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 	__lowerCamelCase							(				__a    : str		)      ->       CheckpointState:
	print(f'''Reading saved model from {model_file}'''		)
	_lowercase   =torch.load(__a      ,							map_location=lambda __a      ,							__a		: default_restore_location(__a      ,							"cpu"		)		)
	return CheckpointState(**__a		)
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ):
		_lowercase   =src_file


	def __lowerCAmelCase      (   self       ):
		raise NotImplementedError


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

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   =DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"       )[0]       )       )
		print(F'''Loading DPR biencoder from {self.src_file}'''       )
		_lowercase   =load_states_from_checkpoint(self.src_file       )
		_lowercase   ,  _lowercase   =model.ctx_encoder, "ctx_model."
		# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
		_lowercase   ={"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(lowerCAmelCase_       ):
				_lowercase   =key[len(lowerCAmelCase_       ) :]
				if not key.startswith("encode_proj."       ):
					_lowercase   ="bert_model." + key
				_lowercase   =value
		encoder.load_state_dict(lowerCAmelCase_       )
		return model

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   =DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"       )[0]       )       )
		print(F'''Loading DPR biencoder from {self.src_file}'''       )
		_lowercase   =load_states_from_checkpoint(self.src_file       )
		_lowercase   ,  _lowercase   =model.question_encoder, "question_model."
		# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
		_lowercase   ={"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(lowerCAmelCase_       ):
				_lowercase   =key[len(lowerCAmelCase_       ) :]
				if not key.startswith("encode_proj."       ):
					_lowercase   ="bert_model." + key
				_lowercase   =value
		encoder.load_state_dict(lowerCAmelCase_       )
		return model

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   =DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"       )[0]       )       )
		print(F'''Loading DPR reader from {self.src_file}'''       )
		_lowercase   =load_states_from_checkpoint(self.src_file       )
		# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
		_lowercase   ={
		    "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"       ):
				_lowercase   ="encoder.bert_model." + key[len("encoder."       ) :]
			_lowercase   =value
		model.span_predictor.load_state_dict(lowerCAmelCase_       )
		return model

def 	__lowerCamelCase							(				__a    : str      ,							__a    : Path      ,							__a    : Path		)      ->       int:
	_lowercase   =Path(__a		)
	dest_dir.mkdir(exist_ok=__a		)

	_lowercase   =DPRState.from_type(__a      ,							src_file=__a		)
	_lowercase   =dpr_state.load_dpr_model()
	model.save_pretrained(__a		)
	model.from_pretrained(__a		)  # 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)








import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCamelCase			:					List[str]         =							logging.getLogger(__name__)
def 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'  )
						parser.add_argument(
						    '--dataset_name'						,      type=__snake_case						,      default='wikitext'						,      help='Name of the training. Explore datasets at: hf.co/datasets.'						,      )
						parser.add_argument(
						    '--dataset_config'						,      type=__snake_case						,      default='wikitext-103-raw-v1'						,      help='Configuration name of the dataset.'  )
						parser.add_argument(
						    '--batch_size'						,      type=__snake_case						,      default=10_00						,      help='Batch size during training.'						,      )
						parser.add_argument(
						    '--vocab_size'						,      type=__snake_case						,      default=1_00_48						,      help='Size of the desired vocabulary.'						,      )
						parser.add_argument(
						    '--limit'						,      default=__snake_case						,      type=__snake_case						,      help='Limit the number of shards (used for debugging).'						,      )
						parser.add_argument(
						    '--export_to_hub'						,      action='store_true'						,      )

						__magic_name__					=      parser.parse_args()
						return args
def 				UpperCamelCase_	(					__snake_case      :    Any  )->   Optional[int]:




						'''simple docstring'''
						__magic_name__					=      datasets.load_dataset(args.dataset_name						,      args.dataset_config						,      split='train'  )

						if args.limit is not None:
												__magic_name__					=      min(len(__snake_case  )						,      args.limit  )
												__magic_name__					=      dataset.select(range(__snake_case  )  )
												logger.info(F'''Limiting the dataset to {args.limit} entries.'''  )

						def batch_iterator():
												for i in range(0						,      len(__snake_case  )						,      args.batch_size  ):
																		yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
						__magic_name__					=      Tokenizer(Unigram()  )
						__magic_name__					=      normalizers.Sequence([normalizers.Replace('``'						,      '"'  ), normalizers.Replace('\'\''						,      '"'  )]  )
						__magic_name__					=      pre_tokenizers.Metaspace()

						# Prepare the trainer.
						__magic_name__					=      UnigramTrainer(
						    unk_token='<unk>'						,      special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']						,      vocab_size=args.vocab_size						,      )

						logger.info('Training the tokenizer.'  )
						tokenizer.train_from_iterator(batch_iterator()						,      trainer=__snake_case  )
						logger.info('Tokenizer training complete!'  )

						__magic_name__					=      tokenizer.token_to_id('[CLS]'  )
						__magic_name__					=      tokenizer.token_to_id('[SEP]'  )
						__magic_name__					=      processors.TemplateProcessing(
						    single='[CLS]:0 $A:0 [SEP]:0'						,      pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'						,      special_tokens=[
						        ('[CLS]', cls_token_id),
						        ('[SEP]', sep_token_id),
						    ]						,      )
						__magic_name__					=      decoders.Metaspace()

						if args.export_to_hub:
												logger.info('Exporting the trained tokenzier to Hub.'  )
												__magic_name__					=      AlbertTokenizerFast(tokenizer_object=__snake_case  )
												new_tokenizer.push_to_hub('unigram-tokenizer-dataset'  )


if __name__ == "__main__":
					UpperCamelCase			:					Dict         =							parse_args()
					main(args)

