





'''simple docstring'''


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.
_SCREAMING_SNAKE_CASE	 =							{"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class    lowerCAmelCase_		(		unittest.TestCase		):
 __lowerCamelCase						:							Optional[Any]         =					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
 __lowerCamelCase						:							str         =					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

 if model_mapping is not None:
  __lowerCamelCase						:							Optional[int]         =					{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
 if tf_model_mapping is not None:
  __lowerCamelCase						:							List[Any]         =					{
      config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
  }




 @require_torch
 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="pt"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     _lowerCAmelCase				=      text_classifier("This is great !"			,     top_k=2		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]		)

     _lowerCAmelCase				=      text_classifier(["This is great !", "This is bad"]			,     top_k=2		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
         ]			,     )

     _lowerCAmelCase				=      text_classifier("This is great !"			,     top_k=1		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     # Legacy behavior
     _lowerCAmelCase				=      text_classifier("This is great !"			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     _lowerCAmelCase				=      text_classifier("This is great !"			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]		)

     _lowerCAmelCase				=      text_classifier(["This is great !", "Something else"]			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
         ]			,     )

     _lowerCAmelCase				=      text_classifier(["This is great !", "Something else"]			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             {"label": "LABEL_0", "score": 0.504},
             {"label": "LABEL_0", "score": 0.504},
         ]			,     )




 @require_torch
 def 							_snake_case   (	self		)    ->							Tuple:
     import torch

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

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)




 @require_tf
 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="tf"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)




 @slow
 @require_torch
 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      pipeline("text-classification"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("This is bad !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "NEGATIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("Birds are a type of animal"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 0.988}]		)




 @slow
 @require_tf
 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      pipeline("text-classification"			,     framework="tf"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("This is bad !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "NEGATIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("Birds are a type of animal"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 0.988}]		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      TextClassificationPipeline(model=_lowerCAmelCase			,     tokenizer=_lowerCAmelCase		)
     return text_classifier, ["HuggingFace is in", "This is another test"]





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      text_classifier.model
     # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
     _lowerCAmelCase				=      "HuggingFace is in"
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase		)

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

     _lowerCAmelCase				=      ["HuggingFace is in ", "Paris is in France"]
     _lowerCAmelCase				=      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
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase			,     top_k=_lowerCAmelCase		)
     _lowerCAmelCase				=      len(model.config.idalabel.values()		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [[{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}] * N, [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}] * N]			,     )

     _lowerCAmelCase				=      {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
     _lowerCAmelCase				=      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.
     _lowerCAmelCase				=      [["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
     _lowerCAmelCase				=      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()		)









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        raise TypeError("Input value must be an 'int' type"    )

    _lowerCAmelCase				=      0
    while number:
        position += 1
        number >>= 1

    return position


if __name__ == "__main__":
       import doctest

       doctest.testmod()


