


import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


SCREAMING_SNAKE_CASE__     					=			"Run commands across TPU VMs for initial setup before running `accelerate launch`."


def     lowercase	(    a=None ):



		'''simple docstring'''




		if subparsers is not None:
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						subparsers.add_parser("tpu-config"     , description=_description )
		else:
				SCREAMING_SNAKE_CASE_				:int				     =						argparse.ArgumentParser("Accelerate tpu-config command"     , description=_description )
		# Core arguments
		SCREAMING_SNAKE_CASE_				:Dict				     =						parser.add_argument_group(
		    "Config Arguments"     , "Arguments that can be configured through `accelerate config`." )
		config_args.add_argument(
		    "--config_file"     , type=A__     , default=A__     , help="Path to the config file to use for accelerate."     , )
		config_args.add_argument(
		    "--tpu_name"     , default=A__     , help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."     , )
		config_args.add_argument(
		    "--tpu_zone"     , default=A__     , help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."     , )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						parser.add_argument_group("TPU Arguments"     , "Arguments for options ran inside the TPU." )
		pod_args.add_argument(
		    "--use_alpha"     , action="store_true"     , help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."     , )
		pod_args.add_argument(
		    "--command_file"     , default=A__     , help="The path to the file containing the commands to run on the pod on startup."     , )
		pod_args.add_argument(
		    "--command"     , action="append"     , nargs="+"     , help="A command to run on the pod. Can be passed multiple times."     , )
		pod_args.add_argument(
		    "--install_accelerate"     , action="store_true"     , help="Whether to install accelerate on the pod. Defaults to False."     , )
		pod_args.add_argument(
		    "--accelerate_version"     , default="latest"     , help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."     , )
		pod_args.add_argument(
		    "--debug"     , action="store_true"     , help="If set, will print the command that would be run instead of running it." )

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


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						None

		# Get the default from the config file if it exists.
		if args.config_file is not None or os.path.isfile(A__ ):
				SCREAMING_SNAKE_CASE_				:Any				     =						load_config_from_file(args.config_file )
				if not args.command_file and defaults.command_file is not None and not args.command:
						SCREAMING_SNAKE_CASE_				:str				     =						defaults.command_file
				if not args.command and defaults.commands is not None:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						defaults.commands
				if not args.tpu_name:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						defaults.tpu_name
				if not args.tpu_zone:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						defaults.tpu_zone
		if args.accelerate_version == "dev":
				SCREAMING_SNAKE_CASE_				:Dict				     =						"""git+https://github.com/huggingface/accelerate.git"""
		elif args.accelerate_version == "latest":
				SCREAMING_SNAKE_CASE_				:str				     =						"""accelerate -U"""
		elif isinstance(parse(args.accelerate_version )     , A__ ):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						F"accelerate=={args.accelerate_version}"

		if not args.command_file and not args.command:
				raise ValueError("You must specify either a command file or a command to run on the pod." )

		if args.command_file:
				with open(args.command_file     , "r" ) as f:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						[f.read().splitlines()]

				# To turn list of lists into list of strings
		if isinstance(args.command[0]     , A__ ):
				SCREAMING_SNAKE_CASE_				:int				     =						[line for cmd in args.command for line in cmd]
		# Default to the shared folder and install accelerate
		SCREAMING_SNAKE_CASE_				:int				     =						["""cd /usr/share"""]
		if args.install_accelerate:
				new_cmd += [F"pip install {args.accelerate_version}"]
		new_cmd += args.command
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						"""; """.join(A__ )

		# Then send it to gcloud
		# Eventually try to use google-api-core to do this instead of subprocess
		SCREAMING_SNAKE_CASE_				:Any				     =						["""gcloud"""]
		if args.use_alpha:
				cmd += ["alpha"]
		cmd += [
		    "compute",
		    "tpus",
		    "tpu-vm",
		    "ssh",
		    args.tpu_name,
		    "--zone",
		    args.tpu_zone,
		    "--command",
		    args.command,
		    "--worker",
		    "all",
		]
		if args.debug:
				print(F"Running {' '.join(A__ )}" )
				return
		subprocess.run(A__ )
		print("Successfully setup pod." )


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:str				     =						tpu_command_parser()
		SCREAMING_SNAKE_CASE_				:Tuple				     =						parser.parse_args()

		tpu_command_launcher(A__ )










'''simple docstring'''






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__A   :    str											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  WavaVecaForSequenceClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : str     =  downstream_dict["""projector.weight"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""projector.bias"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""model.post_net.linear.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.post_net.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Any				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  WavaVecaForAudioFrameClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.linear.weight"""]
  lowerCAmelCase_     : List[str]     =  downstream_dict["""model.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[Any]     =  WavaVecaForXVector.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : List[Any]     =  downstream_dict["""connector.weight"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
    lowerCAmelCase_     : List[str]     =  downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    lowerCAmelCase_     : List[str]     =  downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  lowerCAmelCase_     : int     =  downstream_dict["""objective.W"""]
  return model







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  str				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  torch.load(A__				,       map_location="""cpu"""   )

  lowerCAmelCase_     : Optional[int]     =  checkpoint["""Downstream"""]

  lowerCAmelCase_     : int     =  WavaVecaConfig.from_pretrained(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  WavaVecaFeatureExtractor.from_pretrained(
      A__				,       return_attention_mask=A__				,       do_normalize=A__   )

  lowerCAmelCase_     : Optional[int]     =  hf_config.architectures[0]
  if arch.endswith("""ForSequenceClassification"""   ):
    lowerCAmelCase_     : List[str]     =  convert_classification(A__				,       A__				,       A__   )
  elif arch.endswith("""ForAudioFrameClassification"""   ):
    lowerCAmelCase_     : Dict     =  convert_diarization(A__				,       A__				,       A__   )
  elif arch.endswith("""ForXVector"""   ):
    lowerCAmelCase_     : List[Any]     =  convert_xvector(A__				,       A__				,       A__   )
  else:
    raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'   )

  if hf_config.use_weighted_layer_sum:
    lowerCAmelCase_     : Optional[int]     =  checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(A__   )
  hf_model.save_pretrained(A__   )


if __name__ == "__main__":
  __A   :    List[Any]											= argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __A   :    Tuple											= parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



