





import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


snake_case__   :				Union[str, Any]						=	logging.get_logger(__name__)





class A_   (     _lowercase  ):




   def __init__(self      :Tuple       ,  *_UpperCamelCase      :str       ,  **_UpperCamelCase      :Optional[Any]					)->   str:
         warnings.warn(
             '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
             ''' Please use ConditionalDetrImageProcessor instead.'''       ,  __SCREAMING_SNAKE_CASE       ,  )
         super().__init__(*__SCREAMING_SNAKE_CASE       ,  **__SCREAMING_SNAKE_CASE					)






import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE:    Optional[int]     						=							sys.argv[1]
_SCREAMING_SNAKE_CASE:    int     						=							sys.argv[2]
_SCREAMING_SNAKE_CASE:    str     						=							int(sys.argv[3])

_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							0

_SCREAMING_SNAKE_CASE:    Any     						=							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
			for line in f_p:
						_SCREAMING_SNAKE_CASE:    Any     						=							line.rstrip()

						if not line:
									print(line)
									_SCREAMING_SNAKE_CASE:    str     						=							0
									continue

						_SCREAMING_SNAKE_CASE:    Optional[int]     						=							line.split()[0]

						_SCREAMING_SNAKE_CASE:    Tuple     						=							len(tokenizer.tokenize(token))

						# Token contains strange control characters like \x96 or \x95
						# Just filter out the complete line
						if current_subwords_len == 0:
									continue

						if (subword_len_counter + current_subwords_len) > max_len:
									print('''''')
									print(line)
									_SCREAMING_SNAKE_CASE:    Any     						=							current_subwords_len
									continue

						subword_len_counter += current_subwords_len

						print(line)

