





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


@is_pipeline_test
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    UpperCamelCase     = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


    @require_torch
    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			pipeline(
            task="""text-classification""" ,		model="""hf-internal-testing/tiny-random-distilbert""" ,		framework="""pt"""       )

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

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

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

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

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

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

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

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


    @require_torch
    def 			snake_case_(		self       )		->   int:
        import torch

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

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


    @require_tf
    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			pipeline(
            task="""text-classification""" ,		model="""hf-internal-testing/tiny-random-distilbert""" ,		framework="""tf"""       )

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


    @slow
    @require_torch
    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			pipeline("""text-classification"""       )

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


    @slow
    @require_tf
    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			pipeline("""text-classification""" ,		framework="""tf"""       )

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


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			TextClassificationPipeline(model=A ,		tokenizer=A       )
        return text_classifier, ["HuggingFace is in", "This is another test"]





    def 			snake_case_(		self ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			text_classifier.model
        # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
        _SCREAMING_SNAKE_CASE									=			"""HuggingFace is in"""
        _SCREAMING_SNAKE_CASE									=			text_classifier(A       )

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

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

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










def   a_    ( lowerCAmelCase_      :			str,	lowerCAmelCase_      :			int	):
   return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase_	) - ngram_size + 1	)]


if __name__ == "__main__":
     from doctest import testmod

     testmod()

