



"""simple docstring"""

import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a_			   =					logging.get_logger(__name__)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					RobertaPreLayerNormConfig.from_pretrained(
					    SCREAMING_SNAKE_CASE__				,    architectures=["""RobertaPreLayerNormForMaskedLM"""]			)

					# convert state_dict
					snake_case_					:		Optional[Any] 					=					torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE__				,    filename="""pytorch_model.bin"""			)			)
					snake_case_					:		int 					=					{}
					for tensor_key, tensor_value in original_state_dict.items():
										# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
										if tensor_key.startswith("""roberta."""			):
															snake_case_					:		List[Any] 					=					"""roberta_prelayernorm.""" + tensor_key[len("""roberta."""			) :]

										# The original implementation contains weights which are not used, remove them from the state_dict
										if tensor_key.endswith(""".self.LayerNorm.weight"""			) or tensor_key.endswith(""".self.LayerNorm.bias"""			):
															continue

										snake_case_					:		Dict 					=					tensor_value

					snake_case_					:		Tuple 					=					RobertaPreLayerNormForMaskedLM.from_pretrained(
					    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE__				,    config=SCREAMING_SNAKE_CASE__				,    state_dict=SCREAMING_SNAKE_CASE__			)
					model.save_pretrained(SCREAMING_SNAKE_CASE__			)

					# convert tokenizer
					snake_case_					:		List[Any] 					=					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__			)
					tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
			a_			   =					argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--checkpoint-repo''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			a_			   =					parser.parse_args()
			convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)









"""simple docstring"""

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a_			   =					logging.getLogger(__name__)

if __name__ == "__main__":
			a_			   =					argparse.ArgumentParser(
			    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
			)
			parser.add_argument(
			    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
			)
			parser.add_argument(
			    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
			)
			parser.add_argument('''--vocab_size''', default=30522, type=int)
			a_			   =					parser.parse_args()

			logger.info(F'''Loading data from {args.data_file}''')
			with open(args.data_file, '''rb''') as fp:
						a_			   =					pickle.load(fp)

			logger.info('''Counting occurrences for MLM.''')
			a_			   =					Counter()
			for tk_ids in data:
						counter.update(tk_ids)
			a_			   =					[0] * args.vocab_size
			for k, v in counter.items():
						a_			   =					v

			logger.info(F'''Dump to {args.token_counts_dump}''')
			with open(args.token_counts_dump, '''wb''') as handle:
						pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)




