





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
)
UpperCamelCase__           =			logging.getLogger(__name__)

if __name__ == "__main__":
					UpperCamelCase__           =			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=30_522, type=int)
					UpperCamelCase__           =			parser.parse_args()

					logger.info(f"""Loading data from {args.data_file}""")
					with open(args.data_file, "rb") as fp:
										UpperCamelCase__           =			pickle.load(fp)

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











# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")

class 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = True	,			__lowerCAmelCase = False	):
														UpperCamelCase__					  =      scheduler
														UpperCamelCase__					  =      optimizers if isinstance(__lowerCAmelCase	,			(list, tuple)	) else [optimizers]
														UpperCamelCase__					  =      split_batches
														UpperCamelCase__					  =      step_with_optimizer
														UpperCamelCase__					  =      GradientState()






							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														if not self.step_with_optimizer:
																					# No link between scheduler and optimizer -> just step
																					self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)
																					return

														# Otherwise, first make sure the optimizer was stepped.
														if not self.gradient_state.sync_gradients:
																					if self.gradient_state.adjust_scheduler:
																												self.scheduler._step_count += 1
																					return

														for opt in self.optimizers:
																					if opt.step_was_skipped:
																												return
														if self.split_batches:
																					# Split batches -> the training dataloader batch size is not changed so one step per training step
																					self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)
														else:
																					# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
																					# num_processes steps per training step
																					UpperCamelCase__					  =      AcceleratorState().num_processes
																					for _ in range(__lowerCAmelCase	):
																												# Special case when using OneCycle and `drop_last` was not used
																												if hasattr(self.scheduler	,			"""total_steps"""	):
																																			if self.scheduler._step_count <= self.scheduler.total_steps:
																																										self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)
																												else:
																																			self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.get_last_lr()






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.state_dict()






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														self.scheduler.load_state_dict(__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.get_lr()







							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														return self.scheduler.print_lr(*__lowerCAmelCase	,			**__lowerCAmelCase	)




