






'''simple docstring'''





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 (				_lowercase ):







		"""simple docstring"""



		snake_case    =   ['''image_processor''', '''tokenizer''']
		snake_case    =   '''BlipImageProcessor'''
		snake_case    =   ('''BertTokenizer''', '''BertTokenizerFast''')
		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Any		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      False
						super().__init__(A_						,A_		)
						A	 =      self.image_processor
		def __call__(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       ImageInput = None						,_SCREAMING_SNAKE_CASE		:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, PaddingStrategy] = False						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, TruncationStrategy] = None						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       int = 0						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       Optional[bool] = None						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, TensorType]] = None						,**_SCREAMING_SNAKE_CASE		:       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:
										A	 =      self.tokenizer
										A	 =      self.tokenizer(
										    text=A_						,add_special_tokens=A_						,padding=A_						,truncation=A_						,max_length=A_						,stride=A_						,pad_to_multiple_of=A_						,return_attention_mask=A_						,return_overflowing_tokens=A_						,return_special_tokens_mask=A_						,return_offsets_mapping=A_						,return_token_type_ids=A_						,return_length=A_						,verbose=A_						,return_tensors=A_						,**A_						,)
										return text_encoding

						# add pixel_values
						A	 =      self.image_processor(A_						,return_tensors=A_		)

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

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

						return encoding_image_processor
		def       A(  self		:       int						,*_SCREAMING_SNAKE_CASE		:       Any						,**_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   Union[str, Any]:


						'''simple docstring'''
						return self.tokenizer.batch_decode(*A_						,**A_		)
		def       A(  self		:       List[str]						,*_SCREAMING_SNAKE_CASE		:       str						,**_SCREAMING_SNAKE_CASE		:       Any		)    ->   Any:


						'''simple docstring'''
						return self.tokenizer.decode(*A_						,**A_		)







		@property
		def       A(  self		:       Union[str, Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      self.tokenizer.model_input_names
						A	 =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				if "model" in orig_key:
								A	 =      orig_key.replace('model.',						''   )
				if "norm1" in orig_key:
								A	 =      orig_key.replace('norm1',						'attention.output.LayerNorm'   )
				if "norm2" in orig_key:
								A	 =      orig_key.replace('norm2',						'output.LayerNorm'   )
				if "norm" in orig_key:
								A	 =      orig_key.replace('norm',						'LayerNorm'   )
				if "transformer" in orig_key:
								A	 =      orig_key.split('.'   )[0].split('_'   )[-1]
								A	 =      orig_key.replace(f'transformer_{layer_num}',						f'encoder.layer.{layer_num}'   )
				if "mha.attn" in orig_key:
								A	 =      orig_key.replace('mha.attn',						'attention.self'   )
				if "mha" in orig_key:
								A	 =      orig_key.replace('mha',						'attention'   )
				if "W_q" in orig_key:
								A	 =      orig_key.replace('W_q',						'self.query'   )
				if "W_k" in orig_key:
								A	 =      orig_key.replace('W_k',						'self.key'   )
				if "W_v" in orig_key:
								A	 =      orig_key.replace('W_v',						'self.value'   )
				if "ff1" in orig_key:
								A	 =      orig_key.replace('ff1',						'intermediate.dense'   )
				if "ff2" in orig_key:
								A	 =      orig_key.replace('ff2',						'output.dense'   )
				if "ff" in orig_key:
								A	 =      orig_key.replace('ff',						'output.dense'   )
				if "mlm_class" in orig_key:
								A	 =      orig_key.replace('mlm.mlm_class',						'cls.predictions.decoder'   )
				if "mlm" in orig_key:
								A	 =      orig_key.replace('mlm',						'cls.predictions.transform'   )
				if "cls" not in orig_key:
								A	 =      'yoso.' + orig_key

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      convert_checkpoint_helper(config.max_position_embeddings,						UpperCAmelCase   )

				print(model.load_state_dict(UpperCAmelCase   )   )
				model.eval()
				model.save_pretrained(UpperCAmelCase   )

				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





