






"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_    =							{
    '''configuration_poolformer''': [
        '''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''PoolFormerConfig''',
        '''PoolFormerOnnxConfig''',
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							['''PoolFormerFeatureExtractor''']
						A_    =							['''PoolFormerImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							[
						    '''POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''PoolFormerForImageClassification''',
						    '''PoolFormerModel''',
						    '''PoolFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						A_    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)














"""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_    =							{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}






@is_pipeline_test
class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):
							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 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Tuple		  =  pipeline(
													    task="""text-classification"""			,    model="""hf-internal-testing/tiny-random-distilbert"""			,    framework="""pt"""							)

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

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

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

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

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

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

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

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


							@require_torch
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													import torch

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

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


							@require_tf
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													A__						:  str		  =  pipeline(
													    task="""text-classification"""			,    model="""hf-internal-testing/tiny-random-distilbert"""			,    framework="""tf"""							)

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


							@slow
							@require_torch
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  pipeline("""text-classification"""							)

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


							@slow
							@require_tf
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  pipeline("""text-classification"""			,    framework="""tf"""							)

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


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Any			,    snake_case      :						List[Any]			,    snake_case      :						Any							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  TextClassificationPipeline(model=snake_case			,    tokenizer=snake_case							)
													return text_classifier, ["HuggingFace is in", "This is another test"]


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						Union[str, Any]			,    snake_case      :						Dict							):
													'''simple docstring'''





													A__						:  List[str]		  =  text_classifier.model
													# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
													A__						:  int		  =  """HuggingFace is in"""
													A__						:  List[Any]		  =  text_classifier(snake_case							)

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

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

													A__						:  Optional[Any]		  =  {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
													A__						:  Union[str, Any]		  =  text_classifier(snake_case							)
													self.assertEqual(
													    nested_simplify(snake_case							)			,    {"""label""": ANY(snake_case							), """score""": ANY(snake_case							)}			,    )
													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__						:  Tuple		  =  [["""HuggingFace is in """, """Paris is in France"""]]
													with self.assertRaises(snake_case							):
																			text_classifier(snake_case							)

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






