import os
from typing import List





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  Tuple:






      '''simple docstring'''





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





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  List[Any]:






      '''simple docstring'''





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





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase = None						)->  int:






      '''simple docstring'''





      with open(__lowerCAmelCase		,					'''r'''						) as f:
            UpperCAmelCase    : Optional[int]      =f.readlines()
      with open(os.path.abspath(os.path.join('''examples'''		,					'''nlp_example.py'''						)						)		,					'''r'''						) as f:
            UpperCAmelCase    : List[Any]      =f.readlines()
      with open(__lowerCAmelCase		,					'''r'''						) as f:
            UpperCAmelCase    : Union[str, Any]      =f.readlines()
      if secondary_filename is not None:
            with open(__lowerCAmelCase		,					'''r'''						) as f:
                  UpperCAmelCase    : Tuple      =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:
            UpperCAmelCase    : Any      =clean_lines(get_function_contents_by_name(__lowerCAmelCase		,					'''main'''						)						)
            UpperCAmelCase    : Optional[Any]      =clean_lines(get_function_contents_by_name(__lowerCAmelCase		,					'''main'''						)						)
            UpperCAmelCase    : int      =clean_lines(get_function_contents_by_name(__lowerCAmelCase		,					'''main'''						)						)
            if secondary_filename is not None:
                  UpperCAmelCase    : Any      =clean_lines(get_function_contents_by_name(__lowerCAmelCase		,					'''main'''						)						)
      else:
            UpperCAmelCase    : Optional[Any]      =clean_lines(get_function_contents_by_name(__lowerCAmelCase		,					'''training_function'''						)						)
            UpperCAmelCase    : Tuple      =clean_lines(get_function_contents_by_name(__lowerCAmelCase		,					'''training_function'''						)						)
            UpperCAmelCase    : int      =clean_lines(get_function_contents_by_name(__lowerCAmelCase		,					'''training_function'''						)						)
            if secondary_filename is not None:
                  UpperCAmelCase    : Optional[int]      =clean_lines(
                      get_function_contents_by_name(__lowerCAmelCase		,					'''training_function'''						)						)

      UpperCAmelCase    : Dict      ='''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
      UpperCAmelCase    : Any      =[]
      UpperCAmelCase    : str      =[]  # We keep track of the idxs just in case it's a repeated statement
      UpperCAmelCase    : Tuple      =iter(__lowerCAmelCase						)
      for i in range(len(__lowerCAmelCase						) - 1						):
            if i not in passed_idxs:
                  UpperCAmelCase    : Dict      =next(__lowerCAmelCase						)
                  if (line not in full_file_func) and (line.lstrip() != _dl_line):
                        if "TESTING_MOCKED_DATALOADERS" not in line:
                              new_feature_code.append(__lowerCAmelCase						)
                              passed_idxs.append(__lowerCAmelCase						)
                        else:
                              # Skip over the `config['num_epochs'] = 2` statement
                              UpperCAmelCase    : List[str]      =next(__lowerCAmelCase						)

    # Extract out just the new parts from the full_file_training_func
      UpperCAmelCase    : Optional[int]      =[]
      UpperCAmelCase    : str      =[]  # We keep track of the idxs just in case it's a repeated statement
      for i, line in enumerate(__lowerCAmelCase						):
            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(__lowerCAmelCase						)
                              passed_idxs.append(__lowerCAmelCase						)

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

      return diff_from_example

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case			      =    {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''EsmForMaskedLM''',
         '''EsmForSequenceClassification''',
         '''EsmForTokenClassification''',
         '''EsmModel''',
         '''EsmPreTrainedModel''',
     ]
     __snake_case			      =    ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFEsmForMaskedLM''',
         '''TFEsmForSequenceClassification''',
         '''TFEsmForTokenClassification''',
         '''TFEsmModel''',
         '''TFEsmPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
     from .tokenization_esm import EsmTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_esm import (
              ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              EsmForMaskedLM,
              EsmForSequenceClassification,
              EsmForTokenClassification,
              EsmModel,
              EsmPreTrainedModel,
          )
          from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_esm import (
              TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
              TFEsmModel,
              TFEsmPreTrainedModel,
          )


else:
     import sys

     __snake_case			      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
