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     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  List[str]      ,   lowercase__ :  List[Any]      ,   lowercase__ :  List[Any]=13      ,   lowercase__ :  Tuple=7      ,   lowercase__ :  List[str]=True      ,   lowercase__ :  List[str]=True      ,   lowercase__ :  int=True      ,   lowercase__ :  Dict=True      ,   lowercase__ :  Optional[int]=True      ,   lowercase__ :  Dict=False      ,   lowercase__ :  List[str]=False      ,   lowercase__ :  int=False      ,   lowercase__ :  List[str]=2      ,   lowercase__ :  str=99      ,   lowercase__ :  List[str]=0      ,   lowercase__ :  Union[str, Any]=32      ,   lowercase__ :  Optional[int]=5      ,   lowercase__ :  List[str]=4      ,   lowercase__ :  str=0.1      ,   lowercase__ :  Optional[Any]=0.1      ,   lowercase__ :  Optional[int]=512      ,   lowercase__ :  Union[str, Any]=12      ,   lowercase__ :  Any=2      ,   lowercase__ :  Optional[int]=0.02      ,   lowercase__ :  Any=3      ,   lowercase__ :  Any=4      ,   lowercase__ :  Optional[int]="last"      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  Tuple=None      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = parent
        lowerCAmelCase__					     = batch_size
        lowerCAmelCase__					     = seq_length
        lowerCAmelCase__					     = is_training
        lowerCAmelCase__					     = use_input_lengths
        lowerCAmelCase__					     = use_token_type_ids
        lowerCAmelCase__					     = use_labels
        lowerCAmelCase__					     = gelu_activation
        lowerCAmelCase__					     = sinusoidal_embeddings
        lowerCAmelCase__					     = causal
        lowerCAmelCase__					     = asm
        lowerCAmelCase__					     = n_langs
        lowerCAmelCase__					     = vocab_size
        lowerCAmelCase__					     = n_special
        lowerCAmelCase__					     = hidden_size
        lowerCAmelCase__					     = num_hidden_layers
        lowerCAmelCase__					     = num_attention_heads
        lowerCAmelCase__					     = hidden_dropout_prob
        lowerCAmelCase__					     = attention_probs_dropout_prob
        lowerCAmelCase__					     = max_position_embeddings
        lowerCAmelCase__					     = type_vocab_size
        lowerCAmelCase__					     = type_sequence_label_size
        lowerCAmelCase__					     = initializer_range
        lowerCAmelCase__					     = num_labels
        lowerCAmelCase__					     = num_choices
        lowerCAmelCase__					     = summary_type
        lowerCAmelCase__					     = use_proj
        lowerCAmelCase__					     = scope




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)
        lowerCAmelCase__					     = random_attention_mask([self.batch_size, self.seq_length])

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

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

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

        lowerCAmelCase__					     = self.get_config()

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




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        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 		__snake_case						(      self :  Any      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  List[str]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  List[Any]      ,   lowercase__ :  str      ,   lowercase__ :  str      ,   lowercase__ :  Optional[int]      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = FlaubertModel(config=lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__					     = model(lowercase__      ,   lengths=lowercase__      ,   langs=lowercase__)
        lowerCAmelCase__					     = model(lowercase__      ,   langs=lowercase__)
        lowerCAmelCase__					     = model(lowercase__)
        self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size))




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  str      ,   lowercase__ :  str      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  str      ,   lowercase__ :  List[Any]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Tuple      ,   lowercase__ :  int      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = FlaubertWithLMHeadModel(lowercase__)
        model.to(lowercase__)
        model.eval()

        lowerCAmelCase__					     = 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 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  Dict      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Any      ,   lowercase__ :  str      ,   lowercase__ :  Dict      ,   lowercase__ :  Any      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  int      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = FlaubertForQuestionAnsweringSimple(lowercase__)
        model.to(lowercase__)
        model.eval()

        lowerCAmelCase__					     = model(lowercase__)

        lowerCAmelCase__					     = 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 		__snake_case						(      self :  Dict      ,   lowercase__ :  Any      ,   lowercase__ :  Tuple      ,   lowercase__ :  List[str]      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  Tuple      ,   lowercase__ :  int      ,   lowercase__ :  str      ,   lowercase__ :  List[Any]      ,   lowercase__ :  Optional[Any]      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = FlaubertForQuestionAnswering(lowercase__)
        model.to(lowercase__)
        model.eval()

        lowerCAmelCase__					     = model(lowercase__)

        lowerCAmelCase__					     = model(
            lowercase__      ,   start_positions=lowercase__      ,   end_positions=lowercase__      ,   cls_index=lowercase__      ,   is_impossible=lowercase__      ,   p_mask=lowercase__      ,   )

        lowerCAmelCase__					     = model(
            lowercase__      ,   start_positions=lowercase__      ,   end_positions=lowercase__      ,   cls_index=lowercase__      ,   is_impossible=lowercase__      ,   )

        ((lowerCAmelCase__)     , )					     = result_with_labels.to_tuple()

        lowerCAmelCase__					     = model(lowercase__      ,   start_positions=lowercase__      ,   end_positions=lowercase__)

