



'''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   :			Any					  =    {"""LayoutLMv2Config""", """LayoutLMv3Config"""}

@is_pipeline_test
class 			UpperCamelCase_ (							unittest.TestCase			):
	lowercase          = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	lowercase          = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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





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

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

								UpperCAmelCase     :		Optional[Any]							     =		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}]    )

								UpperCAmelCase     :		str							     =		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}],
								    ]		,		)

								UpperCAmelCase     :		str							     =		text_classifier("""This is great !"""		,		top_k=1    )
								self.assertEqual(nested_simplify(A_    )		,		[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]    )

								# Legacy behavior
								UpperCAmelCase     :		str							     =		text_classifier("""This is great !"""		,		return_all_scores=A_    )
								self.assertEqual(nested_simplify(A_    )		,		[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]    )

								UpperCAmelCase     :		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}]]    )

								UpperCAmelCase     :		Optional[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}],
								    ]		,		)

								UpperCAmelCase     :		Union[str, 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_0""", """score""": 0.5_0_4},
								    ]		,		)





	@require_torch
	def  _lowercase(  self    )			->			Optional[Any]:
								import torch

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

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





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

								UpperCAmelCase     :		Tuple							     =		text_classifier("""This is great !"""    )
								self.assertEqual(nested_simplify(A_    )		,		[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]    )





	@slow
	@require_torch
	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		List[str]							     =		pipeline("""text-classification"""    )

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





	@slow
	@require_tf
	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		int							     =		pipeline("""text-classification"""		,		framework="""tf"""    )

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





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






	def  _lowercase(  self		,		A		,		A    )			->			str:
								UpperCAmelCase     :		Union[str, Any]							     =		text_classifier.model
								# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
								UpperCAmelCase     :		Optional[int]							     =		"HuggingFace is in"
								UpperCAmelCase     :		List[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()    )

								UpperCAmelCase     :		Union[str, Any]							     =		["HuggingFace is in ", "Paris is in France"]
								UpperCAmelCase     :		Optional[int]							     =		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
								UpperCAmelCase     :		List[str]							     =		text_classifier(A_		,		top_k=A_    )
								UpperCAmelCase     :		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]		,		)

								UpperCAmelCase     :		Optional[int]							     =		{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
								UpperCAmelCase     :		List[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.
								UpperCAmelCase     :		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
								UpperCAmelCase     :		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()    )








import functools



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 UpperCamelCase	:					Optional[int]       	=   len(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   len(_lowerCAmelCase     )

 @functools.cache
 def min_distance(_lowerCAmelCase							,   _lowerCAmelCase     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase	:					Union[str, Any]       	=   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1							,   _lowerCAmelCase     )							,   1 + min_distance(_lowerCAmelCase							,   indexa + 1     )							,   diff + min_distance(indexa + 1							,   indexa + 1     )							,   )

 return min_distance(0							,   0     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
