
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 			UpperCamelCase					(    __lowerCamelCase     : Tuple=None    ):


       if subparsers is not None:
              snake_case       :   Tuple							=      subparsers.add_parser("env"    )
       else:
              snake_case       :   List[Any]							=      argparse.ArgumentParser("Accelerate env command"    )

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

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


def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]    ):
       snake_case       :   str							=      torch.__version__
       snake_case       :   Union[str, Any]							=      torch.cuda.is_available()
       snake_case       :   Any							=      is_xpu_available()
       snake_case       :   int							=      is_npu_available()

       snake_case       :   str							=      "Not found"
       # Get the default from the config file.
       if args.config_file is not None or os.path.isfile(UpperCamelCase_    ):
              snake_case       :   Any							=      load_config_from_file(args.config_file    ).to_dict()

       snake_case       :   Tuple							=      {
           "`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(UpperCamelCase_    ),
           "PyTorch NPU available": str(UpperCamelCase_    ),
           "System RAM": f"""{psutil.virtual_memory().total / 1024 ** 3:.2f} GB""",
       }
       if pt_cuda_available:
              snake_case       :   int							=      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:"    )
       snake_case       :   Tuple							=      (
           "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]    )
           if isinstance(UpperCamelCase_				,					UpperCamelCase_    )
           else f"""\t{accelerate_config}"""
       )
       print(UpperCamelCase_    )

       snake_case       :   Union[str, Any]							=      accelerate_config

       return info


def 			UpperCamelCase					(    ):
       snake_case       :   Tuple							=      env_command_parser()
       snake_case       :   List[Any]							=      parser.parse_args()
       env_command(UpperCamelCase_    )
       return 0


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








"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. 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 ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	"openai/whisper-base"
 __UpperCAmelCase	:					Union[str, Any]   		=	(
     "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
     "transcribed text."
 )
 __UpperCAmelCase	:					List[str]   		=	"transcriber"
 __UpperCAmelCase	:					Optional[Any]   		=	WhisperProcessor
 __UpperCAmelCase	:					str   		=	WhisperForConditionalGeneration

 __UpperCAmelCase	:					List[str]   		=	["audio"]
 __UpperCAmelCase	:					Tuple   		=	["text"]


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			int	):
    return self.pre_processor(UpperCAmelCase__,					return_tensors="pt"	).input_features


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any]	):
    return self.model.generate(inputs=UpperCAmelCase__	)



 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[int]	):
    return self.pre_processor.batch_decode(UpperCAmelCase__,					skip_special_tokens=UpperCAmelCase__	)[0]




