"""simple docstring"""






# Lint as: python3
import itertools
import os
import re


__A			   = re.compile(R"([A-Z]+)([A-Z][a-z])")
__A			   = re.compile(R"([a-z\d])([A-Z])")

__A			   = re.compile(R"(?<!_)_(?!_)")
__A			   = re.compile(R"(_{2,})")

__A			   = R"^\w+(\.\w+)*$"

__A			   = R"<>:/\|?*"



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[str]:
   lowercase__:   Optional[Any]					=      _uppercase_uppercase_re.sub(R'''\1_\2'''       ,    __UpperCAmelCase )
   lowercase__:   List[Any]					=      _lowercase_uppercase_re.sub(R'''\1_\2'''       ,    __UpperCAmelCase )
   return name.lower()



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Optional[int]:
   lowercase__:   List[Any]					=      _single_underscore_re.split(__UpperCAmelCase )
   lowercase__:   Optional[int]					=      [_multiple_underscores_re.split(__UpperCAmelCase ) for n in name]
   return "".join(n.capitalize() for n in itertools.chain.from_iterable(__UpperCAmelCase ) if n != '''''' )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[Any]:
   if os.path.basename(__UpperCAmelCase ) != name:
      raise ValueError(F"""Should be a dataset name, not a path: {name}""" )
   return camelcase_to_snakecase(__UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[Any]:
   if os.path.basename(__UpperCAmelCase ) != name:
      raise ValueError(F"""Should be a dataset name, not a path: {name}""" )
   if not re.match(_split_re       ,    __UpperCAmelCase ):
      raise ValueError(F"""Split name should match '{_split_re}'' but got '{split}'.""" )
   return F"""{filename_prefix_for_name(__UpperCAmelCase )}-{split}"""



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=None )  ->  Optional[int]:
   lowercase__:   Optional[Any]					=      filename_prefix_for_split(__UpperCAmelCase       ,    __UpperCAmelCase )
   if filetype_suffix:
      prefix += F""".{filetype_suffix}"""
   lowercase__:   List[Any]					=      os.path.join(__UpperCAmelCase       ,    __UpperCAmelCase )
   return F"""{filepath}*"""




def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None )  ->  int:
   lowercase__:   List[str]					=      filename_prefix_for_split(__UpperCAmelCase       ,    __UpperCAmelCase )
   lowercase__:   str					=      os.path.join(__UpperCAmelCase       ,    __UpperCAmelCase )

   if shard_lengths:
      lowercase__:   Union[str, Any]					=      len(__UpperCAmelCase )
      lowercase__:   str					=      [F"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(__UpperCAmelCase )]
      if filetype_suffix:
         lowercase__:   Dict					=      [filename + F""".{filetype_suffix}""" for filename in filenames]
      return filenames
   else:
      lowercase__:   str					=      prefix
      if filetype_suffix:
         filename += F""".{filetype_suffix}"""
      return [filename]

"""simple docstring"""






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[Any]:
   # Initialise PyTorch model
   lowercase__:   Optional[Any]					=      FunnelConfig.from_json_file(__UpperCAmelCase )
   print(F"""Building PyTorch model from configuration: {config}""" )
   lowercase__:   List[Any]					=      FunnelBaseModel(__UpperCAmelCase ) if base_model else FunnelModel(__UpperCAmelCase )

   # Load weights from tf checkpoint
   load_tf_weights_in_funnel(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )

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


if __name__ == "__main__":
       __A			   = 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(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
       )
       __A			   = parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
       )
