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 		_lowerCamelCase		:
    def __init__(  self , lowerCAmelCase , lowerCAmelCase=13 , lowerCAmelCase=7 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=99 , lowerCAmelCase=32 , lowerCAmelCase=2 , lowerCAmelCase=4 , lowerCAmelCase=37 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=512 , lowerCAmelCase=16 , lowerCAmelCase=2 , lowerCAmelCase=0.02 , lowerCAmelCase=3 , lowerCAmelCase=4 , lowerCAmelCase=None , lowerCAmelCase=0 , )       ->					int:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      parent
     SCREAMING_SNAKE_CASE__:   Dict=      batch_size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      seq_length
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      is_training
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      use_input_mask
     SCREAMING_SNAKE_CASE__:   List[str]=      use_token_type_ids
     SCREAMING_SNAKE_CASE__:   Tuple=      use_labels
     SCREAMING_SNAKE_CASE__:   int=      vocab_size
     SCREAMING_SNAKE_CASE__:   List[Any]=      hidden_size
     SCREAMING_SNAKE_CASE__:   Tuple=      num_hidden_layers
     SCREAMING_SNAKE_CASE__:   Any=      num_attention_heads
     SCREAMING_SNAKE_CASE__:   int=      intermediate_size
     SCREAMING_SNAKE_CASE__:   int=      hidden_act
     SCREAMING_SNAKE_CASE__:   Optional[int]=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__:   Optional[int]=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__:   List[Any]=      max_position_embeddings
     SCREAMING_SNAKE_CASE__:   Tuple=      type_vocab_size
     SCREAMING_SNAKE_CASE__:   List[Any]=      type_sequence_label_size
     SCREAMING_SNAKE_CASE__:   List[str]=      initializer_range
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      num_labels
     SCREAMING_SNAKE_CASE__:   Any=      num_choices
     SCREAMING_SNAKE_CASE__:   Optional[int]=      scope
     SCREAMING_SNAKE_CASE__:   Any=      projection_dim
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   str=      ids_tensor([self.batch_size, self.seq_length] , self.vocab_size   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      None
     if self.use_input_mask:
      # follow test_modeling_tf_ctrl.py
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      random_attention_mask([self.batch_size, self.seq_length]   )

     SCREAMING_SNAKE_CASE__:   Dict=      None
     if self.use_token_type_ids:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size   )

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      None
     SCREAMING_SNAKE_CASE__:   Tuple=      None
     SCREAMING_SNAKE_CASE__:   Any=      None
     if self.use_labels:
      SCREAMING_SNAKE_CASE__:   Dict=      ids_tensor([self.batch_size] , self.type_sequence_label_size   )
      SCREAMING_SNAKE_CASE__:   Any=      ids_tensor([self.batch_size, self.seq_length] , self.num_labels   )
      SCREAMING_SNAKE_CASE__:   Dict=      ids_tensor([self.batch_size] , self.num_choices   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      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=lowerCAmelCase , initializer_range=self.initializer_range , )
     SCREAMING_SNAKE_CASE__:   int=      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 , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   str=      TFDPRContextEncoder(config=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      model(lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      model(lowerCAmelCase , token_type_ids=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      model(lowerCAmelCase   )
     self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   int=      TFDPRQuestionEncoder(config=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      model(lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      model(lowerCAmelCase , token_type_ids=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Tuple=      model(lowerCAmelCase   )
     self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size)   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   List[str]=      TFDPRReader(config=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Tuple=      model(lowerCAmelCase , attention_mask=lowerCAmelCase   )

     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   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   str=      self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	
     ):   Tuple=      config_and_inputs
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      {'''input_ids''': input_ids}
     return config, inputs_dict






@require_tf
class 		_lowerCamelCase		(   UpperCamelCase_    ,     UpperCamelCase_    ,     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:
     SCREAMING_SNAKE_CASE__:   Tuple=      TFDPRModelTester(self   )
     SCREAMING_SNAKE_CASE__:   List[str]=      ConfigTester(self , config_class=lowerCAmelCase , hidden_size=37   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     self.config_tester.run_common_tests()
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   str=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_dpr_context_encoder(*lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   int=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_dpr_question_encoder(*lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_dpr_reader(*lowerCAmelCase   )



    @slow
    def 						UpperCamelCase_				(  self   )       ->					str:
     for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE__:   List[str]=      TFDPRContextEncoder.from_pretrained(lowerCAmelCase   )
      self.assertIsNotNone(lowerCAmelCase   )

     for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE__:   Dict=      TFDPRContextEncoder.from_pretrained(lowerCAmelCase   )
      self.assertIsNotNone(lowerCAmelCase   )

     for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE__:   List[str]=      TFDPRQuestionEncoder.from_pretrained(lowerCAmelCase   )
      self.assertIsNotNone(lowerCAmelCase   )

     for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      TFDPRReader.from_pretrained(lowerCAmelCase   )
      self.assertIsNotNone(lowerCAmelCase   )






@require_tf
class 		_lowerCamelCase		(   unittest.TestCase    ):
    @slow
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      tf.constant(
         [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]   )  # [CLS] hello, is my dog cute? [SEP]
     SCREAMING_SNAKE_CASE__:   Tuple=      model(lowerCAmelCase   )[0]  # embedding shape = (1, 768)
     # compare the actual values for a slice.
     SCREAMING_SNAKE_CASE__:   Any=      tf.constant(
         [
             [
                 0.03236253,
                 0.12753335,
                 0.16818509,
                 0.00279786,
                 0.3896933,
                 0.24264945,
                 0.2178971,
                 -0.02335227,
                 -0.08481959,
                 -0.14324117,
             ]
         ]   )
     self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4   )   )


def 			A__      (	snake_case_ :		list      ):

 if len(snake_case_      ) < 2:
  return collection

 def circle_sort_util(snake_case_ :		list       ,				snake_case_ :		int       ,				snake_case_ :		int      ) -> bool:
  SCREAMING_SNAKE_CASE__:   Dict=      False

  if low == high:
   return swapped

  SCREAMING_SNAKE_CASE__:   str=      low
  SCREAMING_SNAKE_CASE__:   Optional[int]=      high

  while left < right:
   if collection[left] > collection[right]:
    SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      (
        collection[right],
        collection[left],
    )
    SCREAMING_SNAKE_CASE__:   List[Any]=      True

   left += 1
   right -= 1

  if left == right and collection[left] > collection[right + 1]:
   SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   List[str]=      (
       collection[right + 1],
       collection[left],
   )

   SCREAMING_SNAKE_CASE__:   List[str]=      True

  SCREAMING_SNAKE_CASE__:   Dict=      low + int((high - low) / 2      )
  SCREAMING_SNAKE_CASE__:   str=      circle_sort_util(snake_case_       ,				snake_case_       ,				snake_case_      )
  SCREAMING_SNAKE_CASE__:   List[Any]=      circle_sort_util(snake_case_       ,				mid + 1       ,				snake_case_      )

  return swapped or left_swap or right_swap

 SCREAMING_SNAKE_CASE__:   Optional[int]=      True

 while is_not_sorted is True:
  SCREAMING_SNAKE_CASE__:   Tuple=      circle_sort_util(snake_case_       ,				0       ,				len(snake_case_      ) - 1      )

 return collection


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

