


'''simple docstring'''

import os
from typing import List

def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Optional[int]:






       """simple docstring"""







       if name != "training_function" and name != "main":
              raise ValueError(f"Incorrect function name passed: {name}, choose either \'main\' or \'training_function\'" )
       lowercase__    ,lowercase__       =				[], False
       for line in lines:
              if not found_start and f"def {name}" in line:
                     lowercase__       =				True
                     good_lines.append(A )
                     continue
              if found_start:
                     if name == "training_function" and "def main" in line:
                            return good_lines
                     if name == "main" and "if __name__" in line:
                            return good_lines
                     good_lines.append(A )

def _SCREAMING_SNAKE_CASE   (A ) -> Optional[int]:






       """simple docstring"""







       return [line for line in lines if not line.lstrip().startswith('''#''' ) and line != "\n"]

def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A = None ) -> List[str]:






       """simple docstring"""







       with open(A						,				'''r''' ) as f:
              lowercase__       =				f.readlines()
       with open(os.path.abspath(os.path.join('''examples'''						,				'''nlp_example.py''' ) )						,				'''r''' ) as f:
              lowercase__       =				f.readlines()
       with open(A						,				'''r''' ) as f:
              lowercase__       =				f.readlines()
       if secondary_filename is not None:
              with open(A						,				'''r''' ) as f:
                     lowercase__       =				f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
       if parser_only:
              lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''main''' ) )
              lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''main''' ) )
              lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''main''' ) )
              if secondary_filename is not None:
                     lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''main''' ) )
       else:
              lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''training_function''' ) )
              lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''training_function''' ) )
              lowercase__       =				clean_lines(get_function_contents_by_name(A						,				'''training_function''' ) )
              if secondary_filename is not None:
                     lowercase__       =				clean_lines(
                         get_function_contents_by_name(A						,				'''training_function''' ) )

       lowercase__       =				'''train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'''

       # Specific code in our script that differs from the full version, aka what is new
       lowercase__       =				[]
       lowercase__       =				[]  # We keep track of the idxs just in case it's a repeated statement
       lowercase__       =				iter(A )
       for i in range(len(A ) - 1 ):
              if i not in passed_idxs:
                     lowercase__       =				next(A )
                     if (line not in full_file_func) and (line.lstrip() != _dl_line):
                            if "TESTING_MOCKED_DATALOADERS" not in line:
                                   new_feature_code.append(A )
                                   passed_idxs.append(A )
                            else:
                                   # Skip over the `config['num_epochs'] = 2` statement
                                   lowercase__       =				next(A )

    # Extract out just the new parts from the full_file_training_func
       lowercase__       =				[]
       lowercase__       =				[]  # We keep track of the idxs just in case it's a repeated statement
       for i, line in enumerate(A ):
              if i not in passed_idxs:
                     if (line not in full_file_func) and (line.lstrip() != _dl_line):
                            if "TESTING_MOCKED_DATALOADERS" not in line:
                                   new_full_example_parts.append(A )
                                   passed_idxs.append(A )

    # Finally, get the overall diff
       lowercase__       =				[line for line in new_feature_code if line not in new_full_example_parts]
       if secondary_filename is not None:
              lowercase__       =				[line for line in full_file_contents if line not in secondary_file_func]
              lowercase__       =				[line for line in diff_from_example if line not in diff_from_two]

       return diff_from_example








'''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__)




