






"""simple docstring"""


import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()




def 				a (     UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				List[str]		)  ->		Any:
 # Initialize PyTorch model
 __A	:       str       =	CanineConfig()
 __A	:       Union[str, Any]       =	CanineModel(UpperCamelCase_		)
 model.eval()

 print(F"Building PyTorch model from configuration: {config}"		)

 # Load weights from tf checkpoint
 load_tf_weights_in_canine(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_		)

 # Save pytorch-model (weights and configuration)
 print(F"Save PyTorch model to {pytorch_dump_path}"		)
 model.save_pretrained(UpperCamelCase_		)

 # Save tokenizer files
 __A	:       Dict       =	CanineTokenizer()
 print(F"Save tokenizer files to {pytorch_dump_path}"		)
 tokenizer.save_pretrained(UpperCamelCase_		)


if __name__ == "__main__":
   _UpperCamelCase   :						int     						= argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
   )
   parser.add_argument(
       """--pytorch_dump_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to a folder where the PyTorch model will be placed.""",
   )
   _UpperCamelCase   :						Optional[Any]     						= parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)













"""simple docstring"""


from __future__ import annotations

from typing import Any






class __a   :



  def __init__(self  :		Optional[Any]				,			lowercase_  :		Any				,			lowercase_  :		Any   )		->							None:  # noqa: A002
   __A	:       Optional[Any]       =	item
   __A	:       Union[str, Any]       =	next






class __a   :



  def __init__(self  :		Tuple   )		->							None:
   __A	:       Node | None       =	None
   __A	:       Dict       =	0



  def 						A_				(self  :		str				,			lowercase_  :		Any   )		->							None:
   __A	:       Dict       =	Node(lowercase_				,			self.head   )
   self.size += 1



  def 						A_				(self  :		Optional[int]   )		->							Any:
   # Switched 'self.is_empty()' to 'self.head is None'
   # because mypy was considering the possibility that 'self.head'
   # can be None in below else part and giving error
   if self.head is None:
    return None
   else:
    __A	:       Dict       =	self.head.item
    __A	:       List[str]       =	self.head.next
    self.size -= 1
    return item



  def 						A_				(self  :		List[str]   )		->							bool:
   return self.head is None



  def __str__(self  :		Any   )		->							str:
   if self.is_empty():
    return ""
   else:
    __A	:       List[str]       =	self.head
    __A	:       Union[str, Any]       =	''
    __A	:       list[str]       =	[]
    while iterate:
     item_list.append(str(iterate.item   )   )
     __A	:       List[str]       =	iterate.next

    __A	:       str       =	' --> '.join(lowercase_   )

    return item_str



  def __len__(self  :		Union[str, Any]   )		->							int:
   return self.size





