
"""simple docstring"""

def      lowercase__    (       lowerCAmelCase					:       list			,					lowerCAmelCase					:       int = 0			)			->   list:






    """simple docstring"""




    UpperCAmelCase          =       length or len(lowerCAmelCase			)
    UpperCAmelCase          =       False
    for i in range(length - 1			):
        if list_data[i] > list_data[i + 1]:
            UpperCAmelCase      ,				UpperCAmelCase          =       list_data[i + 1], list_data[i]
            UpperCAmelCase          =       True

    return list_data if not swapped else bubble_sort(lowerCAmelCase			,					length - 1			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()





"""simple docstring"""

import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       FlaubertForMultipleChoice,
       FlaubertForQuestionAnswering,
       FlaubertForQuestionAnsweringSimple,
       FlaubertForSequenceClassification,
       FlaubertForTokenClassification,
       FlaubertModel,
       FlaubertWithLMHeadModel,
   )
   from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):




      def __init__(				self ,							lowercase_ ,							lowercase_=1_3 ,							lowercase_=7 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=False ,							lowercase_=False ,							lowercase_=False ,							lowercase_=2 ,							lowercase_=9_9 ,							lowercase_=0 ,							lowercase_=3_2 ,							lowercase_=5 ,							lowercase_=4 ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=5_1_2 ,							lowercase_=1_2 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=3 ,							lowercase_=4 ,							lowercase_="last" ,							lowercase_=None ,							lowercase_=None ,							)    ->       str:
          UpperCAmelCase          =       parent
          UpperCAmelCase          =       batch_size
          UpperCAmelCase          =       seq_length
          UpperCAmelCase          =       is_training
          UpperCAmelCase          =       use_input_lengths
          UpperCAmelCase          =       use_token_type_ids
          UpperCAmelCase          =       use_labels
          UpperCAmelCase          =       gelu_activation
          UpperCAmelCase          =       sinusoidal_embeddings
          UpperCAmelCase          =       causal
          UpperCAmelCase          =       asm
          UpperCAmelCase          =       n_langs
          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       n_special
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       hidden_dropout_prob
          UpperCAmelCase          =       attention_probs_dropout_prob
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       type_vocab_size
          UpperCAmelCase          =       type_sequence_label_size
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       num_labels
          UpperCAmelCase          =       num_choices
          UpperCAmelCase          =       summary_type
          UpperCAmelCase          =       use_proj
          UpperCAmelCase          =       scope




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size				)
          UpperCAmelCase          =       random_attention_mask([self.batch_size, self.seq_length]				)

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

          UpperCAmelCase          =       None
          if self.use_token_type_ids:
              UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.n_langs				)

          UpperCAmelCase          =       None
          UpperCAmelCase          =       None
          UpperCAmelCase          =       None
          if self.use_labels:
              UpperCAmelCase          =       ids_tensor([self.batch_size] ,							self.type_sequence_label_size				)
              UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.num_labels				)
              UpperCAmelCase          =       ids_tensor([self.batch_size] ,							2				).float()
              UpperCAmelCase          =       ids_tensor([self.batch_size] ,							self.num_choices				)

          UpperCAmelCase          =       self.get_config()

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




      def      a_   (				self				)    ->       Union[str, Any]:
          return 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 ,							)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       List[str]:
          UpperCAmelCase          =       FlaubertModel(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(lowercase_ ,							lengths=lowercase_ ,							langs=lowercase_				)
          UpperCAmelCase          =       model(lowercase_ ,							langs=lowercase_				)
          UpperCAmelCase          =       model(lowercase_				)
          self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       Any:
          UpperCAmelCase          =       FlaubertWithLMHeadModel(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_ ,							token_type_ids=lowercase_ ,							labels=lowercase_				)
          self.parent.assertEqual(result.loss.shape ,							()				)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       List[str]:
          UpperCAmelCase          =       FlaubertForQuestionAnsweringSimple(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_				)

          UpperCAmelCase          =       model(lowercase_ ,							start_positions=lowercase_ ,							end_positions=lowercase_				)
          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      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       int:
          UpperCAmelCase          =       FlaubertForQuestionAnswering(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_				)

          UpperCAmelCase          =       model(
              lowercase_ ,							start_positions=lowercase_ ,							end_positions=lowercase_ ,							cls_index=lowercase_ ,							is_impossible=lowercase_ ,							p_mask=lowercase_ ,							)

          UpperCAmelCase          =       model(
              lowercase_ ,							start_positions=lowercase_ ,							end_positions=lowercase_ ,							cls_index=lowercase_ ,							is_impossible=lowercase_ ,							)

          ((UpperCAmelCase)      ,				)          =       result_with_labels.to_tuple()

          UpperCAmelCase          =       model(lowercase_ ,							start_positions=lowercase_ ,							end_positions=lowercase_				)

          ((UpperCAmelCase)      ,				)          =       result_with_labels.to_tuple()

          self.parent.assertEqual(result_with_labels.loss.shape ,							()				)
          self.parent.assertEqual(result.start_top_log_probs.shape ,							(self.batch_size, model.config.start_n_top)				)
          self.parent.assertEqual(result.start_top_index.shape ,							(self.batch_size, model.config.start_n_top)				)
          self.parent.assertEqual(
              result.end_top_log_probs.shape ,							(self.batch_size, model.config.start_n_top * model.config.end_n_top)				)
          self.parent.assertEqual(
              result.end_top_index.shape ,							(self.batch_size, model.config.start_n_top * model.config.end_n_top)				)
          self.parent.assertEqual(result.cls_logits.shape ,							(self.batch_size,)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       Dict:
          UpperCAmelCase          =       FlaubertForSequenceClassification(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_				)
          UpperCAmelCase          =       model(lowercase_ ,							labels=lowercase_				)

