




import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow




@is_pipeline_test
@require_torch
class 						__lowercase		(unittest.TestCase							):




      """simple docstring"""





      @require_torch
      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         snake_case				:    Tuple           =	pipeline(
             task="""zero-shot-audio-classification"""    ,				model="""hf-internal-testing/tiny-clap-htsat-unfused"""     )
         snake_case				:    int           =	load_dataset("""ashraq/esc50"""     )
         snake_case				:    Dict           =	dataset["""train"""]["""audio"""][-1]["""array"""]
         snake_case				:    int           =	audio_classifier(A    ,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]     )
         self.assertEqual(
             nested_simplify(A     )    ,				[{"""score""": 0.5_01, """label""": """Sound of a dog"""}, {"""score""": 0.4_99, """label""": """Sound of vaccum cleaner"""}]    ,				)

      @unittest.skip("""No models are available in TF"""     )
      def      UpperCAmelCase				(				self     )		->    List[Any]:
         pass

      @slow
      @require_torch
      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         snake_case				:    Optional[int]           =	pipeline(
             task="""zero-shot-audio-classification"""    ,				model="""laion/clap-htsat-unfused"""    ,				)
         # This is an audio of a dog
         snake_case				:    List[Any]           =	load_dataset("""ashraq/esc50"""     )
         snake_case				:    Dict           =	dataset["""train"""]["""audio"""][-1]["""array"""]
         snake_case				:    Tuple           =	audio_classifier(A    ,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]     )

         self.assertEqual(
             nested_simplify(A     )    ,				[
                 {"""score""": 0.9_99, """label""": """Sound of a dog"""},
                 {"""score""": 0.0_01, """label""": """Sound of vaccum cleaner"""},
             ]    ,				)

         snake_case				:    int           =	audio_classifier([audio] * 5    ,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]     )
         self.assertEqual(
             nested_simplify(A     )    ,				[
                 [
                     {"""score""": 0.9_99, """label""": """Sound of a dog"""},
                     {"""score""": 0.0_01, """label""": """Sound of vaccum cleaner"""},
                 ],
             ]
             * 5    ,				)
         snake_case				:    List[str]           =	audio_classifier(
             [audio] * 5    ,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]    ,				batch_size=5     )
         self.assertEqual(
             nested_simplify(A     )    ,				[
                 [
                     {"""score""": 0.9_99, """label""": """Sound of a dog"""},
                     {"""score""": 0.0_01, """label""": """Sound of vaccum cleaner"""},
                 ],
             ]
             * 5    ,				)

      @unittest.skip("""No models are available in TF"""     )
      def      UpperCAmelCase				(				self     )		->    int:
         pass











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase	:	str				=   {
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[Any]				=   ['RoFormerTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RoFormerForCausalLM',
           'RoFormerForMaskedLM',
           'RoFormerForMultipleChoice',
           'RoFormerForQuestionAnswering',
           'RoFormerForSequenceClassification',
           'RoFormerForTokenClassification',
           'RoFormerLayer',
           'RoFormerModel',
           'RoFormerPreTrainedModel',
           'load_tf_weights_in_roformer',
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRoFormerForCausalLM',
           'TFRoFormerForMaskedLM',
           'TFRoFormerForMultipleChoice',
           'TFRoFormerForQuestionAnswering',
           'TFRoFormerForSequenceClassification',
           'TFRoFormerForTokenClassification',
           'TFRoFormerLayer',
           'TFRoFormerModel',
           'TFRoFormerPreTrainedModel',
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Union[str, Any]				=   [
           'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'FlaxRoFormerForMaskedLM',
           'FlaxRoFormerForMultipleChoice',
           'FlaxRoFormerForQuestionAnswering',
           'FlaxRoFormerForSequenceClassification',
           'FlaxRoFormerForTokenClassification',
           'FlaxRoFormerModel',
           'FlaxRoFormerPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

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