        ((lowerCAmelCase__)     , )					     = 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 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Tuple      ,   lowercase__ :  List[str]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Dict      ,   lowercase__ :  Any      ,   lowercase__ :  List[Any]      ,   lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = FlaubertForSequenceClassification(lowercase__)
        model.to(lowercase__)
        model.eval()

        lowerCAmelCase__					     = model(lowercase__)
        lowerCAmelCase__					     = 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 		__snake_case						(      self :  str      ,   lowercase__ :  Tuple      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  List[Any]      ,   lowercase__ :  int      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Any      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  str      ,   lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = self.num_labels
        lowerCAmelCase__					     = FlaubertForTokenClassification(lowercase__)
        model.to(lowercase__)
        model.eval()

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




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  List[str]      ,   lowercase__ :  List[str]      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = self.num_choices
        lowerCAmelCase__					     = FlaubertForMultipleChoice(config=lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__					     = input_ids.unsqueeze(1).expand(-1      ,   self.num_choices      ,   -1).contiguous()
        lowerCAmelCase__					     = token_type_ids.unsqueeze(1).expand(-1      ,   self.num_choices      ,   -1).contiguous()
        lowerCAmelCase__					     = input_mask.unsqueeze(1).expand(-1      ,   self.num_choices      ,   -1).contiguous()
        lowerCAmelCase__					     = model(
            lowercase__      ,   attention_mask=lowercase__      ,   token_type_ids=lowercase__      ,   labels=lowercase__      ,   )
        self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_choices))




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase__
        )     , (
            lowerCAmelCase__
        )     , (
            lowerCAmelCase__
        )     , (
            lowerCAmelCase__
        )     , (
            lowerCAmelCase__
        )     , (
            lowerCAmelCase__
        )     , (
            lowerCAmelCase__
        )     , (
            lowerCAmelCase__
        )     , (
            lowerCAmelCase__
        )     , 
        )					     = config_and_inputs
        lowerCAmelCase__					     = {
            'input_ids': input_ids,
            'token_type_ids': token_type_ids,
            'lengths': input_lengths,
            'attention_mask': input_mask,
        }
        return config, inputs_dict






@require_torch
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE	,					unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_     			= (
     (
         FlaubertModel,
         FlaubertWithLMHeadModel,
         FlaubertForQuestionAnswering,
         FlaubertForQuestionAnsweringSimple,
         FlaubertForSequenceClassification,
         FlaubertForTokenClassification,
         FlaubertForMultipleChoice,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase_     			= (
     {
         'feature-extraction': FlaubertModel,
         'fill-mask': FlaubertWithLMHeadModel,
         'question-answering': FlaubertForQuestionAnsweringSimple,
         'text-classification': FlaubertForSequenceClassification,
         'token-classification': FlaubertForTokenClassification,
         'zero-shot': FlaubertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  Optional[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 		__snake_case						(      self :  str      ,   lowercase__ :  str      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  int=False):







        '''simple docstring'''



        lowerCAmelCase__					     = super()._prepare_for_class(lowercase__      ,   lowercase__      ,   return_labels=lowercase__)

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

        return inputs_dict




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = FlaubertModelTester(self)
        lowerCAmelCase__					     = ConfigTester(self      ,   config_class=lowercase__      ,   emb_dim=37)




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        self.config_tester.run_common_tests()




 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*lowercase__)




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*lowercase__)




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_simple_qa(*lowercase__)




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*lowercase__)




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase__)




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_token_classif(*lowercase__)




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_multiple_choice(*lowercase__)




 @slow
 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowerCAmelCase__					     = FlaubertModel.from_pretrained(lowercase__)
               self.assertIsNotNone(lowercase__)




 @slow
 @require_torch_gpu
 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = 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

               lowerCAmelCase__					     = True
               lowerCAmelCase__					     = model_class(config=lowercase__)

               lowerCAmelCase__					     = self._prepare_for_class(lowercase__      ,   lowercase__)
               lowerCAmelCase__					     = 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'))
                      lowerCAmelCase__					     = 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     a_			(						unittest.TestCase						):






 '''simple docstring'''






 @slow
 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = FlaubertModel.from_pretrained('flaubert/flaubert_base_cased')
        lowerCAmelCase__					     = torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]])
        with torch.no_grad():
               lowerCAmelCase__					     = model(lowercase__)[0]
        lowerCAmelCase__					     = torch.Size((1, 11, 768))
        self.assertEqual(output.shape      ,   lowercase__)
        lowerCAmelCase__					     = torch.tensor(
            [[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]])

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

def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = len(lowerCAmelCase__     )
       for i in range(lowerCAmelCase__     ):
              for j in range(i + 1     ,  lowerCAmelCase__     ):
                     if numbers[j] < numbers[i]:
                            lowerCAmelCase__     , lowerCAmelCase__					     = numbers[j], numbers[i]
       return numbers


if __name__ == "__main__":
    lowerCAmelCase__     						=			input('Enter numbers separated by a comma:\n').strip()
    lowerCAmelCase__     						=			[int(item) for item in user_input.split(',')]
    print(exchange_sort(unsorted))