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




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       List[str]:
          UpperCAmelCase          =       self.num_labels
          UpperCAmelCase          =       FlaubertForTokenClassification(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_ ,							labels=lowercase_				)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.num_labels)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       Tuple:
          UpperCAmelCase          =       self.num_choices
          UpperCAmelCase          =       FlaubertForMultipleChoice(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       input_ids.unsqueeze(1				).expand(-1 ,							self.num_choices ,							-1				).contiguous()
          UpperCAmelCase          =       token_type_ids.unsqueeze(1				).expand(-1 ,							self.num_choices ,							-1				).contiguous()
          UpperCAmelCase          =       input_mask.unsqueeze(1				).expand(-1 ,							self.num_choices ,							-1				).contiguous()
          UpperCAmelCase          =       model(
              lowercase_ ,							attention_mask=lowercase_ ,							token_type_ids=lowercase_ ,							labels=lowercase_ ,							)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_choices)				)




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				
          )          =       config_and_inputs
          UpperCAmelCase          =       {
              'input_ids': input_ids,
              'token_type_ids': token_type_ids,
              'lengths': input_lengths,
              'attention_mask': input_mask,
          }
          return config, inputs_dict

@require_torch
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Union[str, Any]				   =						(
          (
              FlaubertModel,
              FlaubertWithLMHeadModel,
              FlaubertForQuestionAnswering,
              FlaubertForQuestionAnsweringSimple,
              FlaubertForSequenceClassification,
              FlaubertForTokenClassification,
              FlaubertForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE :				str				   =						(
          {
              "feature-extraction": FlaubertModel,
              "fill-mask": FlaubertWithLMHeadModel,
              "question-answering": FlaubertForQuestionAnsweringSimple,
              "text-classification": FlaubertForSequenceClassification,
              "token-classification": FlaubertForTokenClassification,
              "zero-shot": FlaubertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Union[str, Any]:
          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      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_=False				)    ->       List[str]:
          UpperCAmelCase          =       super()._prepare_for_class(lowercase_ ,							lowercase_ ,							return_labels=lowercase_				)

          if return_labels:
              if model_class.__name__ == "FlaubertForQuestionAnswering":
                  UpperCAmelCase          =       torch.zeros(
                      self.model_tester.batch_size ,							dtype=torch.long ,							device=lowercase_				)
                  UpperCAmelCase          =       torch.zeros(
                      self.model_tester.batch_size ,							dtype=torch.long ,							device=lowercase_				)

          return inputs_dict




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       FlaubertModelTester(self				)
          UpperCAmelCase          =       ConfigTester(self ,							config_class=lowercase_ ,							emb_dim=3_7				)




      def      a_   (				self				)    ->       Optional[Any]:
          self.config_tester.run_common_tests()




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_model(*lowercase_				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_lm_head(*lowercase_				)




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_simple_qa(*lowercase_				)




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_qa(*lowercase_				)




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase_				)




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_token_classif(*lowercase_				)




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_multiple_choice(*lowercase_				)




      @slow
      def      a_   (				self				)    ->       List[str]:
          for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase          =       FlaubertModel.from_pretrained(lowercase_				)
              self.assertIsNotNone(lowercase_				)




      @slow
      @require_torch_gpu
      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase      ,				UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
              # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
              if model_class == FlaubertForMultipleChoice:
                  return

              UpperCAmelCase          =       True
              UpperCAmelCase          =       model_class(config=lowercase_				)

              UpperCAmelCase          =       self._prepare_for_class(lowercase_ ,							lowercase_				)
              UpperCAmelCase          =       torch.jit.trace(
                  lowercase_ ,							(inputs_dict['input_ids'].to('cpu'				), inputs_dict['attention_mask'].to('cpu'				))				)

              with tempfile.TemporaryDirectory() as tmp:
                  torch.jit.save(lowercase_ ,							os.path.join(lowercase_ ,							'traced_model.pt'				)				)
                  UpperCAmelCase          =       torch.jit.load(os.path.join(lowercase_ ,							'traced_model.pt'				) ,							map_location=lowercase_				)
                  loaded(inputs_dict['input_ids'].to(lowercase_				) ,							inputs_dict['attention_mask'].to(lowercase_				)				)

@require_torch
class   _UpperCAmelCase				(		unittest.TestCase				):




      @slow
      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       FlaubertModel.from_pretrained('flaubert/flaubert_base_cased'				)
          UpperCAmelCase          =       torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]				)
          with torch.no_grad():
              UpperCAmelCase          =       model(lowercase_				)[0]
          UpperCAmelCase          =       torch.Size((1, 1_1, 7_6_8)				)
          self.assertEqual(output.shape ,							lowercase_				)
          UpperCAmelCase          =       torch.tensor(
              [[[-2.6_2_5_1, -1.4_2_9_8, -0.0_2_2_7], [-2.8_5_1_0, -1.6_3_8_7, 0.2_2_5_8], [-2.8_1_1_4, -1.1_8_3_2, -0.3_0_6_6]]]				)

          self.assertTrue(torch.allclose(output[:, :3, :3] ,							lowercase_ ,							atol=1E-4				)				)



