





from __future__ import annotations

import bisect
def 					_A	(		SCREAMING_SNAKE_CASE					:       list[int]	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       int = 0	,	SCREAMING_SNAKE_CASE					:       int = -1						):





  """simple docstring"""



  if hi < 0:
    a__     : List[Any]  =len(SCREAMING_SNAKE_CASE						)

  while lo < hi:
    a__     : Union[str, Any]  =lo + (hi - lo) // 2
    if sorted_collection[mid] < item:
      a__     : Union[str, Any]  =mid + 1
    else:
      a__     : List[str]  =mid

  return lo
def 					_A	(		SCREAMING_SNAKE_CASE					:       list[int]	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       int = 0	,	SCREAMING_SNAKE_CASE					:       int = -1						):





  """simple docstring"""



  if hi < 0:
    a__     : List[str]  =len(SCREAMING_SNAKE_CASE						)

  while lo < hi:
    a__     : str  =lo + (hi - lo) // 2
    if sorted_collection[mid] <= item:
      a__     : str  =mid + 1
    else:
      a__     : str  =mid

  return lo
def 					_A	(		SCREAMING_SNAKE_CASE					:       list[int]	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       int = 0	,	SCREAMING_SNAKE_CASE					:       int = -1						):





  """simple docstring"""



  sorted_collection.insert(bisect_left(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)	,	SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       list[int]	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       int = 0	,	SCREAMING_SNAKE_CASE					:       int = -1						):





  """simple docstring"""



  sorted_collection.insert(bisect_right(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)	,	SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       list[int]	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  a__     : Optional[Any]  =0
  a__     : str  =len(SCREAMING_SNAKE_CASE						) - 1

  while left <= right:
    a__     : Union[str, Any]  =left + (right - left) // 2
    a__     : int  =sorted_collection[midpoint]
    if current_item == item:
      return midpoint
    elif item < current_item:
      a__     : Optional[Any]  =midpoint - 1
    else:
      a__     : Dict  =midpoint + 1
  return None
def 					_A	(		SCREAMING_SNAKE_CASE					:       list[int]	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  a__     : Any  =bisect.bisect_left(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  if index != len(SCREAMING_SNAKE_CASE						) and sorted_collection[index] == item:
    return index
  return None
def 					_A	(		SCREAMING_SNAKE_CASE					:       list[int]	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if right < left:
    return None

  a__     : Optional[int]  =left + (right - left) // 2

  if sorted_collection[midpoint] == item:
    return midpoint
  elif sorted_collection[midpoint] > item:
    return binary_search_by_recursion(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	midpoint - 1						)
  else:
    return binary_search_by_recursion(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	midpoint + 1	,	SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
    UpperCAmelCase     :     Tuple							=	input("""Enter numbers separated by comma:\n""").strip()
    UpperCAmelCase     :     List[str]							=	sorted(int(item) for item in user_input.split(""","""))
    UpperCAmelCase     :     int							=	int(input("""Enter a single number to be found in the list:\n"""))
    UpperCAmelCase     :     str							=	binary_search(collection, target)
    if result is None:
        print(F"""{target} was not found in {collection}.""")
    else:
        print(F"""{target} was found at position {result} in {collection}.""")













import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase     :     List[Any]							=	{"""LayoutLMv2Config""", """LayoutLMv3Config"""}







@is_pipeline_test
class     __lowerCAmelCase	(   unittest.TestCase):
      _lowercase    : str            =      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _lowercase    : Tuple            =      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            _lowercase    : Dict            =      {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            _lowercase    : int            =      {
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }







      @require_torch
      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : str  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="pt"							)

        a__     : Any  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        a__     : Any  =text_classifier("This is great !"		,				top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]							)

        a__     : Tuple  =text_classifier(["This is great !", "This is bad"]		,				top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,				)

        a__     : List[Any]  =text_classifier("This is great !"		,				top_k=1							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        # Legacy behavior
        a__     : Any  =text_classifier("This is great !"		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        a__     : List[str]  =text_classifier("This is great !"		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]							)

        a__     : Optional[int]  =text_classifier(["This is great !", "Something else"]		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,				)

        a__     : int  =text_classifier(["This is great !", "Something else"]		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                {"label": "LABEL_0", "score": 0.5_04},
                {"label": "LABEL_0", "score": 0.5_04},
            ]		,				)







      @require_torch
      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        import torch

        a__     : Dict  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="pt"		,				device=torch.device("cpu"							)		,				)

        a__     : Optional[Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)







      @require_tf
      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Optional[int]  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="tf"							)

        a__     : Optional[Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)







      @slow
      @require_torch
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : Union[str, Any]  =pipeline("text-classification"							)

        a__     : Union[str, Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 1.0}]							)
        a__     : Optional[Any]  =text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "NEGATIVE", "score": 1.0}]							)
        a__     : Dict  =text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 0.9_88}]							)







      @slow
      @require_tf
      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Tuple  =pipeline("text-classification"		,				framework="tf"							)

        a__     : str  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 1.0}]							)
        a__     : str  =text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "NEGATIVE", "score": 1.0}]							)
        a__     : Dict  =text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 0.9_88}]							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : List[Any]  =TextClassificationPipeline(model=lowerCAmelCase__		,				tokenizer=lowerCAmelCase__							)
        return text_classifier, ["HuggingFace is in", "This is another test"]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




        a__     : Tuple  =text_classifier.model
        # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
        a__     : List[Any]  ="HuggingFace is in"
        a__     : int  =text_classifier(lowerCAmelCase__							)

        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]							)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)

        a__     : Optional[int]  =["HuggingFace is in ", "Paris is in France"]
        a__     : Optional[Any]  =text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}, {"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,				)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)
        self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()							)

        # Forcing to get all results with `top_k=None`
        # This is NOT the legacy format
        a__     : Union[str, Any]  =text_classifier(lowerCAmelCase__		,				top_k=lowerCAmelCase__							)
        a__     : Optional[Any]  =len(model.config.idalabel.values()							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N, [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N]		,				)

        a__     : List[str]  ={"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
        a__     : Optional[Any]  =text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}		,				)
        self.assertTrue(outputs["label"] in model.config.idalabel.values()							)

        # This might be used a text pair, but tokenizer + pipe interaction
        # makes it hard to understand that it's not using the pair properly
        # https://github.com/huggingface/transformers/issues/17305
        # We disabled this usage instead as it was outputting wrong outputs.
        a__     : Any  =[["HuggingFace is in ", "Paris is in France"]]
        with self.assertRaises(lowerCAmelCase__							):
          text_classifier(lowerCAmelCase__							)

        # This used to be valid for doing text pairs
        # We're keeping it working because of backward compatibility
        a__     : Optional[int]  =text_classifier([[["HuggingFace is in ", "Paris is in France"]]]							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,				)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)






