
"""simple docstring"""
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       lowerCAmelCase_		(				unittest.TestCase ):



							'''simple docstring'''





							@require_torch
							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  pipeline(
													    task="""zero-shot-audio-classification"""    ,				model="""hf-internal-testing/tiny-clap-htsat-unfused"""				)
													__lowerCAmelCase     =  load_dataset("""ashraq/esc50"""				)
													__lowerCAmelCase     =  dataset["""train"""]["""audio"""][-1]["""array"""]
													__lowerCAmelCase     =  audio_classifier(snake_case_    ,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]				)
													self.assertEqual(
													    nested_simplify(snake_case_				)    ,				[{"""score""": 0.501, """label""": """Sound of a dog"""}, {"""score""": 0.499, """label""": """Sound of vaccum cleaner"""}]    ,				)




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




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

													self.assertEqual(
													    nested_simplify(snake_case_				)    ,				[
													        {"""score""": 0.999, """label""": """Sound of a dog"""},
													        {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
													    ]    ,				)

													__lowerCAmelCase     =  audio_classifier([audio] * 5    ,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]				)
													self.assertEqual(
													    nested_simplify(snake_case_				)    ,				[
													        [
													            {"""score""": 0.999, """label""": """Sound of a dog"""},
													            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
													        ],
													    ]
													    * 5    ,				)
													__lowerCAmelCase     =  audio_classifier(
													    [audio] * 5    ,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]    ,				batch_size=5				)
													self.assertEqual(
													    nested_simplify(snake_case_				)    ,				[
													        [
													            {"""score""": 0.999, """label""": """Sound of a dog"""},
													            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
													        ],
													    ]
													    * 5    ,				)




							@unittest.skip("""No models are available in TF"""				)
							def    A__						( self				)       ->  Union[str, Any]:
													pass


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_					     =	{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''RagModel''',
			    '''RagPreTrainedModel''',
			    '''RagSequenceForGeneration''',
			    '''RagTokenForGeneration''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''TFRagModel''',
			    '''TFRagPreTrainedModel''',
			    '''TFRagSequenceForGeneration''',
			    '''TFRagTokenForGeneration''',
			]


if TYPE_CHECKING:
			from .configuration_rag import RagConfig
			from .retrieval_rag import RagRetriever
			from .tokenization_rag import RagTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_rag import (
						    TFRagModel,
						    TFRagPreTrainedModel,
						    TFRagSequenceForGeneration,
						    TFRagTokenForGeneration,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE_					     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
