



'''simple docstring'''




from ...processing_utils import ProcessorMixin







class       _lowerCAmelCase				(     UpperCamelCase_					):
	__SCREAMING_SNAKE_CASE  :						Union[str, Any]						     =   'WhisperFeatureExtractor'
	__SCREAMING_SNAKE_CASE  :						List[str]						     =   'WhisperTokenizer'


	def __init__(self			, lowercase			, lowercase				):
				super().__init__(lowercase_			, lowercase_				)
				A_      :		int      =	self.feature_extractor
				A_      :		Dict      =	False


	def        _a      (self			, lowercase=None			, lowercase=None			, lowercase=True				):
				return self.tokenizer.get_decoder_prompt_ids(task=lowercase_			, language=lowercase_			, no_timestamps=lowercase_				)


	def __call__(self			, *lowercase			, **lowercase				):




				if self._in_target_context_manager:
							return self.current_processor(*lowercase_			, **lowercase_				)

				A_      :		List[Any]      =	kwargs.pop("""audio"""			, lowercase_				)
				A_      :		Any      =	kwargs.pop("""sampling_rate"""			, lowercase_				)
				A_      :		List[str]      =	kwargs.pop("""text"""			, lowercase_				)
				if len(lowercase_				) > 0:
							A_      :		List[Any]      =	args[0]
							A_      :		List[Any]      =	args[1:]

				if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process."""				)

				if audio is not None:
							A_      :		Tuple      =	self.feature_extractor(lowercase_			, *lowercase_			, sampling_rate=lowercase_			, **lowercase_				)
				if text is not None:
							A_      :		Any      =	self.tokenizer(lowercase_			, **lowercase_				)

				if text is None:
							return inputs

				elif audio is None:
							return encodings
				else:
							A_      :		Optional[int]      =	encodings['input_ids']
							return inputs


	def        _a      (self			, *lowercase			, **lowercase				):
				return self.tokenizer.batch_decode(*lowercase_			, **lowercase_				)


	def        _a      (self			, *lowercase			, **lowercase				):
				return self.tokenizer.decode(*lowercase_			, **lowercase_				)


	def        _a      (self			, lowercase			, lowercase="np"				):
				return self.tokenizer.get_prompt_ids(lowercase_			, return_tensors=lowercase_				)



# 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())


