


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.
snake_case							:       List[Any]     			=      {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}







@is_pipeline_test
class        snake_case_   (unittest.TestCase      ):
					UpperCAmelCase__					:    Any          =			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
					UpperCAmelCase__					:    List[str]          =			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

					if model_mapping is not None:
										UpperCAmelCase__					:    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__					:    str          =			{
										    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
										}
					@require_torch
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[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(__snake_case      )			,[{'label': 'LABEL_0', 'score': 0.5_04}]      )

												a__             =     text_classifier('This is great !'			,top_k=2      )
												self.assertEqual(
												    nested_simplify(__snake_case      )			,[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]      )

												a__             =     text_classifier(['This is great !', 'This is bad']			,top_k=2      )
												self.assertEqual(
												    nested_simplify(__snake_case      )			,[
												        [{'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__             =     text_classifier('This is great !'			,top_k=1      )
												self.assertEqual(nested_simplify(__snake_case      )			,[{'label': 'LABEL_0', 'score': 0.5_04}]      )

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

												a__             =     text_classifier('This is great !'			,return_all_scores=__snake_case      )
												self.assertEqual(
												    nested_simplify(__snake_case      )			,[[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]]      )

												a__             =     text_classifier(['This is great !', 'Something else']			,return_all_scores=__snake_case      )
												self.assertEqual(
												    nested_simplify(__snake_case      )			,[
												        [{'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__             =     text_classifier(['This is great !', 'Something else']			,return_all_scores=__snake_case      )
												self.assertEqual(
												    nested_simplify(__snake_case      )			,[
												        {'label': 'LABEL_0', 'score': 0.5_04},
												        {'label': 'LABEL_0', 'score': 0.5_04},
												    ]			,)
					@require_torch
					def  lowerCamelCase__(      self   :Tuple      )					->    List[Any]:
												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(__snake_case      )			,[{'label': 'LABEL_0', 'score': 0.5_04}]      )
					@require_tf
					def  lowerCamelCase__(      self   :List[str]      )					->    Any:
												a__             =     pipeline(
												    task='text-classification'			,model='hf-internal-testing/tiny-random-distilbert'			,framework='tf'      )

												a__             =     text_classifier('This is great !'      )
												self.assertEqual(nested_simplify(__snake_case      )			,[{'label': 'LABEL_0', 'score': 0.5_04}]      )
					@slow
					@require_torch
					def  lowerCamelCase__(      self   :str      )					->    List[str]:
												a__             =     pipeline('text-classification'      )

												a__             =     text_classifier('This is great !'      )
												self.assertEqual(nested_simplify(__snake_case      )			,[{'label': 'POSITIVE', 'score': 1.0}]      )
												a__             =     text_classifier('This is bad !'      )
												self.assertEqual(nested_simplify(__snake_case      )			,[{'label': 'NEGATIVE', 'score': 1.0}]      )
												a__             =     text_classifier('Birds are a type of animal'      )
												self.assertEqual(nested_simplify(__snake_case      )			,[{'label': 'POSITIVE', 'score': 0.9_88}]      )
					@slow
					@require_tf
					def  lowerCamelCase__(      self   :Any      )					->    Union[str, Any]:
												a__             =     pipeline('text-classification'			,framework='tf'      )

												a__             =     text_classifier('This is great !'      )
												self.assertEqual(nested_simplify(__snake_case      )			,[{'label': 'POSITIVE', 'score': 1.0}]      )
												a__             =     text_classifier('This is bad !'      )
												self.assertEqual(nested_simplify(__snake_case      )			,[{'label': 'NEGATIVE', 'score': 1.0}]      )
												a__             =     text_classifier('Birds are a type of animal'      )
												self.assertEqual(nested_simplify(__snake_case      )			,[{'label': 'POSITIVE', 'score': 0.9_88}]      )
					def  lowerCamelCase__(      self   :Union[str, Any]			,__snake_case   :Optional[Any]			,__snake_case   :str			,__snake_case   :int      )					->    int:
												a__             =     TextClassificationPipeline(model=__snake_case			,tokenizer=__snake_case      )
												return text_classifier, ["HuggingFace is in", "This is another test"]



					def  lowerCamelCase__(      self   :Optional[int]			,__snake_case   :int			,__snake_case   :Dict      )					->    Optional[int]:
												a__             =     text_classifier.model
												# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
												a__             =     'HuggingFace is in'
												a__             =     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__             =     ['HuggingFace is in ', 'Paris is in France']
												a__             =     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__             =     text_classifier(__snake_case			,top_k=__snake_case      )
												a__             =     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__             =     {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
												a__             =     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__             =     [['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__             =     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()      )








import argparse


snake_case							:       int     			=      '''docs/source/_static/js/custom.js'''






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]   ):
							with open(__lowerCAmelCase					,    encoding='utf-8'					,    newline='\n'   ) as f:
														a__             =     f.readlines()
							a__             =     0

							# First let's put the right version
							while not lines[index].startswith('const stableVersion ='   ):
														index += 1
							a__             =     F'const stableVersion = "v{version}"\n'

							# Then update the dictionary
							while not lines[index].startswith('const versionMapping = {'   ):
														index += 1

							# We go until the end
							while not lines[index].startswith('}'   ):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'    "v{version}": "v{version}",\n'

							with open(__lowerCAmelCase					,    'w'					,    encoding='utf-8'					,    newline='\n'   ) as f:
														f.writelines(__lowerCAmelCase   )


if __name__ == "__main__":
						snake_case							:       Optional[Any]     			=      argparse.ArgumentParser()
						parser.add_argument('''--version''', help='''Release version.''')
						snake_case							:       Optional[int]     			=      parser.parse_args()
						update_custom_js(args.version)




