






from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None					)->				Tuple:




      if attention_mask is None:
            UpperCamelCase       						=      tf.cast(tf.math.not_equal(__lowercase	,		config.pad_token_id					)	,		tf.inta					)
      return {"input_ids": input_ids, "attention_mask": attention_mask}






@require_tf
class        a_		:
  lowercase  	=   OPTConfig
  lowercase  	=   {}
  lowercase  	=   """gelu"""

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=13    ,	_SCREAMING_SNAKE_CASE=7    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=99    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE="gelu"    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=20    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE=0    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=16    ,	)						->   Dict:



        """simple docstring"""






        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       						=      False

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      ids_tensor([self.batch_size, self.seq_length - 1]    ,	self.vocab_size   )
        UpperCamelCase       						=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )    ,	1   )
        UpperCamelCase       						=      tf.concat([input_ids, eos_tensor]    ,	axis=1   )

        UpperCamelCase       						=      self.config_cls(
            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    ,	word_embed_proj_dim=self.word_embed_proj_dim    ,	is_encoder_decoder=_SCREAMING_SNAKE_CASE    ,	**self.config_updates    ,	)
        UpperCamelCase       						=      prepare_opt_inputs_dict(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
        return config, inputs_dict


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      TFOPTModel(config=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      inputs_dict['input_ids']

        UpperCamelCase       						=      input_ids[:1, :]
        UpperCamelCase       						=      inputs_dict['attention_mask'][:1, :]
        UpperCamelCase       						=      1

        # first forward pass
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	attention_mask=_SCREAMING_SNAKE_CASE    ,	use_cache=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        UpperCamelCase       						=      ids_tensor((self.batch_size, 3)    ,	config.vocab_size   )
        UpperCamelCase       						=      tf.cast(ids_tensor((self.batch_size, 3)    ,	2   )    ,	tf.inta   )

        # append to next input_ids and
        UpperCamelCase       						=      tf.concat([input_ids, next_tokens]    ,	axis=-1   )
        UpperCamelCase       						=      tf.concat([attention_mask, next_attn_mask]    ,	axis=-1   )

        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	attention_mask=_SCREAMING_SNAKE_CASE   )[0]
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	attention_mask=_SCREAMING_SNAKE_CASE    ,	past_key_values=_SCREAMING_SNAKE_CASE   )[0]

        self.parent.assertEqual(next_tokens.shape[1]    ,	output_from_past.shape[1]   )

        # select random slice
        UpperCamelCase       						=      int(ids_tensor((1,)    ,	output_from_past.shape[-1]   )   )
        UpperCamelCase       						=      output_from_no_past[:, -3:, random_slice_idx]
        UpperCamelCase       						=      output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	rtol=1e-3   )






@require_tf
class        a_		(    __A			, __A			, unittest.TestCase       ):
  lowercase  	=   (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
  lowercase  	=   (TFOPTForCausalLM,) if is_tf_available() else ()
  lowercase  	=   (
      {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
  )
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   10

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      TFOPTModelTester(self   )
        UpperCamelCase       						=      ConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        self.config_tester.run_common_tests()

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE   )


  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        def _get_word_embedding_weight(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              if hasattr(_SCREAMING_SNAKE_CASE    ,	"""weight"""   ):
                    return embedding_layer.weight
              else:
                    # Here we build the word embeddings weights if not exists.
                    # And then we retry to get the attribute once built.
                    model.build()
                    if hasattr(_SCREAMING_SNAKE_CASE    ,	"""weight"""   ):
                          return embedding_layer.weight
                    else:
                          return None

        for model_class in self.all_model_classes:
              for size in [config.vocab_size - 10, config.vocab_size + 10]:
                    # build the embeddings
                    UpperCamelCase       						=      model_class(config=_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      _get_word_embedding_weight(_SCREAMING_SNAKE_CASE    ,	model.get_input_embeddings()   )
                    UpperCamelCase       						=      _get_word_embedding_weight(_SCREAMING_SNAKE_CASE    ,	model.get_output_embeddings()   )

