


'''simple docstring'''

import sys

from transformers import AutoTokenizer


lowerCamelCase							:     Optional[int]								=			sys.argv[1]
lowerCamelCase							:     List[str]								=			sys.argv[2]
lowerCamelCase							:     List[Any]								=			int(sys.argv[3])

lowerCamelCase							:     Dict								=			0

lowerCamelCase							:     Union[str, 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:
  lowerCamelCase							:     Tuple								=			line.rstrip()

  if not line:
   print(line)
   lowerCamelCase							:     Tuple								=			0
   continue

  lowerCamelCase							:     List[str]								=			line.split()[0]

  lowerCamelCase							:     int								=			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)
   lowerCamelCase							:     int								=			current_subwords_len
   continue

  subword_len_counter += current_subwords_len

  print(line)








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase							:     Optional[Any]								=			{
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:     Optional[int]								=			[
     'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MobileViTV2ForImageClassification',
     'MobileViTV2ForSemanticSegmentation',
     'MobileViTV2Model',
     'MobileViTV2PreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase							:     Dict								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




