






"""simple docstring"""




import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:		int		     =			logging.get_logger(__name__)

A_		:		str		     =			{
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		List[Any]									='''data2vec-audio'''





       def __init__( self  ,      _lowerCamelCase=3_2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=3_0_7_2  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1e-5  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)  ,      _lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)  ,      _lowerCamelCase=(1_0, 3, 3, 3, 3, 2, 2)  ,      _lowerCamelCase=False  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase=1_9  ,      _lowerCamelCase=5  ,      _lowerCamelCase=0.0_5  ,      _lowerCamelCase=1_0  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=1_0  ,      _lowerCamelCase=0  ,      _lowerCamelCase="sum"  ,      _lowerCamelCase=False  ,      _lowerCamelCase=False  ,      _lowerCamelCase=2_5_6  ,      _lowerCamelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)  ,      _lowerCamelCase=(5, 3, 3, 1, 1)  ,      _lowerCamelCase=(1, 2, 3, 1, 1)  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=0  ,      _lowerCamelCase=1  ,      _lowerCamelCase=2  ,      _lowerCamelCase=False  ,      _lowerCamelCase=3  ,      _lowerCamelCase=2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=None  ,      **_lowerCamelCase  ,      ):
         super().__init__(**_lowerCamelCase  ,      pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    hidden_size
         UpperCamelCase_: int   				=    feat_extract_activation
         UpperCamelCase_: Optional[Any]   				=    list(_lowerCamelCase							)
         UpperCamelCase_: int   				=    list(_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    list(_lowerCamelCase							)
         UpperCamelCase_: Union[str, Any]   				=    conv_bias
         UpperCamelCase_: Optional[int]   				=    num_conv_pos_embeddings
         UpperCamelCase_: List[str]   				=    num_conv_pos_embedding_groups
         UpperCamelCase_: List[Any]   				=    conv_pos_kernel_size
         UpperCamelCase_: int   				=    len(self.conv_dim							)
         UpperCamelCase_: Union[str, Any]   				=    num_hidden_layers
         UpperCamelCase_: Tuple   				=    intermediate_size
         UpperCamelCase_: Union[str, Any]   				=    hidden_act
         UpperCamelCase_: Optional[Any]   				=    num_attention_heads
         UpperCamelCase_: Optional[Any]   				=    hidden_dropout
         UpperCamelCase_: Union[str, Any]   				=    attention_dropout
         UpperCamelCase_: Union[str, Any]   				=    activation_dropout
         UpperCamelCase_: Optional[int]   				=    feat_proj_dropout
         UpperCamelCase_: str   				=    final_dropout
         UpperCamelCase_: Any   				=    layerdrop
         UpperCamelCase_: Union[str, Any]   				=    layer_norm_eps
         UpperCamelCase_: str   				=    initializer_range
         UpperCamelCase_: List[Any]   				=    vocab_size
         UpperCamelCase_: str   				=    use_weighted_layer_sum

         if (
             (len(self.conv_stride							) != self.num_feat_extract_layers)
             or (len(self.conv_kernel							) != self.num_feat_extract_layers)
             or (len(self.conv_dim							) != self.num_feat_extract_layers)
         ):
           raise ValueError(
               'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
               ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
               f''' {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,'''
               f''' `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         UpperCamelCase_: int   				=    mask_time_prob
         UpperCamelCase_: Union[str, Any]   				=    mask_time_length
         UpperCamelCase_: Union[str, Any]   				=    mask_time_min_masks
         UpperCamelCase_: str   				=    mask_feature_prob
         UpperCamelCase_: int   				=    mask_feature_length
         UpperCamelCase_: List[Any]   				=    mask_feature_min_masks

         # ctc loss
         UpperCamelCase_: Tuple   				=    ctc_loss_reduction
         UpperCamelCase_: Tuple   				=    ctc_zero_infinity

         # adapter
         UpperCamelCase_: List[str]   				=    add_adapter
         UpperCamelCase_: List[Any]   				=    adapter_kernel_size
         UpperCamelCase_: Optional[int]   				=    adapter_stride
         UpperCamelCase_: Union[str, Any]   				=    num_adapter_layers
         UpperCamelCase_: List[Any]   				=    output_hidden_size or hidden_size

         # SequenceClassification-specific parameter. Feel free to ignore for other classes.
         UpperCamelCase_: List[str]   				=    classifier_proj_size

         # XVector-specific parameters. Feel free to ignore for other classes.
         UpperCamelCase_: List[str]   				=    list(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    list(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    list(_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    xvector_output_dim





       @property
       def       _a   ( self							):
         return math.prod(self.conv_stride							)







# Copyright 2021 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

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


A_		:		Any		     =			'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 ()	->    Union[str, Any]:
  UpperCamelCase_: Tuple   				=    _ask_options(
      'In which compute environment are you running?'     ,   ['This machine', 'AWS (Amazon SageMaker)']     ,   _convert_compute_environment     ,   )
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    UpperCamelCase_: List[str]   				=    get_sagemaker_input()
  else:
    UpperCamelCase_: List[str]   				=    get_cluster_input()
  return config

def   snake_case (UpperCAmelCase__=None  )	->    Union[str, Any]:
  if subparsers is not None:
    UpperCamelCase_: List[Any]   				=    subparsers.add_parser('config'     ,   description=UpperCAmelCase__  )
  else:
    UpperCamelCase_: List[Any]   				=    argparse.ArgumentParser('Accelerate config command'     ,   description=UpperCAmelCase__  )

  parser.add_argument(
      '--config_file'     ,   default=UpperCAmelCase__     ,   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=UpperCAmelCase__  )
  return parser

def   snake_case (UpperCAmelCase__  )	->    List[Any]:
  UpperCamelCase_: Union[str, Any]   				=    get_user_input()
  if args.config_file is not None:
    UpperCamelCase_: Tuple   				=    args.config_file
  else:
    if not os.path.isdir(UpperCAmelCase__  ):
      os.makedirs(UpperCAmelCase__  )
    UpperCamelCase_: Dict   				=    default_yaml_config_file

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

def   snake_case ()	->    str:
  UpperCamelCase_: Tuple   				=    config_command_parser()
  UpperCamelCase_: int   				=    parser.parse_args()
  config_command(UpperCAmelCase__  )


if __name__ == "__main__":
   main()