

"""simple docstring"""






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						) ->      List[str]:
 if attention_mask is None:
  __magic_name__     =      tf.cast(tf.math.not_equal(__UpperCamelCase      ,   config.pad_token_id						)      ,   tf.inta						)
 return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class        _lowercase					:
     _lowerCamelCase     =       OPTConfig
     _lowerCamelCase     =       {}
     _lowerCamelCase     =       '''gelu'''







     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 ,						):
      __magic_name__     =      parent
      __magic_name__     =      batch_size
      __magic_name__     =      seq_length
      __magic_name__     =      is_training
      __magic_name__     =      use_labels
      __magic_name__     =      vocab_size
      __magic_name__     =      hidden_size
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      num_attention_heads
      __magic_name__     =      intermediate_size
      __magic_name__     =      hidden_act
      __magic_name__     =      hidden_dropout_prob
      __magic_name__     =      attention_probs_dropout_prob
      __magic_name__     =      max_position_embeddings
      __magic_name__     =      eos_token_id
      __magic_name__     =      pad_token_id
      __magic_name__     =      bos_token_id
      __magic_name__     =      embed_dim
      __magic_name__     =      word_embed_proj_dim
      __magic_name__     =      False







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

      __magic_name__     =      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=UpperCamelCase_ ,						**self.config_updates ,						)
      __magic_name__     =      prepare_opt_inputs_dict(UpperCamelCase_ ,						UpperCamelCase_       )
      return config, inputs_dict







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      TFOPTModel(config=UpperCamelCase_       )
      __magic_name__     =      inputs_dict['''input_ids''']

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

      # first forward pass
      __magic_name__     =      model(UpperCamelCase_ ,						attention_mask=UpperCamelCase_ ,						use_cache=UpperCamelCase_       )

      __magic_name__   ,						__magic_name__     =      outputs.to_tuple()

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

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

      __magic_name__     =      model(UpperCamelCase_ ,						attention_mask=UpperCamelCase_       )[0]
      __magic_name__     =      model(UpperCamelCase_ ,						attention_mask=UpperCamelCase_ ,						past_key_values=UpperCamelCase_       )[0]

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

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

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


@require_tf
class        _lowercase					(     __UpperCAmelCase   , __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
     _lowerCamelCase     =       (TFOPTForCausalLM,) if is_tf_available() else ()
     _lowerCamelCase     =       (
         {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
     )
     _lowerCamelCase     =       False
     _lowerCamelCase     =       False
     _lowerCamelCase     =       False
     _lowerCamelCase     =       10







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      TFOPTModelTester(self       )
      __magic_name__     =      ConfigTester(self ,						config_class=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      self.config_tester.run_common_tests()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__     =      self.model_tester.prepare_config_and_inputs_for_common()

      def _get_word_embedding_weight(UpperCamelCase_ ,						UpperCamelCase_       ):
       if hasattr(UpperCamelCase_ ,						'''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(UpperCamelCase_ ,						'''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
        __magic_name__     =      model_class(config=UpperCamelCase_       )
        __magic_name__     =      _get_word_embedding_weight(UpperCamelCase_ ,						model.get_input_embeddings()       )
        __magic_name__     =      _get_word_embedding_weight(UpperCamelCase_ ,						model.get_output_embeddings()       )

        # reshape the embeddings
        model.resize_token_embeddings(UpperCamelCase_       )
        __magic_name__     =      _get_word_embedding_weight(UpperCamelCase_ ,						model.get_input_embeddings()       )
        __magic_name__     =      _get_word_embedding_weight(UpperCamelCase_ ,						model.get_output_embeddings()       )

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

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

        # check that weights remain the same after resizing
        __magic_name__     =      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:
          __magic_name__     =      False
        self.assertTrue(UpperCamelCase_       )

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

         __magic_name__     =      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:
           __magic_name__     =      False
         self.assertTrue(UpperCamelCase_       )





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return tf.constant(__UpperCamelCase      ,   dtype=tf.intaa						)
@require_tf
class        _lowercase					(     unittest.TestCase     ):
     _lowerCamelCase     =       99







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      tf.ones((4, 1) ,						dtype=tf.intaa       ) * 2
      __magic_name__     =      tf.concat([ids_tensor((4, 6) ,						self.vocab_size - 3       ) + 3, eos_column_vector] ,						axis=1       )
      __magic_name__     =      input_ids.shape[0]
      __magic_name__     =      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        _lowercase					(     unittest.TestCase     ):







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      TFOPTModel.from_pretrained('''facebook/opt-350m'''       )
      __magic_name__     =      _long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]       )
      __magic_name__     =      tf.not_equal(UpperCamelCase_ ,						model.config.pad_token_id       )
      with tf.GradientTape():
       __magic_name__     =      model(input_ids=UpperCamelCase_ ,						attention_mask=UpperCamelCase_       ).last_hidden_state
      __magic_name__     =      (1, 11, 512)
      self.assertEqual(output.shape ,						UpperCamelCase_       )
      __magic_name__     =      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] ,						UpperCamelCase_ ,						atol=4E-3       )       )

