



"""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_			   =					logging.get_logger(__name__)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					RobertaPreLayerNormConfig.from_pretrained(
					    SCREAMING_SNAKE_CASE__				,    architectures=["""RobertaPreLayerNormForMaskedLM"""]			)

					# convert state_dict
					snake_case_					:		Optional[Any] 					=					torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE__				,    filename="""pytorch_model.bin"""			)			)
					snake_case_					:		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."""			):
															snake_case_					:		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

										snake_case_					:		Dict 					=					tensor_value

					snake_case_					:		Tuple 					=					RobertaPreLayerNormForMaskedLM.from_pretrained(
					    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE__				,    config=SCREAMING_SNAKE_CASE__				,    state_dict=SCREAMING_SNAKE_CASE__			)
					model.save_pretrained(SCREAMING_SNAKE_CASE__			)

					# convert tokenizer
					snake_case_					:		List[Any] 					=					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__			)
					tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__			)


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





"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""image_processor""", """tokenizer"""]
						_A		:							str							       =		"""ChineseCLIPImageProcessor"""
						_A		:							Tuple							       =		("""BertTokenizer""", """BertTokenizerFast""")







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):
											snake_case_					:		Any 					=					None
											if "feature_extractor" in kwargs:
																warnings.warn(
																    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																    """ instead."""   ,		lowercase__   ,		)
																snake_case_					:		Optional[Any] 					=					kwargs.pop("""feature_extractor"""       )

											snake_case_					:		str 					=					image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																raise ValueError("""You need to specify an `image_processor`."""       )
											if tokenizer is None:
																raise ValueError("""You need to specify a `tokenizer`."""       )

											super().__init__(lowercase__   ,		lowercase__       )
											snake_case_					:		Union[str, Any] 					=					self.image_processor







						def __call__(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):

											if text is None and images is None:
																raise ValueError("""You have to specify either text or images. Both cannot be none."""       )

											if text is not None:
																snake_case_					:		Any 					=					self.tokenizer(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if images is not None:
																snake_case_					:		Tuple 					=					self.image_processor(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if text is not None and images is not None:
																snake_case_					:		List[Any] 					=					image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**lowercase__       )   ,		tensor_type=lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.batch_decode(*lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.decode(*lowercase__   ,		**lowercase__       )







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.tokenizer.model_input_names
											snake_case_					:		Union[str, Any] 					=					self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







						@property
						def 						__UpperCamelCase							(self       ):
											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,		lowercase__   ,		)
											return self.image_processor_class




