





import argparse
import os
import re


UpperCamelCase_												=	'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_												=	re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
UpperCamelCase_												=	re.compile(R'\s*\(\s*"(\S[^"]+)"')






def 				_UpperCAmelCase			(    A				,							A = False     ):




   '''simple docstring'''

   with open(A				,							"r"				,							encoding="utf-8"     ) as f:
      UpperCAmelCase__						=f.read()

   UpperCAmelCase__						=content.split("\n"     )
   UpperCAmelCase__						=[]
   UpperCAmelCase__						=0
   while line_idx < len(A     ):
      if _re_intro_mapping.search(lines[line_idx]     ) is not None:
         UpperCAmelCase__						=len(re.search(R"^(\s*)\S"				,							lines[line_idx]     ).groups()[0]     ) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "("     ):
            new_lines.append(lines[line_idx]     )
            line_idx += 1

         UpperCAmelCase__						=[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               UpperCAmelCase__						=line_idx
               while not lines[line_idx].startswith(" " * indent + ")"     ):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1]     )     )
            else:
               blocks.append(lines[line_idx]     )
            line_idx += 1

         # Sort blocks by their identifiers
         UpperCAmelCase__						=sorted(A				,							key=lambda A     : _re_identifier.search(A     ).groups()[0]     )
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx]     )
         line_idx += 1

   if overwrite:
      with open(A				,							"w"				,							encoding="utf-8"     ) as f:
         f.write("\n".join(A     )     )
   elif "\n".join(A     ) != content:
      return True






def 				_UpperCAmelCase			(    A = False     ):




   '''simple docstring'''

   UpperCAmelCase__						=[os.path.join(A				,							A     ) for f in os.listdir(A     ) if f.endswith(".py"     )]
   UpperCAmelCase__						=[sort_auto_mapping(A				,							overwrite=A     ) for fname in fnames]

   if not overwrite and any(A     ):
      UpperCAmelCase__						=[f for f, d in zip(A				,							A     ) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {', '.join(A     )}. Run `make style` to fix"""
          " this."     )


if __name__ == "__main__":
    UpperCamelCase_												=	argparse.ArgumentParser()
    parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
    UpperCamelCase_												=	parser.parse_args()

    sort_all_auto_mappings(not args.check_only)










import functools
import operator

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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'wavlm'

 def __init__(  self,		A_=32,		A_=768,		A_=12,		A_=12,		A_=3072,		A_="gelu",		A_=0.1,		A_=0.1,		A_=0.1,		A_=0.0,		A_=0.1,		A_=0.1,		A_=0.02,		A_=1E-5,		A_="group",		A_="gelu",		A_=(512, 512, 512, 512, 512, 512, 512),		A_=(5, 2, 2, 2, 2, 2, 2),		A_=(10, 3, 3, 3, 3, 2, 2),		A_=False,		A_=128,		A_=16,		A_=320,		A_=800,		A_=False,		A_=True,		A_=0.05,		A_=10,		A_=2,		A_=0.0,		A_=10,		A_=320,		A_=2,		A_=0.1,		A_=100,		A_=256,		A_=256,		A_=0.1,		A_="mean",		A_=False,		A_=False,		A_=256,		A_=(512, 512, 512, 512, 1500),		A_=(5, 3, 3, 1, 1),		A_=(1, 2, 3, 1, 1),		A_=512,		A_=80,		A_=0,		A_=1,		A_=2,		A_=False,		A_=3,		A_=2,		A_=3,		A_=None,		**A_,		)	->       Union[str, Any]:
    super().__init__(**A_,		pad_token_id=A_,		bos_token_id=A_,		eos_token_id=A_		)
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=feat_extract_norm
    UpperCAmelCase__						=feat_extract_activation
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=conv_bias
    UpperCAmelCase__						=num_buckets
    UpperCAmelCase__						=max_bucket_distance
    UpperCAmelCase__						=num_conv_pos_embeddings
    UpperCAmelCase__						=num_conv_pos_embedding_groups
    UpperCAmelCase__						=len(self.conv_dim		)
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=hidden_dropout
    UpperCAmelCase__						=attention_dropout
    UpperCAmelCase__						=activation_dropout
    UpperCAmelCase__						=feat_proj_dropout
    UpperCAmelCase__						=final_dropout
    UpperCAmelCase__						=layerdrop
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    UpperCAmelCase__						=num_ctc_classes
    UpperCAmelCase__						=vocab_size
    UpperCAmelCase__						=do_stable_layer_norm
    UpperCAmelCase__						=use_weighted_layer_sum
    UpperCAmelCase__						=classifier_proj_size

    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__						=apply_spec_augment
    UpperCAmelCase__						=mask_time_prob
    UpperCAmelCase__						=mask_time_length
    UpperCAmelCase__						=mask_time_min_masks
    UpperCAmelCase__						=mask_feature_prob
    UpperCAmelCase__						=mask_feature_length

    # parameters for pretraining with codevector quantized representations
    UpperCAmelCase__						=num_codevectors_per_group
    UpperCAmelCase__						=num_codevector_groups
    UpperCAmelCase__						=contrastive_logits_temperature
    UpperCAmelCase__						=num_negatives
    UpperCAmelCase__						=codevector_dim
    UpperCAmelCase__						=proj_codevector_dim
    UpperCAmelCase__						=diversity_loss_weight

    # ctc loss
    UpperCAmelCase__						=ctc_loss_reduction
    UpperCAmelCase__						=ctc_zero_infinity

    # adapter
    UpperCAmelCase__						=add_adapter
    UpperCAmelCase__						=adapter_kernel_size
    UpperCAmelCase__						=adapter_stride
    UpperCAmelCase__						=num_adapter_layers
    UpperCAmelCase__						=output_hidden_size or hidden_size

    # SequenceClassification-specific parameter. Feel free to ignore for other classes.
    UpperCAmelCase__						=classifier_proj_size

    # XVector-specific parameters. Feel free to ignore for other classes.
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=xvector_output_dim






 @property
 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    return functools.reduce(operator.mul,		self.conv_stride,		1		)



