

'''simple docstring'''

import math

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


A_       						=							logging.get_logger(__name__)

A_       						=							{
    "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 							_snake_case (       __lowerCAmelCase						):
   _A       :     Dict       =					'''data2vec-audio'''
   def __init__( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=32     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=768     ,SCREAMING_SNAKE_CASE__			:      List[str]=12     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=12     ,SCREAMING_SNAKE_CASE__			:      List[Any]=3_072     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]="gelu"     ,SCREAMING_SNAKE_CASE__			:      str=0.1     ,SCREAMING_SNAKE_CASE__			:      Dict=0.1     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=0.1     ,SCREAMING_SNAKE_CASE__			:      List[str]=0.0     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=0.1     ,SCREAMING_SNAKE_CASE__			:      List[str]=0.1     ,SCREAMING_SNAKE_CASE__			:      str=0.02     ,SCREAMING_SNAKE_CASE__			:      int=1e-5     ,SCREAMING_SNAKE_CASE__			:      str="gelu"     ,SCREAMING_SNAKE_CASE__			:      List[str]=(512, 512, 512, 512, 512, 512, 512)     ,SCREAMING_SNAKE_CASE__			:      Any=(5, 2, 2, 2, 2, 2, 2)     ,SCREAMING_SNAKE_CASE__			:      Dict=(10, 3, 3, 3, 3, 2, 2)     ,SCREAMING_SNAKE_CASE__			:      List[Any]=False     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=16     ,SCREAMING_SNAKE_CASE__			:      List[str]=19     ,SCREAMING_SNAKE_CASE__			:      List[Any]=5     ,SCREAMING_SNAKE_CASE__			:      str=0.05     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=10     ,SCREAMING_SNAKE_CASE__			:      Dict=2     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=0.0     ,SCREAMING_SNAKE_CASE__			:      str=10     ,SCREAMING_SNAKE_CASE__			:      Any=0     ,SCREAMING_SNAKE_CASE__			:      Any="sum"     ,SCREAMING_SNAKE_CASE__			:      str=False     ,SCREAMING_SNAKE_CASE__			:      List[Any]=False     ,SCREAMING_SNAKE_CASE__			:      List[Any]=256     ,SCREAMING_SNAKE_CASE__			:      List[str]=(512, 512, 512, 512, 1_500)     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=(5, 3, 3, 1, 1)     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=(1, 2, 3, 1, 1)     ,SCREAMING_SNAKE_CASE__			:      List[str]=512     ,SCREAMING_SNAKE_CASE__			:      Tuple=0     ,SCREAMING_SNAKE_CASE__			:      int=1     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=2     ,SCREAMING_SNAKE_CASE__			:      str=False     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=3     ,SCREAMING_SNAKE_CASE__			:      List[Any]=2     ,SCREAMING_SNAKE_CASE__			:      int=3     ,SCREAMING_SNAKE_CASE__			:      str=None     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     super().__init__(**_UpperCamelCase     ,pad_token_id=_UpperCamelCase     ,bos_token_id=_UpperCamelCase     ,eos_token_id=_UpperCamelCase							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					hidden_size
     SCREAMING_SNAKE_CASE:List[str]						      =					feat_extract_activation
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					list(_UpperCamelCase							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					list(_UpperCamelCase							)
     SCREAMING_SNAKE_CASE:Dict						      =					list(_UpperCamelCase							)
     SCREAMING_SNAKE_CASE:int						      =					conv_bias
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					num_conv_pos_embeddings
     SCREAMING_SNAKE_CASE:List[str]						      =					num_conv_pos_embedding_groups
     SCREAMING_SNAKE_CASE:int						      =					conv_pos_kernel_size
     SCREAMING_SNAKE_CASE:Dict						      =					len(self.conv_dim							)
     SCREAMING_SNAKE_CASE:List[str]						      =					num_hidden_layers
     SCREAMING_SNAKE_CASE:Any						      =					intermediate_size
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					hidden_act
     SCREAMING_SNAKE_CASE:List[Any]						      =					num_attention_heads
     SCREAMING_SNAKE_CASE:Optional[int]						      =					hidden_dropout
     SCREAMING_SNAKE_CASE:List[str]						      =					attention_dropout
     SCREAMING_SNAKE_CASE:Dict						      =					activation_dropout
     SCREAMING_SNAKE_CASE:Tuple						      =					feat_proj_dropout
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					final_dropout
     SCREAMING_SNAKE_CASE:str						      =					layerdrop
     SCREAMING_SNAKE_CASE:Tuple						      =					layer_norm_eps
     SCREAMING_SNAKE_CASE:Any						      =					initializer_range
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					vocab_size
     SCREAMING_SNAKE_CASE:Dict						      =					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
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					mask_time_prob
     SCREAMING_SNAKE_CASE:Any						      =					mask_time_length
     SCREAMING_SNAKE_CASE:str						      =					mask_time_min_masks
     SCREAMING_SNAKE_CASE:Optional[int]						      =					mask_feature_prob
     SCREAMING_SNAKE_CASE:str						      =					mask_feature_length
     SCREAMING_SNAKE_CASE:Dict						      =					mask_feature_min_masks

     # ctc loss
     SCREAMING_SNAKE_CASE:int						      =					ctc_loss_reduction
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					ctc_zero_infinity

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

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

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







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









'''simple docstring'''

import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
A_       						=							[
    "kernels/rwkv/wkv_cuda.cu",
    "kernels/rwkv/wkv_op.cpp",
    "kernels/deformable_detr/ms_deform_attn.h",
    "kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh",
    "models/graphormer/algos_graphormer.pyx",
]





def    A_  ( snake_case ):
  # Test all the extensions added in the setup
  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
    A_       						=							argparse.ArgumentParser()
    parser.add_argument("--check_lib", action="store_true", help="Whether to check the build or the actual package.")
    A_       						=							parser.parse_args()
    if args.check_lib:
        A_       						=							importlib.import_module("transformers")
        A_       						=							Path(transformers_module.__file__).parent
    else:
        A_       						=							Path.cwd() / "build/lib/transformers"
    if not test_custom_files_are_present(transformers_path):
        raise ValueError("The built release does not contain the custom files. Fix this before going further!")






