


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, 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
    import tensorflow as tf

    from transformers import (
        TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertConfig,
        DPRConfig,
        TFDPRContextEncoder,
        TFDPRQuestionEncoder,
        TFDPRReader,
    )


class     lowercase__  :






  def __init__(					self      :					List[str]		,						_lowercase      :					int		,						_lowercase      :					Any=13		,						_lowercase      :					List[str]=7		,						_lowercase      :					List[Any]=True		,						_lowercase      :					Tuple=True		,						_lowercase      :					Union[str, Any]=True		,						_lowercase      :					List[Any]=True		,						_lowercase      :					Dict=99		,						_lowercase      :					List[Any]=32		,						_lowercase      :					int=2		,						_lowercase      :					Union[str, Any]=4		,						_lowercase      :					Dict=37		,						_lowercase      :					int="gelu"		,						_lowercase      :					int=0.1		,						_lowercase      :					Optional[Any]=0.1		,						_lowercase      :					str=5_12		,						_lowercase      :					Any=16		,						_lowercase      :					Dict=2		,						_lowercase      :					Any=0.0_2		,						_lowercase      :					Optional[int]=3		,						_lowercase      :					Any=4		,						_lowercase      :					Dict=None		,						_lowercase      :					str=0		,						):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     batch_size
         UpperCAmelCase__										=     seq_length
         UpperCAmelCase__										=     is_training
         UpperCAmelCase__										=     use_input_mask
         UpperCAmelCase__										=     use_token_type_ids
         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__										=     type_vocab_size
         UpperCAmelCase__										=     type_sequence_label_size
         UpperCAmelCase__										=     initializer_range
         UpperCAmelCase__										=     num_labels
         UpperCAmelCase__										=     num_choices
         UpperCAmelCase__										=     scope
         UpperCAmelCase__										=     projection_dim






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







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

         UpperCAmelCase__										=     None
         if self.use_input_mask:
                # follow test_modeling_tf_ctrl.py
                UpperCAmelCase__										=     random_attention_mask([self.batch_size, self.seq_length] )

         UpperCAmelCase__										=     None
         if self.use_token_type_ids:
                UpperCAmelCase__										=     ids_tensor([self.batch_size, self.seq_length]		,						self.type_vocab_size )

         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]		,						self.num_choices )

         UpperCAmelCase__										=     BertConfig(
             vocab_size=self.vocab_size		,						hidden_size=self.hidden_size		,						num_hidden_layers=self.num_hidden_layers		,						num_attention_heads=self.num_attention_heads		,						intermediate_size=self.intermediate_size		,						hidden_act=self.hidden_act		,						hidden_dropout_prob=self.hidden_dropout_prob		,						attention_probs_dropout_prob=self.attention_probs_dropout_prob		,						max_position_embeddings=self.max_position_embeddings		,						type_vocab_size=self.type_vocab_size		,						is_decoder=_lowercase		,						initializer_range=self.initializer_range		,						)
         UpperCAmelCase__										=     DPRConfig(projection_dim=self.projection_dim		,						**config.to_dict() )

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					Dict		,						_lowercase      :					Tuple		,						_lowercase      :					List[str]		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					List[str]		,						_lowercase      :					int		,						_lowercase      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     TFDPRContextEncoder(config=_lowercase )
         UpperCAmelCase__										=     model(_lowercase		,						attention_mask=_lowercase		,						token_type_ids=_lowercase )
         UpperCAmelCase__										=     model(_lowercase		,						token_type_ids=_lowercase )
         UpperCAmelCase__										=     model(_lowercase )
         self.parent.assertEqual(result.pooler_output.shape		,						(self.batch_size, self.projection_dim or self.hidden_size) )






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					Tuple		,						_lowercase      :					int		,						_lowercase      :					int		,						_lowercase      :					List[Any]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					int		,						_lowercase      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     TFDPRQuestionEncoder(config=_lowercase )
         UpperCAmelCase__										=     model(_lowercase		,						attention_mask=_lowercase		,						token_type_ids=_lowercase )
         UpperCAmelCase__										=     model(_lowercase		,						token_type_ids=_lowercase )
         UpperCAmelCase__										=     model(_lowercase )
         self.parent.assertEqual(result.pooler_output.shape		,						(self.batch_size, self.projection_dim or self.hidden_size) )






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Dict		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Dict		,						_lowercase      :					Any		,						_lowercase      :					str		,						_lowercase      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     TFDPRReader(config=_lowercase )
         UpperCAmelCase__										=     model(_lowercase		,						attention_mask=_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) )
         self.parent.assertEqual(result.relevance_logits.shape		,						(self.batch_size,) )






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase__			
         )						,			(
             UpperCAmelCase__			
         )						,			(
             UpperCAmelCase__			
         )						,			(
             UpperCAmelCase__			
         )						,			(
             UpperCAmelCase__			
         )						,			(
             UpperCAmelCase__			
         )						,			(
             UpperCAmelCase__			
         )						,						
         )										=     config_and_inputs
         UpperCAmelCase__										=     {"input_ids": input_ids}
         return config, inputs_dict




@require_tf
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  (
      (
          TFDPRContextEncoder,
          TFDPRQuestionEncoder,
          TFDPRReader,
      )
      if is_tf_available()
      else ()
  )
  A__=  {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

  A__=  False
  A__=  False
  A__=  False
  A__=  False
  A__=  False






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     TFDPRModelTester(self )
         UpperCAmelCase__										=     ConfigTester(self		,						config_class=_lowercase		,						hidden_size=37 )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         self.config_tester.run_common_tests()






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_context_encoder(*_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_question_encoder(*_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_reader(*_lowercase )






  @slow
  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__										=     TFDPRContextEncoder.from_pretrained(_lowercase )
                self.assertIsNotNone(_lowercase )

         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__										=     TFDPRContextEncoder.from_pretrained(_lowercase )
                self.assertIsNotNone(_lowercase )

         for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__										=     TFDPRQuestionEncoder.from_pretrained(_lowercase )
                self.assertIsNotNone(_lowercase )

         for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__										=     TFDPRReader.from_pretrained(_lowercase )
                self.assertIsNotNone(_lowercase )




@require_tf
class     lowercase__  (					unittest.TestCase		):






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




         """simple docstring"""







         UpperCAmelCase__										=     TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base" )

         UpperCAmelCase__										=     tf.constant(
             [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]] )  # [CLS] hello, is my dog cute? [SEP]
         UpperCAmelCase__										=     model(_lowercase )[0]  # embedding shape = (1, 768)
         # compare the actual values for a slice.
         UpperCAmelCase__										=     tf.constant(
             [
                 [
                     0.0_3_2_3_6_2_5_3,
                     0.1_2_7_5_3_3_3_5,
                     0.1_6_8_1_8_5_0_9,
                     0.0_0_2_7_9_7_8_6,
                     0.3_8_9_6_9_3_3,
                     0.2_4_2_6_4_9_4_5,
                     0.2_1_7_8_9_7_1,
                     -0.0_2_3_3_5_2_2_7,
                     -0.0_8_4_8_1_9_5_9,
                     -0.1_4_3_2_4_1_1_7,
                 ]
             ] )
         self.assertTrue(numpy.allclose(output[:, :10].numpy()		,						expected_slice.numpy()		,						atol=1E-4 ) )









def __UpperCAmelCase       (		__A			)						->					list[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     len(__A			)
       for i in range(__A			):
              for j in range(i + 1 ,						__A			):
                     if numbers[j] < numbers[i]:
                            UpperCAmelCase__						,			UpperCAmelCase__										=     numbers[j], numbers[i]
       return numbers


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





