
'''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.
__lowerCAmelCase					=			{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}


@is_pipeline_test
class      _lowerCAmelCase       (       unittest.TestCase ):







							'''simple docstring'''

							lowerCAmelCase_   		=		MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							lowerCAmelCase_   		=		TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

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

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

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

														_snake_case  =    text_classifier(["""This is great !""", """This is bad"""]    ,    top_k=2    )
														self.assertEqual(
														    nested_simplify(__UpperCamelCase    )    ,    [
														        [{"""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  =    text_classifier("""This is great !"""    ,    top_k=1    )
														self.assertEqual(nested_simplify(__UpperCamelCase    )    ,    [{"""label""": """LABEL_0""", """score""": 0.504}]    )

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

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

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

							@require_torch
							def 			lowercase				(self    )	->  Tuple:
														import torch

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

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

							@require_tf
							def 			lowercase				(self    )	->  Dict:
														_snake_case  =    pipeline(
														    task="""text-classification"""    ,    model="""hf-internal-testing/tiny-random-distilbert"""    ,    framework="""tf"""    )

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

							@slow
							@require_torch
							def 			lowercase				(self    )	->  Optional[Any]:
														_snake_case  =    pipeline("""text-classification"""    )

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

							@slow
							@require_tf
							def 			lowercase				(self    )	->  Tuple:
														_snake_case  =    pipeline("""text-classification"""    ,    framework="""tf"""    )

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

							def 			lowercase				(self    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    )	->  Dict:
														_snake_case  =    TextClassificationPipeline(model=__UpperCamelCase    ,    tokenizer=__UpperCamelCase    )
														return text_classifier, ["HuggingFace is in", "This is another test"]





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

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

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

														_snake_case  =    {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
														_snake_case  =    text_classifier(__UpperCamelCase    )
														self.assertEqual(
														    nested_simplify(__UpperCamelCase    )    ,    {"""label""": ANY(__UpperCamelCase    ), """score""": ANY(__UpperCamelCase    )}    ,    )
														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  =    [["""HuggingFace is in """, """Paris is in France"""]]
														with self.assertRaises(__UpperCamelCase    ):
																					text_classifier(__UpperCamelCase    )

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







import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCamelCase      :       str    =						[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]





def    __lowerCAmelCase   (    __snake_case							):
     # Test all the extensions added in the setup
     for file in FILES_TO_FIND:
          if not (transformers_path / file).exists():
               return False
     return True


if __name__ == "__main__":
 lowerCamelCase      :       str    =						argparse.ArgumentParser()
 parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
 lowerCamelCase      :       Dict    =						parser.parse_args()
 if args.check_lib:
  lowerCamelCase      :       Optional[int]    =						importlib.import_module('''transformers''')
  lowerCamelCase      :       str    =						Path(transformers_module.__file__).parent
 else:
  lowerCamelCase      :       List[Any]    =						Path.cwd() / '''build/lib/transformers'''
 if not test_custom_files_are_present(transformers_path):
  raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')






