


'''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.
__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}






@is_pipeline_test
class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       List[str]   						=MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
						_UpperCAmelCase		:       str   						=TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

						if model_mapping is not None:
												_UpperCAmelCase		:       Optional[int]   						={config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
						if tf_model_mapping is not None:
												_UpperCAmelCase		:       List[str]   						={
												    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
												}






						@require_torch
						def 				_UpperCAmelCase       (  self							:      Any	):
													A_			    =		pipeline(
													    task="text-classification" ,  model="hf-internal-testing/tiny-random-distilbert" ,  framework="pt"	)

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

													A_			    =		text_classifier("This is great !" ,  top_k=2	)
													self.assertEqual(
													    nested_simplify(lowerCAmelCase	) ,  [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]	)

													A_			    =		text_classifier(["This is great !", "This is bad"] ,  top_k=2	)
													self.assertEqual(
													    nested_simplify(lowerCAmelCase	) ,  [
													        [{"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}],
													    ] ,  )

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

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

													A_			    =		text_classifier("This is great !" ,  return_all_scores=lowerCAmelCase	)
													self.assertEqual(
													    nested_simplify(lowerCAmelCase	) ,  [[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]]	)

													A_			    =		text_classifier(["This is great !", "Something else"] ,  return_all_scores=lowerCAmelCase	)
													self.assertEqual(
													    nested_simplify(lowerCAmelCase	) ,  [
													        [{"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}],
													    ] ,  )

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






						@require_torch
						def 				_UpperCAmelCase       (  self							:      int	):
													import torch

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

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






						@require_tf
						def 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		pipeline(
													    task="text-classification" ,  model="hf-internal-testing/tiny-random-distilbert" ,  framework="tf"	)

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






						@slow
						@require_torch
						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													A_			    =		pipeline("text-classification"	)

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






						@slow
						@require_tf
						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													A_			    =		pipeline("text-classification" ,  framework="tf"	)

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






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      Optional[Any]	):
													A_			    =		TextClassificationPipeline(model=lowerCAmelCase ,  tokenizer=lowerCAmelCase	)
													return text_classifier, ["HuggingFace is in", "This is another test"]






						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      Tuple	):
													A_			    =		text_classifier.model
													# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
													A_			    =		"HuggingFace is in"
													A_			    =		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_			    =		["HuggingFace is in ", "Paris is in France"]
													A_			    =		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_			    =		text_classifier(lowerCAmelCase ,  top_k=lowerCAmelCase	)
													A_			    =		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_			    =		{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
													A_			    =		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_			    =		[["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_			    =		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()	)









'''simple docstring'''
__SCREAMING_SNAKE_CASE					:				Optional[int]									=		{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


__SCREAMING_SNAKE_CASE					:				Tuple									=		{value: key for key, value in encode_dict.items()}







def       a_				(	UpperCamelCase_ ):
							A_			    =		""
							for letter in word.lower():
														if letter.isalpha() or letter == " ":
																					encoded += encode_dict[letter]
														else:
																					raise Exception("encode() accepts only letters of the alphabet and spaces" )
							return encoded







def       a_				(	UpperCamelCase_ ):
							if set(UpperCamelCase_ ) - {"A", "B", " "} != set():
														raise Exception("decode() accepts only 'A', 'B' and spaces" )
							A_			    =		""
							for word in coded.split():
														while len(UpperCamelCase_ ) != 0:
																					decoded += decode_dict[word[:5]]
																					A_			    =		word[5:]
														decoded += " "
							return decoded.strip()


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

							testmod()





