






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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["image_processor", "tokenizer"]
					a_     =		"BlipImageProcessor"
					a_     =		"AutoTokenizer"



					def __init__( self					:  Dict							,		__A					:  Union[str, Any]							,		__A					:  Any	):
						snake_case__			:				List[str]       =  False
						super().__init__(__A							,		__A	)
						snake_case__			:				Any       =  self.image_processor



					def __call__( self					:  Dict							,		__A					:  ImageInput = None							,		__A					:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,		__A					:  bool = True							,		__A					:  Union[bool, str, PaddingStrategy] = False							,		__A					:  Union[bool, str, TruncationStrategy] = None							,		__A					:  Optional[int] = None							,		__A					:  int = 0							,		__A					:  Optional[int] = None							,		__A					:  Optional[bool] = None							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = True							,		__A					:  Optional[Union[str, TensorType]] = None							,		**__A					:  List[str]							,		):
						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:
							snake_case__			:				Tuple       =  self.tokenizer
							snake_case__			:				Optional[int]       =  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
						snake_case__			:				Any       =  self.image_processor(__A							,		return_tensors=__A	)

						if text is not None:
							snake_case__			:				Optional[int]       =  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:
							snake_case__			:				Optional[Any]       =  None

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

						return encoding_image_processor



					def 	_lowercase					( self					:  List[Any]							,		*__A					:  Optional[int]							,		**__A					:  Dict	):
						return self.tokenizer.batch_decode(*__A							,		**__A	)



					def 	_lowercase					( self					:  Any							,		*__A					:  List[str]							,		**__A					:  str	):
						return self.tokenizer.decode(*__A							,		**__A	)






					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				Optional[int]       =  self.tokenizer.model_input_names
						snake_case__			:				Dict       =  self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)











import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				Optional[Any]       =  [
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "_float_tensor",
	    "decoder.output_projection.weight",
	]
	for k in ignore_keys:
		state_dict.pop(snake_case_					,  snake_case_       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	snake_case__,						snake_case__			:				Optional[Any]       =  emb.weight.shape
	snake_case__			:				Tuple       =  nn.Linear(snake_case_					,  snake_case_					,  bias=snake_case_       )
	snake_case__			:				Optional[int]       =  emb.weight.data
	return lin_layer







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							Optional[Any]="facebook/mbart-large-en-ro"					,  snake_case_ :							Optional[int]=False					,  snake_case_ :							List[Any]=False       ):
	snake_case__			:				Tuple       =  torch.load(snake_case_					,  map_location="cpu"       )["model"]
	remove_ignore_keys_(snake_case_       )
	snake_case__			:				Any       =  state_dict["encoder.embed_tokens.weight"].shape[0]

	snake_case__			:				List[Any]       =  MBartConfig.from_pretrained(snake_case_					,  vocab_size=snake_case_       )
	if mbart_aa and finetuned:
		snake_case__			:				int       =  "relu"

	snake_case__			:				List[str]       =  state_dict["decoder.embed_tokens.weight"]
	snake_case__			:				Tuple       =  MBartForConditionalGeneration(snake_case_       )
	model.model.load_state_dict(snake_case_       )

	if finetuned:
		snake_case__			:				Any       =  make_linear_from_emb(model.model.shared       )

	return model


if __name__ == "__main__":
	__lowerCamelCase :      int										=				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""")
	__lowerCamelCase :      Optional[Any]										=				parser.parse_args()
	__lowerCamelCase :      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)



