


import math

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


_snake_case					:		Dict      =    logging.get_logger(__name__)

_snake_case					:		int      =    {
    "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     a		(__SCREAMING_SNAKE_CASE				):




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  "data2vec-audio"

							def __init__(							self	:  Dict   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  int=768   ,     lowerCamelCase	:  Any=12   ,     lowerCamelCase	:  List[str]=12   ,     lowerCamelCase	:  Tuple=3072   ,     lowerCamelCase	:  Dict="gelu"   ,     lowerCamelCase	:  List[Any]=0.1   ,     lowerCamelCase	:  List[str]=0.1   ,     lowerCamelCase	:  List[Any]=0.1   ,     lowerCamelCase	:  Any=0.0   ,     lowerCamelCase	:  str=0.1   ,     lowerCamelCase	:  str=0.1   ,     lowerCamelCase	:  Any=0.02   ,     lowerCamelCase	:  int=1E-5   ,     lowerCamelCase	:  Optional[Any]="gelu"   ,     lowerCamelCase	:  Optional[int]=(512, 512, 512, 512, 512, 512, 512)   ,     lowerCamelCase	:  Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)   ,     lowerCamelCase	:  Optional[Any]=(10, 3, 3, 3, 3, 2, 2)   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Any=16   ,     lowerCamelCase	:  int=19   ,     lowerCamelCase	:  str=5   ,     lowerCamelCase	:  List[str]=0.05   ,     lowerCamelCase	:  Tuple=10   ,     lowerCamelCase	:  List[str]=2   ,     lowerCamelCase	:  int=0.0   ,     lowerCamelCase	:  Any=10   ,     lowerCamelCase	:  int=0   ,     lowerCamelCase	:  str="sum"   ,     lowerCamelCase	:  Optional[Any]=False   ,     lowerCamelCase	:  Optional[Any]=False   ,     lowerCamelCase	:  Optional[Any]=256   ,     lowerCamelCase	:  Optional[Any]=(512, 512, 512, 512, 1500)   ,     lowerCamelCase	:  List[Any]=(5, 3, 3, 1, 1)   ,     lowerCamelCase	:  Union[str, Any]=(1, 2, 3, 1, 1)   ,     lowerCamelCase	:  Tuple=512   ,     lowerCamelCase	:  List[str]=0   ,     lowerCamelCase	:  str=1   ,     lowerCamelCase	:  List[Any]=2   ,     lowerCamelCase	:  List[str]=False   ,     lowerCamelCase	:  int=3   ,     lowerCamelCase	:  str=2   ,     lowerCamelCase	:  Optional[int]=3   ,     lowerCamelCase	:  Any=None   ,     **lowerCamelCase	:  Dict   ,     )     ->   Tuple:
														super().__init__(**_snake_case   ,     pad_token_id=_snake_case   ,     bos_token_id=_snake_case   ,     eos_token_id=_snake_case    )
														__snake_case		:      int										=       hidden_size
														__snake_case		:      List[str]										=       feat_extract_activation
														__snake_case		:      Union[str, Any]										=       list(_snake_case    )
														__snake_case		:      Union[str, Any]										=       list(_snake_case    )
														__snake_case		:      Optional[int]										=       list(_snake_case    )
														__snake_case		:      Union[str, Any]										=       conv_bias
														__snake_case		:      Union[str, Any]										=       num_conv_pos_embeddings
														__snake_case		:      Union[str, Any]										=       num_conv_pos_embedding_groups
														__snake_case		:      Dict										=       conv_pos_kernel_size
														__snake_case		:      List[Any]										=       len(self.conv_dim    )
														__snake_case		:      Union[str, Any]										=       num_hidden_layers
														__snake_case		:      Union[str, Any]										=       intermediate_size
														__snake_case		:      Optional[int]										=       hidden_act
														__snake_case		:      List[str]										=       num_attention_heads
														__snake_case		:      Optional[int]										=       hidden_dropout
														__snake_case		:      Any										=       attention_dropout
														__snake_case		:      Optional[Any]										=       activation_dropout
														__snake_case		:      Any										=       feat_proj_dropout
														__snake_case		:      Optional[Any]										=       final_dropout
														__snake_case		:      str										=       layerdrop
														__snake_case		:      Union[str, Any]										=       layer_norm_eps
														__snake_case		:      List[Any]										=       initializer_range
														__snake_case		:      int										=       vocab_size
														__snake_case		:      List[Any]										=       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
														__snake_case		:      List[str]										=       mask_time_prob
														__snake_case		:      str										=       mask_time_length
														__snake_case		:      Optional[int]										=       mask_time_min_masks
														__snake_case		:      Optional[int]										=       mask_feature_prob
														__snake_case		:      Any										=       mask_feature_length
														__snake_case		:      Optional[Any]										=       mask_feature_min_masks

