


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.
lowercase_	   =     {'LayoutLMv2Config', 'LayoutLMv3Config'}


@is_pipeline_test
class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      __snake_case 	=       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      __snake_case 	=       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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




      @require_torch
      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							Any		 =						pipeline(
           task='text-classification'						,				model='hf-internal-testing/tiny-random-distilbert'						,				framework='pt'    )

       __lowerCamelCase		:							List[str]		 =						text_classifier('This is great !'    )
       self.assertEqual(nested_simplify(a    )						,				[{'label': 'LABEL_0', 'score': 0.5_0_4}]    )

       __lowerCamelCase		:							Optional[int]		 =						text_classifier('This is great !'						,				top_k=2    )
       self.assertEqual(
           nested_simplify(a    )						,				[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}]    )

       __lowerCamelCase		:							Dict		 =						text_classifier(['This is great !', 'This is bad']						,				top_k=2    )
       self.assertEqual(
           nested_simplify(a    )						,				[
               [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
               [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
           ]						,				)

       __lowerCamelCase		:							Optional[int]		 =						text_classifier('This is great !'						,				top_k=1    )
       self.assertEqual(nested_simplify(a    )						,				[{'label': 'LABEL_0', 'score': 0.5_0_4}]    )

       # Legacy behavior
       __lowerCamelCase		:							List[Any]		 =						text_classifier('This is great !'						,				return_all_scores=a    )
       self.assertEqual(nested_simplify(a    )						,				[{'label': 'LABEL_0', 'score': 0.5_0_4}]    )

       __lowerCamelCase		:							Optional[Any]		 =						text_classifier('This is great !'						,				return_all_scores=a    )
       self.assertEqual(
           nested_simplify(a    )						,				[[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}]]    )

       __lowerCamelCase		:							Any		 =						text_classifier(['This is great !', 'Something else']						,				return_all_scores=a    )
       self.assertEqual(
           nested_simplify(a    )						,				[
               [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
               [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
           ]						,				)

       __lowerCamelCase		:							Tuple		 =						text_classifier(['This is great !', 'Something else']						,				return_all_scores=a    )
       self.assertEqual(
           nested_simplify(a    )						,				[
               {'label': 'LABEL_0', 'score': 0.5_0_4},
               {'label': 'LABEL_0', 'score': 0.5_0_4},
           ]						,				)




      @require_torch
      def 					_snake_case     (    self:	Dict    ):
       import torch

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

       __lowerCamelCase		:							str		 =						text_classifier('This is great !'    )
       self.assertEqual(nested_simplify(a    )						,				[{'label': 'LABEL_0', 'score': 0.5_0_4}]    )




      @require_tf
      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							str		 =						pipeline(
           task='text-classification'						,				model='hf-internal-testing/tiny-random-distilbert'						,				framework='tf'    )

       __lowerCamelCase		:							List[str]		 =						text_classifier('This is great !'    )
       self.assertEqual(nested_simplify(a    )						,				[{'label': 'LABEL_0', 'score': 0.5_0_4}]    )




      @slow
      @require_torch
      def 					_snake_case     (    self:	int    ):
       __lowerCamelCase		:							List[str]		 =						pipeline('text-classification'    )

       __lowerCamelCase		:							List[Any]		 =						text_classifier('This is great !'    )
       self.assertEqual(nested_simplify(a    )						,				[{'label': 'POSITIVE', 'score': 1.0}]    )
       __lowerCamelCase		:							int		 =						text_classifier('This is bad !'    )
       self.assertEqual(nested_simplify(a    )						,				[{'label': 'NEGATIVE', 'score': 1.0}]    )
       __lowerCamelCase		:							int		 =						text_classifier('Birds are a type of animal'    )
       self.assertEqual(nested_simplify(a    )						,				[{'label': 'POSITIVE', 'score': 0.9_8_8}]    )




      @slow
      @require_tf
      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							Dict		 =						pipeline('text-classification'						,				framework='tf'    )

       __lowerCamelCase		:							Optional[Any]		 =						text_classifier('This is great !'    )
       self.assertEqual(nested_simplify(a    )						,				[{'label': 'POSITIVE', 'score': 1.0}]    )
       __lowerCamelCase		:							str		 =						text_classifier('This is bad !'    )
       self.assertEqual(nested_simplify(a    )						,				[{'label': 'NEGATIVE', 'score': 1.0}]    )
       __lowerCamelCase		:							int		 =						text_classifier('Birds are a type of animal'    )
       self.assertEqual(nested_simplify(a    )						,				[{'label': 'POSITIVE', 'score': 0.9_8_8}]    )




      def 					_snake_case     (    self:	Optional[Any]						,				a:	str						,				a:	List[Any]						,				a:	List[str]    ):
       __lowerCamelCase		:							str		 =						TextClassificationPipeline(model=a						,				tokenizer=a    )
       return text_classifier, ["HuggingFace is in", "This is another test"]




      def 					_snake_case     (    self:	List[str]						,				a:	str						,				a:	Optional[int]    ):
       __lowerCamelCase		:							Any		 =						text_classifier.model
       # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
       __lowerCamelCase		:							int		 =						'HuggingFace is in'
       __lowerCamelCase		:							int		 =						text_classifier(a    )

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

       __lowerCamelCase		:							Optional[Any]		 =						['HuggingFace is in ', 'Paris is in France']
       __lowerCamelCase		:							Optional[Any]		 =						text_classifier(a    )
       self.assertEqual(
           nested_simplify(a    )						,				[{'label': ANY(a    ), 'score': ANY(a    )}, {'label': ANY(a    ), 'score': ANY(a    )}]						,				)
       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		:							Union[str, Any]		 =						text_classifier(a						,				top_k=a    )
       __lowerCamelCase		:							Optional[int]		 =						len(model.config.idalabel.values()    )
       self.assertEqual(
           nested_simplify(a    )						,				[[{'label': ANY(a    ), 'score': ANY(a    )}] * N, [{'label': ANY(a    ), 'score': ANY(a    )}] * N]						,				)

       __lowerCamelCase		:							int		 =						{'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
       __lowerCamelCase		:							Optional[Any]		 =						text_classifier(a    )
       self.assertEqual(
           nested_simplify(a    )						,				{'label': ANY(a    ), 'score': ANY(a    )}						,				)
       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		:							Optional[int]		 =						[['HuggingFace is in ', 'Paris is in France']]
       with self.assertRaises(a    ):
        text_classifier(a    )

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





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						prime_factors(SCREAMING_SNAKE_CASE__						)
 if is_square_free(SCREAMING_SNAKE_CASE__						):
  return -1 if len(SCREAMING_SNAKE_CASE__						) % 2 else 1
 return 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()

