






"""simple docstring"""
import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCamelCase_							      = '''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''

def 							snake_case (  ):
			UpperCAmelCase_	:					Any     	=   _ask_options(
			    "In which compute environment are you running?"      ,["This machine", "AWS (Amazon SageMaker)"]      ,_convert_compute_environment      ,)
			if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
						UpperCAmelCase_	:					Any     	=   get_sagemaker_input()
			else:
						UpperCAmelCase_	:					Dict     	=   get_cluster_input()
			return config

def 							snake_case (  A__=None		):




			if subparsers is not None:
						UpperCAmelCase_	:					List[str]     	=   subparsers.add_parser("config"      ,description=A__		)
			else:
						UpperCAmelCase_	:					List[Any]     	=   argparse.ArgumentParser("Accelerate config command"      ,description=A__		)

			parser.add_argument(
			    "--config_file"      ,default=A__      ,help=(
			        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
			        "location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have "
			        "such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed "
			        "with \'huggingface\'."
			    )      ,)

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

def 							snake_case (  A__		):
			UpperCAmelCase_	:					Tuple     	=   get_user_input()
			if args.config_file is not None:
						UpperCAmelCase_	:					Optional[Any]     	=   args.config_file
			else:
						if not os.path.isdir(A__		):
									os.makedirs(A__		)
						UpperCAmelCase_	:					Any     	=   default_yaml_config_file

			if config_file.endswith(".json"		):
						config.to_json_file(A__		)
			else:
						config.to_yaml_file(A__		)
			print(F"""accelerate configuration saved at {config_file}"""		)

def 							snake_case (  ):
			UpperCAmelCase_	:					List[str]     	=   config_command_parser()
			UpperCAmelCase_	:					str     	=   parser.parse_args()
			config_command(A__		)


if __name__ == "__main__":
			main()





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''SpeechT5Tokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SpeechT5ForSpeechToText''',
       '''SpeechT5ForSpeechToSpeech''',
       '''SpeechT5ForTextToSpeech''',
       '''SpeechT5Model''',
       '''SpeechT5PreTrainedModel''',
       '''SpeechT5HifiGan''',
   ]

if TYPE_CHECKING:
   from .configuration_speechta import (
       SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
       SpeechTaConfig,
       SpeechTaHifiGanConfig,
   )
   from .feature_extraction_speechta import SpeechTaFeatureExtractor
   from .processing_speechta import SpeechTaProcessor

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_speechta import SpeechTaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speechta import (
          SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
          SpeechTaForSpeechToSpeech,
          SpeechTaForSpeechToText,
          SpeechTaForTextToSpeech,
          SpeechTaHifiGan,
          SpeechTaModel,
          SpeechTaPreTrainedModel,
      )

else:
   import sys

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



