"""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.
_a					:					Optional[Any]=			{"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class 		UpperCamelCase      (   unittest.TestCase						):
				UpperCAmelCase    :				Optional[int]   =		MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				UpperCAmelCase    :				str   =		TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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



				@require_torch
				def  _lowercase					(self    :				Any)   ->     Dict:
					__snake_case      :							List[Any]						=			pipeline(
					    task='text-classification'     ,  model='hf-internal-testing/tiny-random-distilbert'     ,  framework='pt')

					__snake_case      :							Optional[int]						=			text_classifier('This is great !')
					self.assertEqual(nested_simplify(_A)     ,  [{'label': 'LABEL_0', 'score': 0.504}])

					__snake_case      :							Optional[int]						=			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}])

					__snake_case      :							Dict						=			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}],
					    ]     ,  )

					__snake_case      :							List[str]						=			text_classifier('This is great !'     ,  top_k=1)
					self.assertEqual(nested_simplify(_A)     ,  [{'label': 'LABEL_0', 'score': 0.504}])

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

					__snake_case      :							List[Any]						=			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}]])

					__snake_case      :							Optional[Any]						=			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}],
					    ]     ,  )

					__snake_case      :							Any						=			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  _lowercase					(self    :				Any)   ->     int:
					import torch

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

					__snake_case      :							List[str]						=			text_classifier('This is great !')
					self.assertEqual(nested_simplify(_A)     ,  [{'label': 'LABEL_0', 'score': 0.504}])



				@require_tf
				def  _lowercase					(self    :				Union[str, Any])   ->     List[str]:
					__snake_case      :							Tuple						=			pipeline(
					    task='text-classification'     ,  model='hf-internal-testing/tiny-random-distilbert'     ,  framework='tf')

					__snake_case      :							Dict						=			text_classifier('This is great !')
					self.assertEqual(nested_simplify(_A)     ,  [{'label': 'LABEL_0', 'score': 0.504}])



				@slow
				@require_torch
				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					__snake_case      :							str						=			pipeline('text-classification')

					__snake_case      :							str						=			text_classifier('This is great !')
					self.assertEqual(nested_simplify(_A)     ,  [{'label': 'POSITIVE', 'score': 1.0}])
					__snake_case      :							Union[str, Any]						=			text_classifier('This is bad !')
					self.assertEqual(nested_simplify(_A)     ,  [{'label': 'NEGATIVE', 'score': 1.0}])
					__snake_case      :							Union[str, Any]						=			text_classifier('Birds are a type of animal')
					self.assertEqual(nested_simplify(_A)     ,  [{'label': 'POSITIVE', 'score': 0.988}])



				@slow
				@require_tf
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					__snake_case      :							Tuple						=			pipeline('text-classification'     ,  framework='tf')

					__snake_case      :							Dict						=			text_classifier('This is great !')
					self.assertEqual(nested_simplify(_A)     ,  [{'label': 'POSITIVE', 'score': 1.0}])
					__snake_case      :							str						=			text_classifier('This is bad !')
					self.assertEqual(nested_simplify(_A)     ,  [{'label': 'NEGATIVE', 'score': 1.0}])
					__snake_case      :							Optional[int]						=			text_classifier('Birds are a type of animal')
					self.assertEqual(nested_simplify(_A)     ,  [{'label': 'POSITIVE', 'score': 0.988}])



				def  _lowercase					(self    :				Tuple     ,  _A    :				List[Any]     ,  _A    :				Dict     ,  _A    :				Optional[int])   ->     Any:
					__snake_case      :							List[Any]						=			TextClassificationPipeline(model=_A     ,  tokenizer=_A)
					return text_classifier, ["HuggingFace is in", "This is another test"]






				def  _lowercase					(self    :				str     ,  _A    :				List[str]     ,  _A    :				Dict)   ->     List[Any]:
					__snake_case      :							Tuple						=			text_classifier.model
					# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
					__snake_case      :							str						=			'HuggingFace is in'
					__snake_case      :							Optional[Any]						=			text_classifier(_A)

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

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

					__snake_case      :							Union[str, Any]						=			{'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
					__snake_case      :							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.
					__snake_case      :							Optional[Any]						=			[['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
					__snake_case      :							List[str]						=			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())

"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


_a					:					Tuple=			logging.get_logger(__name__)

class 		UpperCamelCase      (   lowercase						):



				def __init__(self    :				int     ,  *_A    :				str     ,  **_A    :				List[str])   ->     None:
					warnings.warn(
					    'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
					    ' Please use VideoMAEImageProcessor instead.'     ,  _A     ,  )
					super().__init__(*_A     ,  **_A)





