


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					['''image_processor''', '''tokenizer''']
					A__     =					'''BlipImageProcessor'''
					A__     =					'''AutoTokenizer'''

					def __init__(self       :		List[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Any						)		->  Any:




							"""simple docstring"""


							super().__init__(_UpperCAmelCase ,   _UpperCAmelCase						)

							# add QFormer tokenizer
							lowercase__	     =						qformer_tokenizer

					def __call__(self       :		Optional[Any] ,   _UpperCAmelCase       :		ImageInput = None ,   _UpperCAmelCase       :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,   _UpperCAmelCase       :		bool = True ,   _UpperCAmelCase       :		Union[bool, str, PaddingStrategy] = False ,   _UpperCAmelCase       :		Union[bool, str, TruncationStrategy] = None ,   _UpperCAmelCase       :		Optional[int] = None ,   _UpperCAmelCase       :		int = 0 ,   _UpperCAmelCase       :		Optional[int] = None ,   _UpperCAmelCase       :		Optional[bool] = None ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = True ,   _UpperCAmelCase       :		Optional[Union[str, TensorType]] = None ,   **_UpperCAmelCase       :		Dict ,   )		->  BatchFeature:




							"""simple docstring"""


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

							lowercase__	     =						BatchFeature()

							if text is not None:
									lowercase__	     =						self.tokenizer(
									    text=_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   stride=_UpperCAmelCase ,   pad_to_multiple_of=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   return_overflowing_tokens=_UpperCAmelCase ,   return_special_tokens_mask=_UpperCAmelCase ,   return_offsets_mapping=_UpperCAmelCase ,   return_token_type_ids=_UpperCAmelCase ,   return_length=_UpperCAmelCase ,   verbose=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
									encoding.update(_UpperCAmelCase						)
									lowercase__	     =						self.qformer_tokenizer(
									    text=_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   stride=_UpperCAmelCase ,   pad_to_multiple_of=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   return_overflowing_tokens=_UpperCAmelCase ,   return_special_tokens_mask=_UpperCAmelCase ,   return_offsets_mapping=_UpperCAmelCase ,   return_token_type_ids=_UpperCAmelCase ,   return_length=_UpperCAmelCase ,   verbose=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
									lowercase__	     =						qformer_text_encoding.pop("""input_ids"""						)
									lowercase__	     =						qformer_text_encoding.pop("""attention_mask"""						)

							if images is not None:
									lowercase__	     =						self.image_processor(_UpperCAmelCase ,   return_tensors=_UpperCAmelCase						)
									encoding.update(_UpperCAmelCase						)

							return encoding

					def     lowerCamelCase__	(self       :		Tuple ,   *_UpperCAmelCase       :		Any ,   **_UpperCAmelCase       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		str ,   *_UpperCAmelCase       :		Optional[int] ,   **_UpperCAmelCase       :		Union[str, Any]						)		->  int:




							"""simple docstring"""


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

					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def     lowerCamelCase__	(self       :		List[str]						)		->  Dict:




							"""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						)						)

					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		int ,   **_UpperCAmelCase       :		Optional[int]						)		->  int:




							"""simple docstring"""


							if os.path.isfile(_UpperCAmelCase						):
									raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''						)
							os.makedirs(_UpperCAmelCase ,   exist_ok=_UpperCAmelCase						)
							lowercase__	     =						os.path.join(_UpperCAmelCase ,   """qformer_tokenizer"""						)
							self.qformer_tokenizer.save_pretrained(_UpperCAmelCase						)
							return super().save_pretrained(_UpperCAmelCase ,   **_UpperCAmelCase						)


					@classmethod
					def     lowerCamelCase__	(cls       :		Tuple ,   _UpperCAmelCase       :		int ,   **_UpperCAmelCase       :		Optional[int]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						AutoTokenizer.from_pretrained(_UpperCAmelCase ,   subfolder="""qformer_tokenizer"""						)
							lowercase__	     =						cls._get_arguments_from_pretrained(_UpperCAmelCase ,   **_UpperCAmelCase						)
							args.append(_UpperCAmelCase						)
							return cls(*_UpperCAmelCase						)





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """_float_tensor""",
		    """decoder.output_projection.weight""",
		]
		for k in ignore_keys:
				state_dict.pop(__magic_name__       ,       __magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						emb.weight.shape
		lowercase__	     =						nn.Linear(__magic_name__       ,       __magic_name__       ,       bias=__magic_name__	)
		lowercase__	     =						emb.weight.data
		return lin_layer






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str="facebook/mbart-large-en-ro"       ,       __magic_name__	:				Any=False       ,       __magic_name__	:				int=False	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						torch.load(__magic_name__       ,       map_location="""cpu"""	)["""model"""]
		remove_ignore_keys_(__magic_name__	)
		lowercase__	     =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

		lowercase__	     =						MBartConfig.from_pretrained(__magic_name__       ,       vocab_size=__magic_name__	)
		if mbart_aa and finetuned:
				lowercase__	     =						"""relu"""

		lowercase__	     =						state_dict["""decoder.embed_tokens.weight"""]
		lowercase__	     =						MBartForConditionalGeneration(__magic_name__	)
		model.model.load_state_dict(__magic_name__	)

		if finetuned:
				lowercase__	     =						make_linear_from_emb(model.model.shared	)

		return model


if __name__ == "__main__":
	A			:						Optional[Any]        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
	)
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument(
	    '--hf_config',
	    default='facebook/mbart-large-cc25',
	    type=str,
	    help='Which huggingface architecture to use: mbart-large',
	)
	parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
	parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
	A			:						Any        =  parser.parse_args()
	A			:						str        =  convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)