      __magic_name__     =      tf.function(UpperCamelCase_ ,						jit_compile=UpperCamelCase_       )
      __magic_name__     =      xla_generate(UpperCamelCase_ ,						UpperCamelCase_       )[0]
      self.assertTrue(np.allclose(output[:, :3, :3] ,						UpperCamelCase_ ,						atol=4E-2       )       )


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







     def 	lowerCAmelCase__    (      self       ):
      super().setUp()
      __magic_name__     =      '''facebook/opt-350m'''







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      TFOPTForCausalLM.from_pretrained(self.path_model       )
      __magic_name__     =      GPTaTokenizer.from_pretrained(self.path_model       )

      __magic_name__     =      [
          '''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
      __magic_name__     =      tokenizer(UpperCamelCase_ ,						return_tensors='''tf''' ,						padding=UpperCamelCase_ ,						add_special_tokens=UpperCamelCase_       )
      __magic_name__     =      tf.math.reduce_mean(model(inputs.input_ids ,						attention_mask=inputs.attention_mask       )[0] ,						axis=-1       )
      __magic_name__     =      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(UpperCamelCase_ ,						UpperCamelCase_ ,						atol=1E-4       )       )

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


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







     @property
     def 	lowerCAmelCase__    (      self       ):
      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       ):
      __magic_name__     =      '''facebook/opt-125m'''

      __magic_name__     =      [
          '''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''',
      ]

      __magic_name__     =      []
      __magic_name__     =      GPTaTokenizer.from_pretrained(UpperCamelCase_       )
      __magic_name__     =      TFOPTForCausalLM.from_pretrained(UpperCamelCase_       )

      for prompt in self.prompts:
       __magic_name__     =      tokenizer(UpperCamelCase_ ,						return_tensors='''tf'''       ).input_ids

       __magic_name__     =      model.generate(UpperCamelCase_ ,						max_length=10       )

       __magic_name__     =      tokenizer.batch_decode(UpperCamelCase_ ,						skip_special_tokens=UpperCamelCase_       )
       predicted_outputs += generated_string

      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''facebook/opt-350m'''

      __magic_name__     =      GPTaTokenizer.from_pretrained(UpperCamelCase_       )
      __magic_name__     =      TFOPTForCausalLM.from_pretrained(UpperCamelCase_       )

      __magic_name__     =      '''left'''

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

      __magic_name__     =      tokenizer(UpperCamelCase_ ,						return_tensors='''tf''' ,						padding=UpperCamelCase_       )
      __magic_name__     =      inputs['''input_ids''']

      __magic_name__     =      model.generate(input_ids=UpperCamelCase_ ,						attention_mask=inputs['''attention_mask''']       )

      __magic_name__     =      tokenizer(sentences[0] ,						return_tensors='''tf'''       ).input_ids
      __magic_name__     =      model.generate(input_ids=UpperCamelCase_       )

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

      __magic_name__     =      tokenizer.batch_decode(UpperCamelCase_ ,						skip_special_tokens=UpperCamelCase_       )
      __magic_name__     =      tokenizer.decode(output_non_padded[0] ,						skip_special_tokens=UpperCamelCase_       )
      __magic_name__     =      tokenizer.decode(output_padded[0] ,						skip_special_tokens=UpperCamelCase_       )

      __magic_name__     =      [
          '''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]       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''facebook/opt-350m'''

      __magic_name__     =      [
          '''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''',
      ]

      __magic_name__     =      []
      __magic_name__     =      GPTaTokenizer.from_pretrained(UpperCamelCase_       )
      __magic_name__     =      TFOPTForCausalLM.from_pretrained(UpperCamelCase_       )

      for prompt in self.prompts:
       __magic_name__     =      tokenizer(UpperCamelCase_ ,						return_tensors='''tf'''       ).input_ids

       __magic_name__     =      model.generate(UpperCamelCase_ ,						max_length=10       )

       __magic_name__     =      tokenizer.batch_decode(UpperCamelCase_ ,						skip_special_tokens=UpperCamelCase_       )
       predicted_outputs += generated_string

      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )




"""simple docstring"""






from __future__ import annotations

from random import choice





def 	lowercase      (   __UpperCamelCase						) ->      Any:
 return choice(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 __magic_name__     =      random_pivot(__UpperCamelCase						)

 # partition based on pivot
 # linear time
 __magic_name__     =      [e for e in lst if e < pivot]
 __magic_name__     =      [e for e in lst if e > pivot]

 # if we get lucky, pivot might be the element we want.
 # we can easily see this:
 # small (elements smaller than k)
 # + pivot (kth element)
 # + big (elements larger than k)
 if len(__UpperCamelCase						) == k - 1:
  return pivot
 # pivot is in elements bigger than k
 elif len(__UpperCamelCase						) < k - 1:
  return kth_number(__UpperCamelCase      ,   k - len(__UpperCamelCase						) - 1						)
 # pivot is in elements smaller than k
 else:
  return kth_number(__UpperCamelCase      ,   __UpperCamelCase						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

