






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class        __UpperCamelCase      (     _lowerCAmelCase   ):
	__snake_case	:List[str]   =       ['image_processor', 'tokenizer']
	__snake_case	:List[Any]   =       'BlipImageProcessor'
	__snake_case	:Tuple   =       'AutoTokenizer'







	def __init__(  self      :							Tuple       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							int			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  False
		super().__init__(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  self.image_processor







	def __call__(  self      :							List[str]       ,				_lowerCAmelCase      :							ImageInput = None       ,				_lowerCAmelCase      :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,				_lowerCAmelCase      :							bool = True       ,				_lowerCAmelCase      :							Union[bool, str, PaddingStrategy] = False       ,				_lowerCAmelCase      :							Union[bool, str, TruncationStrategy] = None       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							int = 0       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Optional[bool] = None       ,				_lowerCAmelCase      :							bool = False       ,				_lowerCAmelCase      :							bool = False       ,				_lowerCAmelCase      :							bool = False       ,				_lowerCAmelCase      :							bool = False       ,				_lowerCAmelCase      :							bool = False       ,				_lowerCAmelCase      :							bool = True       ,				_lowerCAmelCase      :							Optional[Union[str, TensorType]] = None       ,				**_lowerCAmelCase      :							Tuple       ,				)		-> BatchEncoding:
		"""simple docstring"""







		if images is None and text is None:
			raise ValueError("""You have to specify either images or text."""			)

		# Get only text
		if images is None:
			__lowercase             =  self.tokenizer
			__lowercase             =  self.tokenizer(
			    text=_lowerCAmelCase       ,				add_special_tokens=_lowerCAmelCase       ,				padding=_lowerCAmelCase       ,				truncation=_lowerCAmelCase       ,				max_length=_lowerCAmelCase       ,				stride=_lowerCAmelCase       ,				pad_to_multiple_of=_lowerCAmelCase       ,				return_attention_mask=_lowerCAmelCase       ,				return_overflowing_tokens=_lowerCAmelCase       ,				return_special_tokens_mask=_lowerCAmelCase       ,				return_offsets_mapping=_lowerCAmelCase       ,				return_token_type_ids=_lowerCAmelCase       ,				return_length=_lowerCAmelCase       ,				verbose=_lowerCAmelCase       ,				return_tensors=_lowerCAmelCase       ,				**_lowerCAmelCase       ,				)
			return text_encoding

		# add pixel_values
		__lowercase             =  self.image_processor(_lowerCAmelCase       ,				return_tensors=_lowerCAmelCase			)

		if text is not None:
			__lowercase             =  self.tokenizer(
			    text=_lowerCAmelCase       ,				add_special_tokens=_lowerCAmelCase       ,				padding=_lowerCAmelCase       ,				truncation=_lowerCAmelCase       ,				max_length=_lowerCAmelCase       ,				stride=_lowerCAmelCase       ,				pad_to_multiple_of=_lowerCAmelCase       ,				return_attention_mask=_lowerCAmelCase       ,				return_overflowing_tokens=_lowerCAmelCase       ,				return_special_tokens_mask=_lowerCAmelCase       ,				return_offsets_mapping=_lowerCAmelCase       ,				return_token_type_ids=_lowerCAmelCase       ,				return_length=_lowerCAmelCase       ,				verbose=_lowerCAmelCase       ,				return_tensors=_lowerCAmelCase       ,				**_lowerCAmelCase       ,				)
		else:
			__lowercase             =  None

		if text_encoding is not None:
			encoding_image_processor.update(_lowerCAmelCase			)

		return encoding_image_processor







	def  _a   (  self      :							Optional[int]       ,				*_lowerCAmelCase      :							Union[str, Any]       ,				**_lowerCAmelCase      :							Any			)		-> str:
		"""simple docstring"""







		return self.tokenizer.batch_decode(*_lowerCAmelCase       ,				**_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]       ,				*_lowerCAmelCase      :							Any       ,				**_lowerCAmelCase      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		return self.tokenizer.decode(*_lowerCAmelCase       ,				**_lowerCAmelCase			)







	@property
	# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self.tokenizer.model_input_names
		__lowercase             =  self.image_processor.model_input_names
		return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)









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()
__UpperCamelCase      :      str     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  RobertaPreLayerNormConfig.from_pretrained(
	    lowerCamelCase				,  architectures=["""RobertaPreLayerNormForMaskedLM"""]					)

	# convert state_dict
	__lowercase             =  torch.load(hf_hub_download(repo_id=lowerCamelCase				,  filename="""pytorch_model.bin"""					)					)
	__lowercase             =  {}
	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             =  """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             =  tensor_value

	__lowercase             =  RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=lowerCamelCase				,  config=lowerCamelCase				,  state_dict=lowerCamelCase					)
	model.save_pretrained(lowerCamelCase					)

	# convert tokenizer
	__lowercase             =  AutoTokenizer.from_pretrained(lowerCamelCase					)
	tokenizer.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
						__UpperCamelCase      :      List[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."""
						)
						__UpperCamelCase      :      Dict     						=						parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