                    # reshape the embeddings
                    model.resize_token_embeddings(_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      _get_word_embedding_weight(_SCREAMING_SNAKE_CASE    ,	model.get_input_embeddings()   )
                    UpperCamelCase       						=      _get_word_embedding_weight(_SCREAMING_SNAKE_CASE    ,	model.get_output_embeddings()   )

                    # check that the resized embeddings size matches the desired size.
                    UpperCamelCase       						=      size if size is not None else config.vocab_size

                    self.assertEqual(new_input_embeddings.shape[0]    ,	_SCREAMING_SNAKE_CASE   )

                    # check that weights remain the same after resizing
                    UpperCamelCase       						=      True
                    for pa, pa in zip(old_input_embeddings.value()    ,	new_input_embeddings.value()   ):
                          if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
                                UpperCamelCase       						=      False
                    self.assertTrue(_SCREAMING_SNAKE_CASE   )

                    if old_output_embeddings is not None and new_output_embeddings is not None:
                          self.assertEqual(new_output_embeddings.shape[0]    ,	_SCREAMING_SNAKE_CASE   )

                          UpperCamelCase       						=      True
                          for pa, pa in zip(old_output_embeddings.value()    ,	new_output_embeddings.value()   ):
                                if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
                                      UpperCamelCase       						=      False
                          self.assertTrue(_SCREAMING_SNAKE_CASE   )







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      return tf.constant(__lowercase	,		dtype=tf.intaa					)






@require_tf
class        a_		(    unittest.TestCase       ):
  lowercase  	=   99

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      tf.ones((4, 1)    ,	dtype=tf.intaa   ) * 2
        UpperCamelCase       						=      tf.concat([ids_tensor((4, 6)    ,	self.vocab_size - 3   ) + 3, eos_column_vector]    ,	axis=1   )
        UpperCamelCase       						=      input_ids.shape[0]
        UpperCamelCase       						=      OPTConfig(
            vocab_size=self.vocab_size    ,	hidden_size=24    ,	num_hidden_layers=2    ,	num_attention_heads=2    ,	ffn_dim=32    ,	max_position_embeddings=48    ,	eos_token_id=2    ,	pad_token_id=1    ,	bos_token_id=0    ,	)
        return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class        a_		(    unittest.TestCase       ):

  @slow
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      TFOPTModel.from_pretrained("""facebook/opt-350m"""   )
        UpperCamelCase       						=      _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]   )
        UpperCamelCase       						=      tf.not_equal(_SCREAMING_SNAKE_CASE    ,	model.config.pad_token_id   )
        with tf.GradientTape():
              UpperCamelCase       						=      model(input_ids=_SCREAMING_SNAKE_CASE    ,	attention_mask=_SCREAMING_SNAKE_CASE   ).last_hidden_state
        UpperCamelCase       						=      (1, 11, 512)
        self.assertEqual(output.shape    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tf.constant(
            [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]]   )
        self.assertTrue(np.allclose(output[:, :3, :3]    ,	_SCREAMING_SNAKE_CASE    ,	atol=4e-3   )   )

        UpperCamelCase       						=      tf.function(_SCREAMING_SNAKE_CASE    ,	jit_compile=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      xla_generate(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )[0]
        self.assertTrue(np.allclose(output[:, :3, :3]    ,	_SCREAMING_SNAKE_CASE    ,	atol=4e-2   )   )






@require_tf
@slow
class        a_		(    unittest.TestCase       ):

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        super().setUp()
        UpperCamelCase       						=      'facebook/opt-350m'


  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      TFOPTForCausalLM.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(_SCREAMING_SNAKE_CASE    ,	return_tensors="""tf"""    ,	padding=_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tf.math.reduce_mean(model(inputs.input_ids    ,	attention_mask=inputs.attention_mask   )[0]    ,	axis=-1   )
        UpperCamelCase       						=      tf.constant(
            [
                [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(np.allclose(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	atol=1e-4   )   )

