
import functools
import operator

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


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Optional[Any]         =   '''sew-d'''

       def __init__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Tuple=32					,   _SCREAMING_SNAKE_CASE: Optional[Any]=768					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=12					,   _SCREAMING_SNAKE_CASE: Any=12					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=3072					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=2					,   _SCREAMING_SNAKE_CASE: Optional[Any]=512					,   _SCREAMING_SNAKE_CASE: Dict=256					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=True					,   _SCREAMING_SNAKE_CASE: List[Any]=True					,   _SCREAMING_SNAKE_CASE: List[str]=("p2c", "c2p")					,   _SCREAMING_SNAKE_CASE: List[Any]="layer_norm"					,   _SCREAMING_SNAKE_CASE: Union[str, Any]="gelu_python"					,   _SCREAMING_SNAKE_CASE: List[Any]=0.1					,   _SCREAMING_SNAKE_CASE: Tuple=0.1					,   _SCREAMING_SNAKE_CASE: List[Any]=0.1					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=0.0					,   _SCREAMING_SNAKE_CASE: int=0.1					,   _SCREAMING_SNAKE_CASE: str=0.02					,   _SCREAMING_SNAKE_CASE: int=1e-7					,   _SCREAMING_SNAKE_CASE: Optional[int]=1e-5					,   _SCREAMING_SNAKE_CASE: Any="group"					,   _SCREAMING_SNAKE_CASE: Any="gelu"					,   _SCREAMING_SNAKE_CASE: Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)					,   _SCREAMING_SNAKE_CASE: List[str]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)					,   _SCREAMING_SNAKE_CASE: Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)					,   _SCREAMING_SNAKE_CASE: int=False					,   _SCREAMING_SNAKE_CASE: List[Any]=128					,   _SCREAMING_SNAKE_CASE: Optional[int]=16					,   _SCREAMING_SNAKE_CASE: Dict=True					,   _SCREAMING_SNAKE_CASE: Optional[Any]=0.05					,   _SCREAMING_SNAKE_CASE: List[str]=10					,   _SCREAMING_SNAKE_CASE: List[str]=2					,   _SCREAMING_SNAKE_CASE: Any=0.0					,   _SCREAMING_SNAKE_CASE: Optional[int]=10					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=0					,   _SCREAMING_SNAKE_CASE: Dict="mean"					,   _SCREAMING_SNAKE_CASE: List[str]=False					,   _SCREAMING_SNAKE_CASE: List[str]=False					,   _SCREAMING_SNAKE_CASE: Dict=256					,   _SCREAMING_SNAKE_CASE: Optional[Any]=0					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=1					,   _SCREAMING_SNAKE_CASE: Dict=2					,   **_SCREAMING_SNAKE_CASE: int					,   )       -> Tuple:




              """simple docstring"""


              super().__init__(**_SCREAMING_SNAKE_CASE					,   pad_token_id=_SCREAMING_SNAKE_CASE					,   bos_token_id=_SCREAMING_SNAKE_CASE					,   eos_token_id=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  hidden_size
              UpperCamelCase_          =  feat_extract_norm
              UpperCamelCase_          =  feat_extract_activation
              UpperCamelCase_          =  list(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  list(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  list(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  conv_bias
              UpperCamelCase_          =  num_conv_pos_embeddings
              UpperCamelCase_          =  num_conv_pos_embedding_groups
              UpperCamelCase_          =  len(self.conv_dim		)
              UpperCamelCase_          =  num_hidden_layers
              UpperCamelCase_          =  intermediate_size
              UpperCamelCase_          =  squeeze_factor
              UpperCamelCase_          =  max_position_embeddings
              UpperCamelCase_          =  position_buckets
              UpperCamelCase_          =  share_att_key
              UpperCamelCase_          =  relative_attention
              UpperCamelCase_          =  norm_rel_ebd
              UpperCamelCase_          =  list(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  hidden_act
              UpperCamelCase_          =  num_attention_heads
              UpperCamelCase_          =  hidden_dropout
              UpperCamelCase_          =  attention_dropout
              UpperCamelCase_          =  activation_dropout
              UpperCamelCase_          =  feat_proj_dropout
              UpperCamelCase_          =  final_dropout
              UpperCamelCase_          =  layer_norm_eps
              UpperCamelCase_          =  feature_layer_norm_eps
              UpperCamelCase_          =  initializer_range
              UpperCamelCase_          =  vocab_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)`,"
                         f'''but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'''
                         f'''= {len(self.conv_stride		)}`, `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
              UpperCamelCase_          =  mask_feature_min_masks

              # ctc loss
              UpperCamelCase_          =  ctc_loss_reduction
              UpperCamelCase_          =  ctc_zero_infinity

              # sequence classification
              UpperCamelCase_          =  use_weighted_layer_sum
              UpperCamelCase_          =  classifier_proj_size



       @property
       def lowercase (		self: Optional[Any]		)       -> Tuple:




              """simple docstring"""


              return functools.reduce(operator.mul					,   self.conv_stride					,   1		)








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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_ = False  )     ->		Union[str, Any]:
       with open(UpperCamelCase_					,		"r"					,		encoding="utf-8"  ) as f:
              UpperCamelCase_          =  f.read()

       UpperCamelCase_          =  content.split("\n"  )
       UpperCamelCase_          =  []
       UpperCamelCase_          =  0
       while line_idx < len(UpperCamelCase_  ):
              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(UpperCamelCase_					,		key=lambda UpperCamelCase_  : _re_identifier.search(UpperCamelCase_  ).groups()[0]  )
                     new_lines += blocks
              else:
                     new_lines.append(lines[line_idx]  )
                     line_idx += 1

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




def  lowerCAmelCase_ (							UpperCamelCase_ = False  )     ->		List[Any]:
       UpperCamelCase_          =  [os.path.join(UpperCamelCase_					,		UpperCamelCase_  ) for f in os.listdir(UpperCamelCase_  ) if f.endswith(".py"  )]
       UpperCamelCase_          =  [sort_auto_mapping(UpperCamelCase_					,		overwrite=UpperCamelCase_  ) for fname in fnames]

       if not overwrite and any(UpperCamelCase_  ):
              UpperCamelCase_          =  [f for f, d in zip(UpperCamelCase_					,		UpperCamelCase_  ) if d]
              raise ValueError(
                  F'''The following files have auto mappings that need sorting: {", ".join(UpperCamelCase_  )}. 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)

