"""simple docstring"""




import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


__lowerCamelCase									=	collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							print(F'''Reading saved model from {model_file}'''					)
							A__						  =				torch.load(UpperCamelCase__							,     map_location=lambda UpperCamelCase__							,     UpperCamelCase__					: default_restore_location(UpperCamelCase__							,     'cpu'					)					)
							return CheckpointState(**UpperCamelCase__					)
class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase  )       ->					Any:
													A__						  =				src_file





						def 					snake_case__						(							self  )       ->					Optional[Any]:
													raise NotImplementedError





						@staticmethod
						def 					snake_case__						(							__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase  )       ->					"DPRState":
													if comp_type.startswith('c'  ):
																				return DPRContextEncoderState(*__UpperCAmelCase		,**__UpperCAmelCase  )
													if comp_type.startswith('q'  ):
																				return DPRQuestionEncoderState(*__UpperCAmelCase		,**__UpperCAmelCase  )
													if comp_type.startswith('r'  ):
																				return DPRReaderState(*__UpperCAmelCase		,**__UpperCAmelCase  )
													else:
																				raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'  )






class      UpperCamelCase__(      __A			):





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__						  =				DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'  )[0]  )  )
													print(f'''Loading DPR biencoder from {self.src_file}'''  )
													A__						  =				load_states_from_checkpoint(self.src_file  )
													A__  ,	A__						  =				model.ctx_encoder, 'ctx_model.'
													# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
													A__						  =				{'bert_model.embeddings.position_ids': model.ctx_encoder.bert_model.embeddings.position_ids}
													for key, value in saved_state.model_dict.items():
																				if key.startswith(__UpperCAmelCase  ):
																											A__						  =				key[len(__UpperCAmelCase  ) :]
																											if not key.startswith('encode_proj.'  ):
																																		A__						  =				'bert_model.' + key
																											A__						  =				value
													encoder.load_state_dict(__UpperCAmelCase  )
													return model






class      UpperCamelCase__(      __A			):





						def 					snake_case__						(							self  )       ->					Optional[Any]:
													A__						  =				DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'  )[0]  )  )
													print(f'''Loading DPR biencoder from {self.src_file}'''  )
													A__						  =				load_states_from_checkpoint(self.src_file  )
													A__  ,	A__						  =				model.question_encoder, 'question_model.'
													# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
													A__						  =				{'bert_model.embeddings.position_ids': model.question_encoder.bert_model.embeddings.position_ids}
													for key, value in saved_state.model_dict.items():
																				if key.startswith(__UpperCAmelCase  ):
																											A__						  =				key[len(__UpperCAmelCase  ) :]
																											if not key.startswith('encode_proj.'  ):
																																		A__						  =				'bert_model.' + key
																											A__						  =				value
													encoder.load_state_dict(__UpperCAmelCase  )
													return model






class      UpperCamelCase__(      __A			):





						def 					snake_case__						(							self  )       ->					Optional[int]:
													A__						  =				DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'  )[0]  )  )
													print(f'''Loading DPR reader from {self.src_file}'''  )
													A__						  =				load_states_from_checkpoint(self.src_file  )
													# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
													A__						  =				{
													    'encoder.bert_model.embeddings.position_ids': model.span_predictor.encoder.bert_model.embeddings.position_ids
													}
													for key, value in saved_state.model_dict.items():
																				if key.startswith('encoder.'  ) and not key.startswith('encoder.encode_proj'  ):
																											A__						  =				'encoder.bert_model.' + key[len('encoder.'  ) :]
																				A__						  =				value
													model.span_predictor.load_state_dict(__UpperCAmelCase  )
													return model






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				Path(UpperCamelCase__					)
							dest_dir.mkdir(exist_ok=UpperCamelCase__					)

							A__						  =				DPRState.from_type(UpperCamelCase__							,     src_file=UpperCamelCase__					)
							A__						  =				dpr_state.load_dpr_model()
							model.save_pretrained(UpperCamelCase__					)
							model.from_pretrained(UpperCamelCase__					)  # sanity check


if __name__ == "__main__":
				__lowerCamelCase									=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--type", type=str, help="Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
				)
				parser.add_argument(
				    "--src",
				    type=str,
				    help=(
				        "Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"
				        " https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"
				        " 'retriever' checkpoints."
				    ),
				)
				parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model directory.")
				__lowerCamelCase									=	parser.parse_args()

				__lowerCamelCase									=	Path(args.src)
				__lowerCamelCase									=	F'''converted-{src_file.name}''' if args.dest is None else args.dest
				__lowerCamelCase									=	Path(dest_dir)
				assert src_file.exists()
				assert (
				    args.type is not None
				), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
				convert(args.type, src_file, dest_dir)






"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase									=	logging.get_logger(__name__)

__lowerCamelCase									=	{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    str    				=		'vilt'





						def __init__(							self		,__UpperCAmelCase=3_05_22		,__UpperCAmelCase=2		,__UpperCAmelCase=2		,__UpperCAmelCase=40		,__UpperCAmelCase=7_68		,__UpperCAmelCase=12		,__UpperCAmelCase=12		,__UpperCAmelCase=30_72		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=1e-12		,__UpperCAmelCase=3_84		,__UpperCAmelCase=32		,__UpperCAmelCase=3		,__UpperCAmelCase=True		,__UpperCAmelCase=-1		,__UpperCAmelCase=False		,__UpperCAmelCase=-1		,**__UpperCAmelCase		,)       ->					Tuple:
													super().__init__(tie_word_embeddings=__UpperCAmelCase		,**__UpperCAmelCase  )

													A__						  =				vocab_size
													A__						  =				type_vocab_size
													A__						  =				modality_type_vocab_size
													A__						  =				max_position_embeddings

													A__						  =				hidden_size
													A__						  =				num_hidden_layers
													A__						  =				num_attention_heads
													A__						  =				intermediate_size
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				attention_probs_dropout_prob
													A__						  =				initializer_range
													A__						  =				layer_norm_eps

													A__						  =				image_size
													A__						  =				patch_size
													A__						  =				num_channels
													A__						  =				qkv_bias
													A__						  =				max_image_length
													A__						  =				num_images





