






"""simple docstring"""





import argparse

import torch

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


logging.set_verbosity_info()
A__					: List[Any]=	logging.get_logger(__name__)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    UpperCamelCase__         =    WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE ,	config=SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    downstream_dict['projector.weight']
    UpperCamelCase__         =    downstream_dict['projector.bias']
    UpperCamelCase__         =    downstream_dict['model.post_net.linear.weight']
    UpperCamelCase__         =    downstream_dict['model.post_net.linear.bias']
    return model







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE ,	config=SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    downstream_dict['model.linear.weight']
    UpperCamelCase__         =    downstream_dict['model.linear.bias']
    return model







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[str]:
    """simple docstring"""

    UpperCamelCase__         =    WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE ,	config=SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    downstream_dict['connector.weight']
    UpperCamelCase__         =    downstream_dict['connector.bias']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
        UpperCamelCase__         =    downstream_dict[
            F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
        ]
        UpperCamelCase__         =    downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

    UpperCamelCase__         =    downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
    UpperCamelCase__         =    downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
    UpperCamelCase__         =    downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
    UpperCamelCase__         =    downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
    UpperCamelCase__         =    downstream_dict['objective.W']
    return model







@torch.no_grad()
def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Any:
    """simple docstring"""

    UpperCamelCase__         =    torch.load(SCREAMING_SNAKE_CASE ,	map_location='cpu'					)

    UpperCamelCase__         =    checkpoint['Downstream']

    UpperCamelCase__         =    WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    WavaVecaFeatureExtractor.from_pretrained(
        SCREAMING_SNAKE_CASE ,	return_attention_mask=SCREAMING_SNAKE_CASE ,	do_normalize=SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    hf_config.architectures[0]
    if arch.endswith('ForSequenceClassification'					):
        UpperCamelCase__         =    convert_classification(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    elif arch.endswith('ForAudioFrameClassification'					):
        UpperCamelCase__         =    convert_diarization(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    elif arch.endswith('ForXVector'					):
        UpperCamelCase__         =    convert_xvector(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    else:
        raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'					)

    if hf_config.use_weighted_layer_sum:
        UpperCamelCase__         =    checkpoint['Featurizer']['weights']

    hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE					)
    hf_model.save_pretrained(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  A__					: str=	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__					: List[Any]=	parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)












"""simple docstring"""





# 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 subprocess

from packaging.version import Version, parse

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


A__					: Optional[Any]=	"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=None					)					->	Dict:
    """simple docstring"""

    if subparsers is not None:
        UpperCamelCase__         =    subparsers.add_parser('tpu-config' ,	description=_description					)
    else:
        UpperCamelCase__         =    argparse.ArgumentParser('Accelerate tpu-config command' ,	description=_description					)
    # Core arguments
    UpperCamelCase__         =    parser.add_argument_group(
        'Config Arguments' ,	'Arguments that can be configured through `accelerate config`.'					)
    config_args.add_argument(
        '--config_file' ,	type=SCREAMING_SNAKE_CASE ,	default=SCREAMING_SNAKE_CASE ,	help='Path to the config file to use for accelerate.' ,	)
    config_args.add_argument(
        '--tpu_name' ,	default=SCREAMING_SNAKE_CASE ,	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=SCREAMING_SNAKE_CASE ,	help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.' ,	)
    UpperCamelCase__         =    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=SCREAMING_SNAKE_CASE ,	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=SCREAMING_SNAKE_CASE					)
    return parser







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    None

    # Get the default from the config file if it exists.
    if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    load_config_from_file(args.config_file					)
        if not args.command_file and defaults.command_file is not None and not args.command:
            UpperCamelCase__         =    defaults.command_file
        if not args.command and defaults.commands is not None:
            UpperCamelCase__         =    defaults.commands
        if not args.tpu_name:
            UpperCamelCase__         =    defaults.tpu_name
        if not args.tpu_zone:
            UpperCamelCase__         =    defaults.tpu_zone
    if args.accelerate_version == "dev":
        UpperCamelCase__         =    'git+https://github.com/huggingface/accelerate.git'
    elif args.accelerate_version == "latest":
        UpperCamelCase__         =    'accelerate -U'
    elif isinstance(parse(args.accelerate_version					) ,	SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    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:
            UpperCamelCase__         =    [f.read().splitlines()]

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

    # Then send it to gcloud
    # Eventually try to use google-api-core to do this instead of subprocess
    UpperCamelCase__         =    ['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(SCREAMING_SNAKE_CASE					)}'					)
        return
    subprocess.run(SCREAMING_SNAKE_CASE					)
    print('Successfully setup pod.'					)







def   lowerCAmelCase_( )					->	int:
    """simple docstring"""

    UpperCamelCase__         =    tpu_command_parser()
    UpperCamelCase__         =    parser.parse_args()

    tpu_command_launcher(SCREAMING_SNAKE_CASE					)




