




import functools
import operator

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


lowerCAmelCase_						 =					logging.get_logger(__name__)

lowerCAmelCase_						 =					{
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 		_A   (    A_ ):
  _UpperCamelCase  :     Union[str, Any]					=     "sew"






  def __init__(    self						:      Dict ,		_A						:      Tuple=32 ,		_A						:      Dict=768 ,		_A						:      Optional[int]=12 ,		_A						:      str=12 ,		_A						:      Union[str, Any]=3_072 ,		_A						:      List[str]=2 ,		_A						:      List[str]="gelu" ,		_A						:      str=0.1 ,		_A						:      Union[str, Any]=0.1 ,		_A						:      Tuple=0.1 ,		_A						:      Optional[int]=0.0 ,		_A						:      str=0.1 ,		_A						:      Optional[int]=0.1 ,		_A						:      int=0.02 ,		_A						:      Tuple=1E-5 ,		_A						:      Union[str, Any]="group" ,		_A						:      List[Any]="gelu" ,		_A						:      int=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,		_A						:      Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,		_A						:      Union[str, Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,		_A						:      str=False ,		_A						:      str=128 ,		_A						:      Union[str, Any]=16 ,		_A						:      Optional[int]=True ,		_A						:      Dict=0.05 ,		_A						:      Optional[Any]=10 ,		_A						:      str=2 ,		_A						:      List[Any]=0.0 ,		_A						:      Tuple=10 ,		_A						:      List[str]=0 ,		_A						:      str="mean" ,		_A						:      List[Any]=False ,		_A						:      Union[str, Any]=False ,		_A						:      Dict=256 ,		_A						:      str=0 ,		_A						:      Optional[int]=1 ,		_A						:      Dict=2 ,		**_A						:      str ,		)						->       int:






       """simple docstring"""







       super().__init__(**snake_case__ ,		pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ )
       lowercase   :  Any        =					hidden_size
       lowercase   :  Union[str, Any]        =					feat_extract_norm
       lowercase   :  Tuple        =					feat_extract_activation
       lowercase   :  List[Any]        =					list(snake_case__ )
       lowercase   :  str        =					list(snake_case__ )
       lowercase   :  Dict        =					list(snake_case__ )
       lowercase   :  List[str]        =					conv_bias
       lowercase   :  List[Any]        =					num_conv_pos_embeddings
       lowercase   :  List[str]        =					num_conv_pos_embedding_groups
       lowercase   :  Optional[Any]        =					len(self.conv_dim )
       lowercase   :  Optional[int]        =					num_hidden_layers
       lowercase   :  int        =					intermediate_size
       lowercase   :  int        =					squeeze_factor
       lowercase   :  Optional[int]        =					hidden_act
       lowercase   :  List[str]        =					num_attention_heads
       lowercase   :  int        =					hidden_dropout
       lowercase   :  Any        =					attention_dropout
       lowercase   :  str        =					activation_dropout
       lowercase   :  str        =					feat_proj_dropout
       lowercase   :  List[Any]        =					final_dropout
       lowercase   :  Any        =					layerdrop
       lowercase   :  int        =					layer_norm_eps
       lowercase   :  Optional[int]        =					initializer_range
       lowercase   :  Optional[Any]        =					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
       lowercase   :  Optional[Any]        =					apply_spec_augment
       lowercase   :  Tuple        =					mask_time_prob
       lowercase   :  Dict        =					mask_time_length
       lowercase   :  Optional[int]        =					mask_time_min_masks
       lowercase   :  Optional[int]        =					mask_feature_prob
       lowercase   :  Dict        =					mask_feature_length
       lowercase   :  Any        =					mask_feature_min_masks

       # ctc loss
       lowercase   :  Any        =					ctc_loss_reduction
       lowercase   :  Optional[int]        =					ctc_zero_infinity

       # sequence classification
       lowercase   :  int        =					use_weighted_layer_sum
       lowercase   :  Dict        =					classifier_proj_size






  @property
  def    __a     (    self						:      Union[str, Any] )						->       int:






       """simple docstring"""







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

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Union[str, Any]       ):
       # Initialise PyTorch model
       snake_case			:  Optional[int]     							= BertConfig.from_json_file(__lowerCamelCase       )
       print(f"""Building PyTorch model from configuration: {config}"""       )
       snake_case			:  Any     							= BertForPreTraining(__lowerCamelCase       )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
       torch.save(model.state_dict()							,   __lowerCamelCase       )


if __name__ == "__main__":
   __lowerCamelCase				=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--bert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained BERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   __lowerCamelCase				=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





