np.ones((1, 1) ) * model.config.eos_token_id
               lowercase  :		Any						=      model(__UpperCamelCase )
               self.assertIsNotNone(__UpperCamelCase )






@require_sentencepiece
@require_flax
class   SCREAMING_SNAKE_CASE_		(       unittest.TestCase				):



 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      FlaxOPTModel.from_pretrained('''facebook/opt-350m''' )
        lowercase  :		Optional[int]						=      jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
        lowercase  :		List[str]						=      model(input_ids=__UpperCamelCase ).last_hidden_state
        lowercase  :		str						=      (1, 11, 512)
        self.assertEqual(output.shape   ,		__UpperCamelCase )
        lowercase  :		Any						=      jnp.array(
            [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]] )
        self.assertTrue(jnp.allclose(output[:, :3, :3]   ,		__UpperCamelCase   ,		atol=4E-2 ) )






@require_flax
@slow
class   SCREAMING_SNAKE_CASE_		(       unittest.TestCase				):



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        super().setUp()
        lowercase  :		Union[str, Any]						=      '''facebook/opt-350m'''





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      FlaxOPTForCausalLM.from_pretrained(self.path_model )
        lowercase  :		Union[str, Any]						=      GPTaTokenizer.from_pretrained(self.path_model )

        lowercase  :		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
        lowercase  :		int						=      tokenizer(__UpperCamelCase   ,		return_tensors='''jax'''   ,		padding=__UpperCamelCase   ,		add_special_tokens=__UpperCamelCase )
        lowercase  :		Optional[Any]						=      model(inputs.input_ids   ,		attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
        lowercase  :		Optional[Any]						=      jnp.array(
            [
                [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
            ] )
        self.assertTrue(jnp.allclose(__UpperCamelCase   ,		__UpperCamelCase   ,		atol=4E-2 ) )

        lowercase  :		Union[str, Any]						=      jax.jit(__UpperCamelCase )
        lowercase  :		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   SCREAMING_SNAKE_CASE_		(       unittest.TestCase				):



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		int						=      '''facebook/opt-125m'''

        lowercase  :		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''',
        ]

        lowercase  :		Union[str, Any]						=      []

        lowercase  :		Optional[int]						=      FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase )
        lowercase  :		Optional[int]						=      GPTaTokenizer.from_pretrained(__UpperCamelCase )

        for prompt in self.prompts:
               lowercase  :		str						=      tokenizer(__UpperCamelCase   ,		return_tensors='''jax''' ).input_ids

               lowercase  :		Optional[int]						=      model.generate(__UpperCamelCase   ,		max_length=10 )
               lowercase  :		Optional[int]						=      generated_ids[0]

               lowercase  :		int						=      tokenizer.batch_decode(__UpperCamelCase   ,		skip_special_tokens=__UpperCamelCase )
               predicted_outputs += generated_string

        self.assertListEqual(__UpperCamelCase   ,		__UpperCamelCase )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      '''facebook/opt-350m'''

        lowercase  :		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''',
        ]

        lowercase  :		int						=      []
        lowercase  :		Union[str, Any]						=      FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase )
        lowercase  :		Tuple						=      GPTaTokenizer.from_pretrained(__UpperCamelCase )

        for prompt in self.prompts:
               lowercase  :		Any						=      tokenizer(__UpperCamelCase   ,		return_tensors='''jax''' ).input_ids

               lowercase  :		Union[str, Any]						=      model.generate(__UpperCamelCase   ,		max_length=10 )
               lowercase  :		int						=      generated_ids[0]

               lowercase  :		Optional[Any]						=      tokenizer.batch_decode(__UpperCamelCase   ,		skip_special_tokens=__UpperCamelCase )
               predicted_outputs += generated_string

