from __future__ import annotations

import unittest

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

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


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import (
       TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       FlaubertConfig,
       TFFlaubertForMultipleChoice,
       TFFlaubertForQuestionAnsweringSimple,
       TFFlaubertForSequenceClassification,
       TFFlaubertForTokenClassification,
       TFFlaubertModel,
       TFFlaubertWithLMHeadModel,
   )





class 		_lowercase						:

 def __init__( self	:							Any   ,      snake_case	:							Optional[Any]   ,      )   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						str =					parent
  UpperCamelCase_					:						Union[str, Any] =					1_3
  UpperCamelCase_					:						Optional[Any] =					7
  UpperCamelCase_					:						List[str] =					True
  UpperCamelCase_					:						Optional[int] =					True
  UpperCamelCase_					:						Union[str, Any] =					True
  UpperCamelCase_					:						List[Any] =					True
  UpperCamelCase_					:						Tuple =					True
  UpperCamelCase_					:						Any =					False
  UpperCamelCase_					:						int =					False
  UpperCamelCase_					:						Tuple =					False
  UpperCamelCase_					:						Union[str, Any] =					2
  UpperCamelCase_					:						Dict =					9_9
  UpperCamelCase_					:						Tuple =					0
  UpperCamelCase_					:						Any =					3_2
  UpperCamelCase_					:						List[Any] =					2
  UpperCamelCase_					:						Tuple =					4
  UpperCamelCase_					:						List[str] =					0.1
  UpperCamelCase_					:						int =					0.1
  UpperCamelCase_					:						int =					5_1_2
  UpperCamelCase_					:						List[Any] =					1_6
  UpperCamelCase_					:						Any =					2
  UpperCamelCase_					:						Any =					0.02
  UpperCamelCase_					:						List[str] =					3
  UpperCamelCase_					:						Tuple =					4
  UpperCamelCase_					:						int =					"last"
  UpperCamelCase_					:						int =					True
  UpperCamelCase_					:						Dict =					None
  UpperCamelCase_					:						Tuple =					0

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						str =					ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size			)
  UpperCamelCase_					:						Tuple =					random_attention_mask([self.batch_size, self.seq_length]   ,      dtype=tf.floataa			)

  UpperCamelCase_					:						Tuple =					None
  if self.use_input_lengths:
   UpperCamelCase_					:						Optional[Any] =					(
       ids_tensor([self.batch_size]   ,      vocab_size=2			) + self.seq_length - 2
   )  # small variation of seq_length

  UpperCamelCase_					:						str =					None
  if self.use_token_type_ids:
   UpperCamelCase_					:						Optional[int] =					ids_tensor([self.batch_size, self.seq_length]   ,      self.n_langs			)

  UpperCamelCase_					:						Dict =					None
  UpperCamelCase_					:						Dict =					None
  UpperCamelCase_					:						Tuple =					None
  if self.use_labels:
   UpperCamelCase_					:						str =					ids_tensor([self.batch_size]   ,      self.type_sequence_label_size			)
   UpperCamelCase_					:						Union[str, Any] =					ids_tensor([self.batch_size, self.seq_length]   ,      self.num_labels			)
   UpperCamelCase_					:						int =					ids_tensor([self.batch_size]   ,      2   ,      dtype=tf.floataa			)
   UpperCamelCase_					:						Union[str, Any] =					ids_tensor([self.batch_size]   ,      self.num_choices			)

  UpperCamelCase_					:						List[Any] =					FlaubertConfig(
      vocab_size=self.vocab_size   ,      n_special=self.n_special   ,      emb_dim=self.hidden_size   ,      n_layers=self.num_hidden_layers   ,      n_heads=self.num_attention_heads   ,      dropout=self.hidden_dropout_prob   ,      attention_dropout=self.attention_probs_dropout_prob   ,      gelu_activation=self.gelu_activation   ,      sinusoidal_embeddings=self.sinusoidal_embeddings   ,      asm=self.asm   ,      causal=self.causal   ,      n_langs=self.n_langs   ,      max_position_embeddings=self.max_position_embeddings   ,      initializer_range=self.initializer_range   ,      summary_type=self.summary_type   ,      use_proj=self.use_proj   ,      bos_token_id=self.bos_token_id   ,      )

  return (
      config,
      input_ids,
      token_type_ids,
      input_lengths,
      sequence_labels,
      token_labels,
      is_impossible_labels,
      choice_labels,
      input_mask,
  )