        UpperCamelCase       						=      tf.function(_SCREAMING_SNAKE_CASE    ,	jit_compile=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tf.math.reduce_mean(xla_generate(inputs.input_ids    ,	attention_mask=inputs.attention_mask   )[0]    ,	axis=-1   )
        self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	atol=1e-4   )   )






@require_tf
@slow
class        a_		(    unittest.TestCase       ):

  @property
  def 						A__		(   self   )						->   Dict:



        """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   )						->   Tuple:



        """simple docstring"""






        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       						=      GPTaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      TFOPTForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE   )

        for prompt in self.prompts:
              UpperCamelCase       						=      tokenizer(_SCREAMING_SNAKE_CASE    ,	return_tensors="""tf"""   ).input_ids

              UpperCamelCase       						=      model.generate(_SCREAMING_SNAKE_CASE    ,	max_length=10   )

              UpperCamelCase       						=      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    ,	skip_special_tokens=_SCREAMING_SNAKE_CASE   )
              predicted_outputs += generated_string

        self.assertListEqual(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      'facebook/opt-350m'

        UpperCamelCase       						=      GPTaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      TFOPTForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      'left'

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

        UpperCamelCase       						=      tokenizer(_SCREAMING_SNAKE_CASE    ,	return_tensors="""tf"""    ,	padding=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      inputs['input_ids']

        UpperCamelCase       						=      model.generate(input_ids=_SCREAMING_SNAKE_CASE    ,	attention_mask=inputs["""attention_mask"""]   )

        UpperCamelCase       						=      tokenizer(sentences[0]    ,	return_tensors="""tf"""   ).input_ids
        UpperCamelCase       						=      model.generate(input_ids=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      inputs_non_padded.shape[-1] - tf.math.reduce_sum(
            tf.cast(inputs["""attention_mask"""][-1]    ,	tf.intaa   )   )
        UpperCamelCase       						=      tokenizer(sentences[1]    ,	return_tensors="""tf"""   ).input_ids
        UpperCamelCase       						=      model.generate(input_ids=_SCREAMING_SNAKE_CASE    ,	max_length=model.config.max_length - num_paddings   )

        UpperCamelCase       						=      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    ,	skip_special_tokens=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.decode(output_non_padded[0]    ,	skip_special_tokens=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.decode(output_padded[0]    ,	skip_special_tokens=_SCREAMING_SNAKE_CASE   )

        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(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
        self.assertListEqual(_SCREAMING_SNAKE_CASE    ,	[non_padded_sentence, padded_sentence]   )


  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        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       						=      GPTaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      TFOPTForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE   )

        for prompt in self.prompts:
              UpperCamelCase       						=      tokenizer(_SCREAMING_SNAKE_CASE    ,	return_tensors="""tf"""   ).input_ids

              UpperCamelCase       						=      model.generate(_SCREAMING_SNAKE_CASE    ,	max_length=10   )

              UpperCamelCase       						=      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    ,	skip_special_tokens=_SCREAMING_SNAKE_CASE   )
              predicted_outputs += generated_string

        self.assertListEqual(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )








'''simple docstring'''

from __future__ import annotations







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				tuple[float, list[float]]:
      UpperCamelCase       						=      list(range(len(__UpperCamelCase					)					)					)
      UpperCamelCase       						=      [v / w for v, w in zip(__UpperCamelCase	,		__UpperCamelCase					)]
      index.sort(key=lambda __UpperCamelCase					: ratio[i]	,		reverse=__UpperCamelCase					)

      UpperCamelCase       						=      0
      UpperCamelCase       						=      [0] * len(__UpperCamelCase					)
      for i in index:
            if weight[i] <= capacity:
                  UpperCamelCase       						=      1
                  max_value += value[i]
                  capacity -= weight[i]
            else:
                  UpperCamelCase       						=      capacity / weight[i]
                  max_value += value[i] * capacity / weight[i]
                  break

      return max_value, fractions


if __name__ == "__main__":
     import doctest

     doctest.testmod()
