





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	:	Tuple		     =       {"""LayoutLMv2Config""", """LayoutLMv3Config"""}







@is_pipeline_test
class 				__lowerCAmelCase       (    unittest.TestCase):
      _lowercase				:					Dict						=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _lowercase				:					int						=			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				:					Optional[Any]						=			{
                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__	:					Union[str, Any]										=pipeline(
            task="text-classification"		,      model="hf-internal-testing/tiny-random-distilbert"		,      framework="pt"							)

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

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

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

        a__	:					Any										=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__	:					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__	:					List[Any]										=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							)   ->						Dict:



        '''simple docstring'''




        import torch

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

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







      @require_tf
      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




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

        a__	:					List[str]										=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__	:					Optional[Any]										=pipeline("text-classification"							)

        a__	:					Dict										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "POSITIVE", "score": 1.0}]							)
        a__	:					Tuple										=text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "NEGATIVE", "score": 1.0}]							)
        a__	:					Union[str, Any]										=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							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=pipeline("text-classification"		,      framework="tf"							)

        a__	:					str										=text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "POSITIVE", "score": 1.0}]							)
        a__	:					List[str]										=text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,      [{"label": "NEGATIVE", "score": 1.0}]							)
        a__	:					List[str]										=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__							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					Optional[int]										=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__	:					int										=text_classifier.model
        # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
        a__	:					Optional[Any]										="HuggingFace is in"
        a__	:					List[str]										=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__	:					List[str]										=text_classifier(lowerCAmelCase__		,      top_k=lowerCAmelCase__							)
        a__	:					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__	:					Optional[int]										={"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
        a__	:					str										=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__	:					List[str]										=[["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__	:					Dict										=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()							)







UpperCamelCase  							=						[0, 2, 4, 6, 8]
UpperCamelCase  							=						[1, 3, 5, 7, 9]

def 			lowercase_				(			_lowerCamelCase							:       int		,	_lowerCamelCase							:       int		,	_lowerCamelCase							:       list[int]		,	_lowerCamelCase							:       int):
    if remaining_length == 0:
        if digits[0] == 0 or digits[-1] == 0:
            return 0

        for i in range(length // 2 - 1		,	-1		,	-1):
            remainder += digits[i] + digits[length - i - 1]

            if remainder % 2 == 0:
                return 0

            remainder //= 10

        return 1

    if remaining_length == 1:
        if remainder % 2 == 0:
            return 0

        lowercase__ :   str    					=				0
        for digit in range(10):
            lowercase__ :   str    					=				digit
            result += reversible_numbers(
                0		,	(remainder + 2 * digit) // 10		,	_lowerCamelCase		,	_lowerCamelCase)
        return result

    lowercase__ :   Dict    					=				0
    for digita in range(10):
        lowercase__ :   int    					=				digita

        if (remainder + digita) % 2 == 0:
            lowercase__ :   Optional[Any]    					=				ODD_DIGITS
        else:
            lowercase__ :   str    					=				EVEN_DIGITS

        for digita in other_parity_digits:
            lowercase__ :   List[str]    					=				digita
            result += reversible_numbers(
                remaining_length - 2		,	(remainder + digita + digita) // 10		,	_lowerCamelCase		,	_lowerCamelCase		,	)
    return result

def 			lowercase_				(			_lowerCamelCase							:       int = 9):
    lowercase__ :   Tuple    					=				0
    for length in range(1		,	max_power + 1):
        result += reversible_numbers(_lowerCamelCase		,	0		,	[0] * length		,	_lowerCamelCase)
    return result


if __name__ == "__main__":
      print(f"{solution() = }")



