from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE:    int     						=							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE:    Optional[int]     						=							{
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   '''regnet'''
		A_    			=   ['''x''', '''y''']



		def __init__(      self     :					List[Any]	,       __SCREAMING_SNAKE_CASE     :					str=3	,       __SCREAMING_SNAKE_CASE     :					str=32	,       __SCREAMING_SNAKE_CASE     :					Any=[128, 192, 512, 1_088]	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]=[2, 6, 12, 2]	,       __SCREAMING_SNAKE_CASE     :					str=64	,       __SCREAMING_SNAKE_CASE     :					int="y"	,       __SCREAMING_SNAKE_CASE     :					Optional[int]="relu"	,       **__SCREAMING_SNAKE_CASE     :					Optional[int]	,       ):






					"""simple docstring"""
					super().__init__(**__SCREAMING_SNAKE_CASE )
					if layer_type not in self.layer_types:
								raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types )}' )
					_snake_case														=   num_channels
					_snake_case														=   embedding_size
					_snake_case														=   hidden_sizes
					_snake_case														=   depths
					_snake_case														=   groups_width
					_snake_case														=   layer_type
					_snake_case														=   hidden_act
					# always downsample in the first stage
					_snake_case														=   True


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)

