






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        __UpperCamelCase    (     unittest.TestCase   ):







	@require_torch
	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  pipeline(
		    task="""zero-shot-audio-classification"""       ,				model="""hf-internal-testing/tiny-clap-htsat-unfused"""			)
		__lowercase             =  load_dataset("""ashraq/esc50"""			)
		__lowercase             =  dataset["""train"""]["""audio"""][-1]["""array"""]
		__lowercase             =  audio_classifier(_lowerCAmelCase       ,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]			)
		self.assertEqual(
		    nested_simplify(_lowerCAmelCase			)       ,				[{"""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      :							Any			)		-> int:
		"""simple docstring"""







		pass







	@slow
	@require_torch
	def  _a   (  self      :							Tuple			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  pipeline(
		    task="""zero-shot-audio-classification"""       ,				model="""laion/clap-htsat-unfused"""       ,				)
		# This is an audio of a dog
		__lowercase             =  load_dataset("""ashraq/esc50"""			)
		__lowercase             =  dataset["""train"""]["""audio"""][-1]["""array"""]
		__lowercase             =  audio_classifier(_lowerCAmelCase       ,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]			)

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

		__lowercase             =  audio_classifier([audio] * 5       ,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]			)
		self.assertEqual(
		    nested_simplify(_lowerCAmelCase			)       ,				[
		        [
		            {"""score""": 0.999, """label""": """Sound of a dog"""},
		            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
		        ],
		    ]
		    * 5       ,				)
		__lowercase             =  audio_classifier(
		    [audio] * 5       ,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]       ,				batch_size=5			)
		self.assertEqual(
		    nested_simplify(_lowerCAmelCase			)       ,				[
		        [
		            {"""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]			)		-> int:
		"""simple docstring"""







		pass









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCamelCase      :      Any     						=						{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      :      Dict     						=						[
						    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MraForMaskedLM""",
						    """MraForMultipleChoice""",
						    """MraForQuestionAnswering""",
						    """MraForSequenceClassification""",
						    """MraForTokenClassification""",
						    """MraLayer""",
						    """MraModel""",
						    """MraPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						__UpperCamelCase      :      List[str]     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

