



'''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					):


	@require_torch
	def        _a      (self				):
				A_      :		List[Any]      =	pipeline(
				    task="""zero-shot-audio-classification"""			, model="""hf-internal-testing/tiny-clap-htsat-unfused"""				)
				A_      :		Union[str, Any]      =	load_dataset("""ashraq/esc50"""				)
				A_      :		Union[str, Any]      =	dataset['train']['audio'][-1]['array']
				A_      :		List[Any]      =	audio_classifier(lowercase_			, candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]				)
				self.assertEqual(
				    nested_simplify(lowercase_				)			, [{"""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        _a      (self				):
				pass


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

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

				A_      :		Any      =	audio_classifier([audio] * 5			, candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]				)
				self.assertEqual(
				    nested_simplify(lowercase_				)			, [
				        [
				            {"""score""": 0.9_99, """label""": """Sound of a dog"""},
				            {"""score""": 0.0_01, """label""": """Sound of vaccum cleaner"""},
				        ],
				    ]
				    * 5			, )
				A_      :		Tuple      =	audio_classifier(
				    [audio] * 5			, candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]			, batch_size=5				)
				self.assertEqual(
				    nested_simplify(lowercase_				)			, [
				        [
				            {"""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        _a      (self				):
				pass



# Copyright 2022 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.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available




def       a_ (       SCREAMING_SNAKE_CASE__   : str=None	):
       '''simple docstring'''





       if subparsers is not None:
              _lowerCamelCase			:						str            =subparsers.add_parser('env'	)
       else:
              _lowerCamelCase			:						List[Any]            =argparse.ArgumentParser('Accelerate env command'	)

       parser.add_argument(
           '--config_file'						,     default=SCREAMING_SNAKE_CASE__						,     help='The config file to use for the default values in the launching script.'	)

       if subparsers is not None:
              parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
       return parser




def       a_ (       SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						int            =torch.__version__
       _lowerCamelCase			:						Optional[Any]            =torch.cuda.is_available()
       _lowerCamelCase			:						int            =is_xpu_available()
       _lowerCamelCase			:						Union[str, Any]            =is_npu_available()

       _lowerCamelCase			:						Optional[int]            ='Not found'
       # Get the default from the config file.
       if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						Any            =load_config_from_file(args.config_file	).to_dict()

       _lowerCamelCase			:						List[str]            ={
           '`Accelerate` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'Numpy version': np.__version__,
           'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
           'PyTorch XPU available': str(SCREAMING_SNAKE_CASE__	),
           'PyTorch NPU available': str(SCREAMING_SNAKE_CASE__	),
           'System RAM': F'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''',
       }
       if pt_cuda_available:
              _lowerCamelCase			:						List[Any]            =torch.cuda.get_device_name()

       print('\nCopy-and-paste the text below in your GitHub issue\n'	)
       print('\n'.join([F'''- {prop}: {val}''' for prop, val in info.items()]	)	)

       print('- `Accelerate` default config:' if args.config_file is None else '- `Accelerate` config passed:'	)
       _lowerCamelCase			:						Dict            =(
           '\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
           if isinstance(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
           else F'''\t{accelerate_config}'''
       )
       print(SCREAMING_SNAKE_CASE__	)

       _lowerCamelCase			:						str            =accelerate_config

       return info




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =env_command_parser()
       _lowerCamelCase			:						Any            =parser.parse_args()
       env_command(SCREAMING_SNAKE_CASE__	)
       return 0


if __name__ == "__main__":
 raise SystemExit(main())


