






from typing import List, Optional, Union

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



class   lowerCamelCase      (lowerCAmelCase_			):







		'''simple docstring'''



		_snake_case    :    int       						=  ['''image_processor''', '''tokenizer''']
		_snake_case    :    Optional[int]       						=  '''BridgeTowerImageProcessor'''
		_snake_case    :    List[Any]       						=  ('''RobertaTokenizer''', '''RobertaTokenizerFast''')



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Optional[int]:
							super().__init__(__snake_case				,		__snake_case   )



		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = True				,		_UpperCamelCase = False				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = 0				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = False				,		_UpperCamelCase = False				,		_UpperCamelCase = False				,		_UpperCamelCase = False				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							BatchEncoding:
							UpperCAmelCase_    :      int            =						self.tokenizer(
							    text=__snake_case				,		add_special_tokens=__snake_case				,		padding=__snake_case				,		truncation=__snake_case				,		max_length=__snake_case				,		stride=__snake_case				,		pad_to_multiple_of=__snake_case				,		return_token_type_ids=__snake_case				,		return_attention_mask=__snake_case				,		return_overflowing_tokens=__snake_case				,		return_special_tokens_mask=__snake_case				,		return_offsets_mapping=__snake_case				,		return_length=__snake_case				,		verbose=__snake_case				,		return_tensors=__snake_case				,		**__snake_case				,		)
							# add pixel_values + pixel_mask
							UpperCAmelCase_    :      Optional[Any]            =						self.image_processor(
							    __snake_case				,		return_tensors=__snake_case				,		do_normalize=__snake_case				,		do_center_crop=__snake_case				,		**__snake_case   )
							encoding.update(__snake_case   )

							return encoding



		def 					__UpperCAmelCase			(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							str:
							return self.tokenizer.batch_decode(*__snake_case				,		**__snake_case   )



		def 					__UpperCAmelCase			(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							Optional[Any]:
							return self.tokenizer.decode(*__snake_case				,		**__snake_case   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							UpperCAmelCase_    :      Tuple            =						self.tokenizer.model_input_names
							UpperCAmelCase_    :      Optional[int]            =						self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =[
			    'decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(_snake_case       ,     _snake_case	)






def        UpperCamelCase_(							_snake_case   :  Any	):




			"""simple docstring"""






			__a      ,    __a        =emb.weight.shape
			__a        =nn.Linear(_snake_case       ,     _snake_case       ,     bias=_snake_case	)
			__a        =emb.weight.data
			return lin_layer






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        =Namespace(**checkpoint['cfg']['model']	)
			__a        =checkpoint['model']
			remove_ignore_keys_(_snake_case	)
			__a        =state_dict['decoder.embed_tokens.weight'].shape[0]

			__a        ={key.replace('decoder'       ,     'model'	): val for key, val in state_dict.items()}

			__a        =XGLMConfig(
			    vocab_size=_snake_case       ,     max_position_embeddings=args.max_target_positions       ,     num_layers=args.decoder_layers       ,     attention_heads=args.decoder_attention_heads       ,     ffn_dim=args.decoder_ffn_embed_dim       ,     d_model=args.decoder_embed_dim       ,     layerdrop=args.decoder_layerdrop       ,     dropout=args.dropout       ,     attention_dropout=args.attention_dropout       ,     activation_dropout=args.activation_dropout       ,     activation_function='gelu'       ,     scale_embedding=not args.no_scale_embedding       ,     tie_word_embeddings=args.share_decoder_input_output_embed       ,     )

			__a        =XGLMForCausalLM(_snake_case	)
			__a        =model.load_state_dict(_snake_case       ,     strict=_snake_case	)
			print(_snake_case	)
			__a        =make_linear_from_emb(model.model.embed_tokens	)

			return model


if __name__ == "__main__":
						_lowerCAmelCase					:			str  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("fairseq_path", type=str, help="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.")
						_lowerCAmelCase					:			str  				=			parser.parse_args()
						_lowerCAmelCase					:			Union[str, Any]  				=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)