														# ctc loss
														__snake_case		:      Any										=       ctc_loss_reduction
														__snake_case		:      Optional[Any]										=       ctc_zero_infinity

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

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														__snake_case		:      Dict										=       classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														__snake_case		:      int										=       list(_snake_case    )
														__snake_case		:      Tuple										=       list(_snake_case    )
														__snake_case		:      Any										=       list(_snake_case    )
														__snake_case		:      Dict										=       xvector_output_dim






							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   List[str]:
														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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_snake_case					:		Optional[Any]      =    "Create a default config file for Accelerate with only a few flags set."







def     lowerCAmelCase_					( __lowerCamelCase="no"							,     __lowerCamelCase = default_json_config_file							,     __lowerCamelCase = False     ):
							__snake_case		:      int										=       Path(__lowerCamelCase     )
							path.parent.mkdir(parents=__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							if path.exists():
														print(
														    F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'     )
														return False
							__snake_case		:      Any										=       mixed_precision.lower()
							if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
														raise ValueError(
														    F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'     )
							__snake_case		:      Optional[int]										=       {
							    "compute_environment": "LOCAL_MACHINE",
							    "mixed_precision": mixed_precision,
							}
							if torch.cuda.is_available():
														__snake_case		:      Dict										=       torch.cuda.device_count()
														__snake_case		:      Tuple										=       num_gpus
														__snake_case		:      List[str]										=       False
														if num_gpus > 1:
																					__snake_case		:      Optional[int]										=       "MULTI_GPU"
														else:
																					__snake_case		:      Dict										=       "NO"
							elif is_xpu_available() and use_xpu:
														__snake_case		:      List[str]										=       torch.xpu.device_count()
														__snake_case		:      str										=       num_xpus
														__snake_case		:      int										=       False
														if num_xpus > 1:
																					__snake_case		:      Optional[int]										=       "MULTI_XPU"
														else:
																					__snake_case		:      str										=       "NO"
							elif is_npu_available():
														__snake_case		:      Any										=       torch.npu.device_count()
														__snake_case		:      str										=       num_npus
														__snake_case		:      str										=       False
														if num_npus > 1:
																					__snake_case		:      Optional[int]										=       "MULTI_NPU"
														else:
																					__snake_case		:      int										=       "NO"
							else:
														__snake_case		:      List[Any]										=       0
														__snake_case		:      Dict										=       True
														__snake_case		:      Tuple										=       1
														__snake_case		:      Tuple										=       "NO"
							__snake_case		:      str										=       ClusterConfig(**__lowerCamelCase     )
							config.to_json_file(__lowerCamelCase     )
							return path







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Optional[Any]										=       parser.add_parser("default"							,     parents=__lowerCamelCase							,     help=__lowerCamelCase							,     formatter_class=__lowerCamelCase     )
							parser.add_argument(
							    "--config_file"							,     default=__lowerCamelCase							,     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'."
							    )							,     dest="save_location"							,     )

							parser.add_argument(
							    "--mixed_precision"							,     choices=["no", "fp16", "bf16"]							,     type=__lowerCamelCase							,     help="Whether or not to use mixed precision training. "
							    "Choose between FP16 and BF16 (bfloat16) training. "
							    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."							,     default="no"							,     )
							parser.set_defaults(func=__lowerCamelCase     )
							return parser







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       write_basic_config(args.mixed_precision							,     args.save_location     )
							if config_file:
														print(F'accelerate configuration saved at {config_file}'     )

