






'''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:      Union[str, Any]  				=				logging.get_logger(__name__)






def 	_UpperCAmelCase    (  a		:	str						,			a		:	str							)			->			Union[str, Any]:





						"""simple docstring"""
						lowercase_      :   Dict        =			RobertaPreLayerNormConfig.from_pretrained(
						    a						,			architectures=['RobertaPreLayerNormForMaskedLM']							)

						# convert state_dict
						lowercase_      :   Dict        =			torch.load(hf_hub_download(repo_id=a						,			filename='pytorch_model.bin'							)							)
						lowercase_      :   Union[str, Any]        =			{}
						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.'							):
																		lowercase_      :   Optional[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

												lowercase_      :   int        =			tensor_value

						lowercase_      :   str        =			RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=a						,			config=a						,			state_dict=a							)
						model.save_pretrained(a							)

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


if __name__ == "__main__":
						A:      str  				=				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:      str  				=				parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)








'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A:      Dict  				=				logging.get_logger(__name__)

A:      Optional[Any]  				=				{
    "google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json",
    # See all ViT models at https://huggingface.co/models?filter=vit
}






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  'vit'
	def __init__(							self  ,      _lowercase=768  ,      _lowercase=12  ,      _lowercase=12  ,      _lowercase=3072  ,      _lowercase="gelu"  ,      _lowercase=0.0  ,      _lowercase=0.0  ,      _lowercase=0.02  ,      _lowercase=1E-1_2  ,      _lowercase=224  ,      _lowercase=16  ,      _lowercase=3  ,      _lowercase=True  ,      _lowercase=16  ,      **_lowercase  ,      )						->  List[str]:
							super().__init__(**_lowercase       )

							lowercase_      :   Optional[int]        =			hidden_size
							lowercase_      :   str        =			num_hidden_layers
							lowercase_      :   str        =			num_attention_heads
							lowercase_      :   int        =			intermediate_size
							lowercase_      :   List[Any]        =			hidden_act
							lowercase_      :   Any        =			hidden_dropout_prob
							lowercase_      :   List[str]        =			attention_probs_dropout_prob
							lowercase_      :   str        =			initializer_range
							lowercase_      :   List[str]        =			layer_norm_eps
							lowercase_      :   Any        =			image_size
							lowercase_      :   Tuple        =			patch_size
							lowercase_      :   Optional[Any]        =			num_channels
							lowercase_      :   str        =			qkv_bias
							lowercase_      :   List[str]        =			encoder_stride






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Any        =  version.parse('1.11'				)
	@property
	def        lowerCamelCase__		(							self       )						->  Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]       )







	@property
	def        lowerCamelCase__		(							self       )						->  float:
							return 1E-4
