


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


@is_pipeline_test
class 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
     __SCREAMING_SNAKE_CASE     					=    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


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

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

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

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

       _UpperCAmelCase		:							Optional[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		:							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_1""", """score""": 0.4_9_6}],
               [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
           ]							,					)

       _UpperCAmelCase		:							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        __snake_case(  self    ):
       import torch

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

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


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

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


     @slow
     @require_torch
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     pipeline("""text-classification"""    )

       _UpperCAmelCase		:							Tuple							=     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[str]							=     text_classifier("""Birds are a type of animal"""    )
       self.assertEqual(nested_simplify(A_    )							,					[{"""label""": """POSITIVE""", """score""": 0.9_8_8}]    )


     @slow
     @require_tf
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							int							=     pipeline("""text-classification"""							,					framework="""tf"""    )

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


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




     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							Any							=     text_classifier.model
       # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
       _UpperCAmelCase		:							str							=     """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		:							Dict							=     ["""HuggingFace is in """, """Paris is in France"""]
       _UpperCAmelCase		:							Tuple							=     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		:							Optional[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		:							Union[str, Any]							=     {"""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		:							Optional[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		:							List[str]							=     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()    )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__					:     List[str]       =     [
     'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
     'SEWForCTC',
     'SEWForSequenceClassification',
     'SEWModel',
     'SEWPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_sew import (
      SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
      SEWForCTC,
      SEWForSequenceClassification,
      SEWModel,
      SEWPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE__					:     Tuple       =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





