




import unittest

from transformers import DonutProcessor


_A				     =						'''naver-clova-ix/donut-base'''


class 				_lowerCAmelCase		(  unittest.TestCase						):




				def      __a		(			self				)     ->   Dict:
											SCREAMING_SNAKE_CASE      :			Optional[Any]						  =DonutProcessor.from_pretrained(lowerCAmelCase__				)







				def      __a		(			self				)     ->   List[Any]:
											SCREAMING_SNAKE_CASE      :			Optional[Any]						  ={
											    "name": "John Doe",
											    "age": "99",
											    "city": "Atlanta",
											    "state": "GA",
											    "zip": "30301",
											    "phone": "123-4567",
											    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
											}

											SCREAMING_SNAKE_CASE      :			Tuple						  =(
											    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
											    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
											    "<s_nicknames><s_nickname>Johnny</s_nickname>"
											    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
											)
											SCREAMING_SNAKE_CASE      :			List[Any]						  =self.processor.tokenajson(lowerCAmelCase__				)

											self.assertDictEqual(lowerCAmelCase__		,		lowerCAmelCase__				)













"""simple docstring"""




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a		:					Optional[int]	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase       ,			architectures=["RobertaPreLayerNormForMaskedLM"]   )

	# convert state_dict
	a :						Any      =							torch.load(hf_hub_download(repo_id=_lowercase       ,			filename="pytorch_model.bin"   )   )
	a :						int      =							{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("roberta."   ):
			a :						List[Any]      =							"roberta_prelayernorm." + tensor_key[len("roberta."   ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(".self.LayerNorm.weight"   ) or tensor_key.endswith(".self.LayerNorm.bias"   ):
			continue

		a :						Dict      =							tensor_value

	a :						Optional[Any]      =							RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase       ,			config=_lowercase       ,			state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	a :						List[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
		a		:					Any	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint-repo''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		a		:					Tuple	      =     parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