        self.assertListEqual(__UpperCamelCase   ,		__UpperCamelCase )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      '''facebook/opt-125m'''
        lowercase  :		int						=      [
            '''Today is a beautiful day and I want to thank''',
            '''In the city of Rome Canaver Canaver Canaver Canaver''',
        ]
        lowercase  :		Optional[Any]						=      FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase )
        lowercase  :		List[Any]						=      GPTaTokenizer.from_pretrained(__UpperCamelCase )
        lowercase  :		Union[str, Any]						=      tokenizer(
            [
                '''Today is a beautiful day and I want to''',
                '''In the city of''',
            ]   ,		return_tensors='''jax'''   ,		padding=__UpperCamelCase   ,		)

        lowercase  :		Dict						=      jax.jit(model.generate )

        lowercase  :		Optional[int]						=      jit_generate(inputs['''input_ids''']   ,		attention_mask=inputs['''attention_mask'''] ).sequences

        lowercase  :		List[str]						=      tokenizer.batch_decode(__UpperCamelCase   ,		skip_special_tokens=__UpperCamelCase )

        self.assertIsNotNone(__UpperCamelCase   ,		__UpperCamelCase )





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      '''facebook/opt-350m'''

        lowercase  :		Union[str, Any]						=      GPTaTokenizer.from_pretrained(__UpperCamelCase )
        lowercase  :		Tuple						=      FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase )

        lowercase  :		Any						=      '''left'''

        # use different length sentences to test batching
        lowercase  :		str						=      [
            '''Hello, my dog is a little''',
            '''Today, I''',
        ]

        lowercase  :		Union[str, Any]						=      tokenizer(__UpperCamelCase   ,		return_tensors='''jax'''   ,		padding=__UpperCamelCase )
        lowercase  :		str						=      inputs['''input_ids''']

        lowercase  :		int						=      model.generate(input_ids=__UpperCamelCase   ,		attention_mask=inputs['''attention_mask''']   ,		trace=__UpperCamelCase )

        lowercase  :		int						=      tokenizer(sentences[0]   ,		return_tensors='''jax''' ).input_ids
        lowercase  :		Union[str, Any]						=      model.generate(input_ids=__UpperCamelCase )

        lowercase  :		int						=      inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
        lowercase  :		Optional[Any]						=      tokenizer(sentences[1]   ,		return_tensors='''jax''' ).input_ids
        lowercase  :		Dict						=      model.generate(input_ids=__UpperCamelCase   ,		max_length=model.config.max_length - num_paddings )

        lowercase  :		Union[str, Any]						=      tokenizer.batch_decode(outputs[0]   ,		skip_special_tokens=__UpperCamelCase )
        lowercase  :		str						=      tokenizer.decode(output_non_padded[0][0]   ,		skip_special_tokens=__UpperCamelCase )
        lowercase  :		Union[str, Any]						=      tokenizer.decode(output_padded[0][0]   ,		skip_special_tokens=__UpperCamelCase )

        lowercase  :		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] )










'''simple docstring'''






import math
from timeit import timeit




def 		a_( lowerCamelCase      )				->int:






       '''simple docstring'''




       lowercase  :		Dict						=      0
       lowercase  :		Union[str, Any]						=      abs(lowerCamelCase      )
       while True:
              lowercase  :		List[Any]						=      n // 10
              digits += 1
              if n == 0:
                     break
       return digits




def 		a_( lowerCamelCase      )				->int:






       '''simple docstring'''




       return 1 if n == 0 else math.floor(math.log(abs(lowerCamelCase      )   ,				10      ) + 1      )




def 		a_( lowerCamelCase      )				->int:






       '''simple docstring'''




       return len(str(abs(lowerCamelCase      )      )      )




def 		a_( )				->None:






       '''simple docstring'''




       from collections.abc import Callable

       def benchmark_a_function(lowerCamelCase   ,				lowerCamelCase      ) -> None:
              lowercase  :		str						=      f"""{func.__name__}({value})"""
              lowercase  :		Optional[Any]						=      timeit(f"""__main__.{call}"""   ,				setup='''import __main__'''      )
              print(f"""{call}: {func(lowerCamelCase      )} -- {timing} seconds"""      )

       for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
              for func in (num_digits, num_digits_fast, num_digits_faster):
                     benchmark_a_function(lowerCamelCase   ,				lowerCamelCase      )
              print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()