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							Any   ,      snake_case	:							Tuple   ,      snake_case	:							Any   ,      snake_case	:							List[Any]   ,      snake_case	:							str   ,      snake_case	:							int   ,      snake_case	:							Dict   ,      snake_case	:							Dict   ,      snake_case	:							List[Any]   ,      )   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					TFFlaubertModel(config=__A			)
  UpperCamelCase_					:						Any =					{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
  UpperCamelCase_					:						Dict =					model(__A			)

  UpperCamelCase_					:						Any =					[input_ids, input_mask]
  UpperCamelCase_					:						Tuple =					model(__A			)
  self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							str   ,      snake_case	:							List[Any]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							int   ,      snake_case	:							List[Any]   ,      snake_case	:							Tuple   ,      snake_case	:							Dict   ,      )   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						int =					TFFlaubertWithLMHeadModel(__A			)

  UpperCamelCase_					:						List[str] =					{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
  UpperCamelCase_					:						int =					model(__A			)

  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							Any   ,      snake_case	:							Any   ,      snake_case	:							Optional[int]   ,      snake_case	:							int   ,      snake_case	:							Tuple   ,      snake_case	:							List[Any]   ,      snake_case	:							List[Any]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							int   ,      )   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					TFFlaubertForQuestionAnsweringSimple(__A			)

  UpperCamelCase_					:						Optional[int] =					{"input_ids": input_ids, "lengths": input_lengths}

  UpperCamelCase_					:						Union[str, Any] =					model(__A			)

  self.parent.assertEqual(result.start_logits.shape   ,      (self.batch_size, self.seq_length)			)
  self.parent.assertEqual(result.end_logits.shape   ,      (self.batch_size, self.seq_length)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Tuple   ,      snake_case	:							Dict   ,      snake_case	:							List[str]   ,      snake_case	:							List[Any]   ,      snake_case	:							List[str]   ,      snake_case	:							List[str]   ,      snake_case	:							str   ,      snake_case	:							int   ,      snake_case	:							int   ,      )   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					TFFlaubertForSequenceClassification(__A			)

  UpperCamelCase_					:						str =					{"input_ids": input_ids, "lengths": input_lengths}

  UpperCamelCase_					:						Union[str, Any] =					model(__A			)

  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.type_sequence_label_size)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							List[Any]   ,      snake_case	:							Any   ,      snake_case	:							Optional[int]   ,      snake_case	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Any   ,      snake_case	:							List[Any]   ,      snake_case	:							Dict   ,      snake_case	:							List[str]   ,      )   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					self.num_labels
  UpperCamelCase_					:						Optional[Any] =					TFFlaubertForTokenClassification(config=__A			)
  UpperCamelCase_					:						int =					{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
  UpperCamelCase_					:						Union[str, Any] =					model(__A			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.num_labels)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							List[Any]   ,      snake_case	:							Tuple   ,      snake_case	:							List[Any]   ,      snake_case	:							Tuple   ,      snake_case	:							str   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							List[str]   ,      snake_case	:							Any   ,      snake_case	:							Dict   ,      )   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.num_choices
  UpperCamelCase_					:						Optional[Any] =					TFFlaubertForMultipleChoice(config=__A			)
  UpperCamelCase_					:						Tuple =					tf.tile(tf.expand_dims(__A   ,      1			)   ,      (1, self.num_choices, 1)			)
  UpperCamelCase_					:						int =					tf.tile(tf.expand_dims(__A   ,      1			)   ,      (1, self.num_choices, 1)			)
  UpperCamelCase_					:						List[str] =					tf.tile(tf.expand_dims(__A   ,      1			)   ,      (1, self.num_choices, 1)			)
  UpperCamelCase_					:						Optional[int] =					{
      "input_ids": multiple_choice_inputs_ids,
      "attention_mask": multiple_choice_input_mask,
      "token_type_ids": multiple_choice_token_type_ids,
  }
  UpperCamelCase_					:						Union[str, Any] =					model(__A			)
  self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_choices)			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.prepare_config_and_inputs()
  (
      UpperCamelCase_	
  )					:						Optional[Any] =					config_and_inputs
  UpperCamelCase_					:						List[Any] =					{
      "input_ids": input_ids,
      "token_type_ids": token_type_ids,
      "langs": token_type_ids,
      "lengths": input_lengths,
  }
  return config, inputs_dict





