"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase					: Optional[int]        ={"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase					: Union[str, Any]        =[
           """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """WavLMForAudioFrameClassification""",
           """WavLMForCTC""",
           """WavLMForSequenceClassification""",
           """WavLMForXVector""",
           """WavLMModel""",
           """WavLMPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavlm import (
                  WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavLMForAudioFrameClassification,
                  WavLMForCTC,
                  WavLMForSequenceClassification,
                  WavLMForXVector,
                  WavLMModel,
                  WavLMPreTrainedModel,
              )

else:
       import sys

       __lowerCAmelCase					: Optional[int]        =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




"""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					: int        ={"""LayoutLMv2Config""", """LayoutLMv3Config"""}





@is_pipeline_test
class 				_A		( unittest.TestCase			):
      snake_case__    :     Optional[int]													=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      snake_case__    :     str													=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

      @require_torch
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			pipeline(
                task="""text-classification"""	, model="""hf-internal-testing/tiny-random-distilbert"""	, framework="""pt"""		)

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

            lowercase														=			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}]		)

            lowercase														=			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}],
                ]	, )

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

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

            lowercase														=			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}]]		)

            lowercase														=			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}],
                ]	, )

            lowercase														=			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        A__ (		self		):







            """simple docstring"""



            import torch

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

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

      @require_tf
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			pipeline(
                task="""text-classification"""	, model="""hf-internal-testing/tiny-random-distilbert"""	, framework="""tf"""		)

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

      @slow
      @require_torch
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			pipeline("""text-classification"""		)

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

      @slow
      @require_tf
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			pipeline("""text-classification"""	, framework="""tf"""		)

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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			TextClassificationPipeline(model=__lowerCAmelCase	, tokenizer=__lowerCAmelCase		)
            return text_classifier, ["HuggingFace is in", "This is another test"]







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			text_classifier.model
            # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
            lowercase														=			"""HuggingFace is in"""
            lowercase														=			text_classifier(__lowerCAmelCase		)

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

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

            lowercase														=			{"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
            lowercase														=			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.
            lowercase														=			[["""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
            lowercase														=			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()		)



