






"""simple docstring"""







import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A )     ->       int:
				if isinstance(_A  ,				_A ):
								lowercase    :      List[str]       = np.full((len(_A ), sequence_length, 2)  ,				_A )
				else:
								lowercase    :      Tuple       = np.full((len(_A ), sequence_length)  ,				_A )

				for i, tensor in enumerate(_A ):
								if padding_side == "right":
												if isinstance(_A  ,				_A ):
																lowercase    :      int       = tensor[:sequence_length]
												else:
																lowercase    :      List[Any]       = tensor[:sequence_length]
								else:
												if isinstance(_A  ,				_A ):
																lowercase    :      Any       = tensor[:sequence_length]
												else:
																lowercase    :      Dict       = tensor[:sequence_length]

				return out_tensor.tolist()


def      UpperCamelCase       ( _A )     ->       List[str]:
				lowercase    :      Union[str, Any]       = ord(_A )
				if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
								return True
				lowercase    :      Optional[int]       = unicodedata.category(_A )
				if cat.startswith("""P""" ):
								return True
				return False


@dataclass
class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : PreTrainedTokenizerBase
						_SCREAMING_SNAKE_CASE  : Union[bool, str, PaddingStrategy]												=		True
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		None
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		None
						_SCREAMING_SNAKE_CASE  : int												=		-100
						_SCREAMING_SNAKE_CASE  : str												=		"pt"






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :str				)      ->List[Any]:
										import torch

										lowercase    :      Tuple       = """label""" if """label""" in features[0].keys() else """labels"""
										lowercase    :      Tuple       = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
										lowercase    :      Any       = self.tokenizer.pad(
										    __magic_name__			, padding=self.padding			, max_length=self.max_length			, pad_to_multiple_of=self.pad_to_multiple_of			, return_tensors="""pt""" if labels is None else None			, )

										if labels is None:
														return batch

										lowercase    :      int       = torch.tensor(batch["""entity_ids"""]				).shape[1]
										lowercase    :      Optional[int]       = self.tokenizer.padding_side
										if padding_side == "right":
														lowercase    :      int       = [
														    list(__magic_name__				) + [self.label_pad_token_id] * (sequence_length - len(__magic_name__				)) for label in labels
														]
										else:
														lowercase    :      Any       = [
														    [self.label_pad_token_id] * (sequence_length - len(__magic_name__				)) + list(__magic_name__				) for label in labels
														]

										lowercase    :      List[Any]       = [feature["""ner_tags"""] for feature in features]
										lowercase    :      Optional[int]       = padding_tensor(__magic_name__			, -1			, __magic_name__			, __magic_name__				)
										lowercase    :      int       = [feature["""original_entity_spans"""] for feature in features]
										lowercase    :      Tuple       = padding_tensor(__magic_name__			, (-1, -1)			, __magic_name__			, __magic_name__				)
										lowercase    :      str       = {k: torch.tensor(__magic_name__			, dtype=torch.intaa				) for k, v in batch.items()}

										return batch








"""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
)
_lowerCAmelCase            =	logging.getLogger(__name__)

if __name__ == "__main__":
				_lowerCAmelCase            =	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=3_05_22, type=int)
				_lowerCAmelCase            =	parser.parse_args()

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

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