@require_tf
class 		_lowercase						(					snake_case_						,      snake_case_						,      unittest.TestCase       ):
 lowercase				     =   (
     (
         TFFlaubertModel,
         TFFlaubertWithLMHeadModel,
         TFFlaubertForSequenceClassification,
         TFFlaubertForQuestionAnsweringSimple,
         TFFlaubertForTokenClassification,
         TFFlaubertForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 lowercase				     =   (
     (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
 )  # TODO (PVP): Check other models whether language generation is also applicable
 lowercase				     =   (
     {
         "feature-extraction": TFFlaubertModel,
         "fill-mask": TFFlaubertWithLMHeadModel,
         "question-answering": TFFlaubertForQuestionAnsweringSimple,
         "text-classification": TFFlaubertForSequenceClassification,
         "token-classification": TFFlaubertForTokenClassification,
         "zero-shot": TFFlaubertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 lowercase				     =   False
 lowercase				     =   False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							int   ,      snake_case	:							Any   ,      snake_case	:							Any   ,      snake_case	:							List[Any]   ,      snake_case	:							Any			)   ->						List[Any]:

  """simple docstring"""





  if (
      pipeline_test_casse_name == "QAPipelineTests"
      and tokenizer_name is not None
      and not tokenizer_name.endswith('Fast'			)
  ):
   # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
   # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
   # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
   return True

  return False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					TFFlaubertModelTester(self			)
  UpperCamelCase_					:						Optional[int] =					ConfigTester(self   ,      config_class=__A   ,      emb_dim=3_7			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						List[Any]:

  """simple docstring"""





  self.config_tester.run_common_tests()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_flaubert_model(*__A			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_flaubert_lm_head(*__A			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_flaubert_qa(*__A			)

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

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_flaubert_sequence_classif(*__A			)

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_flaubert_for_token_classification(*__A			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						str =					self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_flaubert_for_multiple_choice(*__A			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Dict:

  """simple docstring"""





  for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   UpperCamelCase_					:						int =					TFFlaubertModel.from_pretrained(__A			)
   self.assertIsNotNone(__A			)





@require_tf
@require_sentencepiece
@require_tokenizers
class 		_lowercase						(					unittest.TestCase       ):

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased'			)

  UpperCamelCase_					:						str =					tf.convert_to_tensor(
      [[0, 1_5_8, 7_3_5, 2_5_9_2, 1_4_2_4, 6_7_2_7, 8_2, 1]]   ,      dtype=tf.intaa   ,      )  # "J'aime flaubert !"

  UpperCamelCase_					:						Dict =					model(__A			)[0]
  UpperCamelCase_					:						List[str] =					tf.TensorShape((1, 8, 5_1_2)			)
  self.assertEqual(output.shape   ,      __A			)
  # compare the actual values for a slice.
  UpperCamelCase_					:						Tuple =					tf.convert_to_tensor(
      [
          [
              [-1.8768773, -1.566555, 0.27072418],
              [-1.6920038, -0.5873505, 1.9329599],
              [-2.9563985, -1.6993835, 1.7972052],
          ]
      ]   ,      dtype=tf.floataa   ,      )

  self.assertTrue(np.allclose(output[:, :3, :3].numpy()   ,      expected_slice.numpy()   ,      atol=1e-4			)			)







def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for i in range(len(SCREAMING_SNAKE_CASE_	) - 1      ,						0      ,						-1	):
    lowerCamelCase :					Tuple    =				False

    for j in range(SCREAMING_SNAKE_CASE_      ,						0      ,						-1	):
      if unsorted[j] < unsorted[j - 1]:
        lowerCamelCase    ,	lowerCamelCase :					int    =				unsorted[j - 1], unsorted[j]
        lowerCamelCase :					Optional[int]    =				True

    for j in range(SCREAMING_SNAKE_CASE_	):
      if unsorted[j] > unsorted[j + 1]:
        lowerCamelCase    ,	lowerCamelCase :					Union[str, Any]    =				unsorted[j + 1], unsorted[j]
        lowerCamelCase :					Optional[Any]    =				True

    if not swapped:
      break
  return unsorted


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _snake_case             =     input('''Enter numbers separated by a comma:\n''').strip()
   _snake_case             =     [int(item) for item in user_input.split(''',''')]
   print(f'''{cocktail_shaker_sort(unsorted) = }''')



