





import os
from typing import List

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : Any	) ->						Optional[int]:
   if name != "training_function" and name != "main":
      raise ValueError(f"Incorrect function name passed: {name}, choose either \'main\' or \'training_function\'"	)
   A_      ,						A_					:				Any          =			[], False
   for line in lines:
      if not found_start and f"def {name}" in line:
         A_					:				List[Any]          =			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      __snake_case						(					_lowerCAmelCase : Optional[Any]	) ->						Optional[int]:
   return [line for line in lines if not line.lstrip().startswith("#"	) and line != "\n"]





def      __snake_case						(					_lowerCAmelCase : Optional[Any]				,				_lowerCAmelCase : Union[str, Any]				,				_lowerCAmelCase : Dict				,				_lowerCAmelCase : str = None	) ->						List[str]:
   with open(_lowerCAmelCase				,				"r"	) as f:
      A_					:				Any          =			f.readlines()
   with open(os.path.abspath(os.path.join("examples"				,				"nlp_example.py"	)	)				,				"r"	) as f:
      A_					:				Any          =			f.readlines()
   with open(_lowerCAmelCase				,				"r"	) as f:
      A_					:				Optional[Any]          =			f.readlines()
   if secondary_filename is not None:
      with open(_lowerCAmelCase				,				"r"	) as f:
         A_					:				List[str]          =			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:
      A_					:				Tuple          =			clean_lines(get_function_contents_by_name(_lowerCAmelCase				,				"main"	)	)
      A_					:				Any          =			clean_lines(get_function_contents_by_name(_lowerCAmelCase				,				"main"	)	)
      A_					:				Optional[int]          =			clean_lines(get_function_contents_by_name(_lowerCAmelCase				,				"main"	)	)
      if secondary_filename is not None:
         A_					:				Union[str, Any]          =			clean_lines(get_function_contents_by_name(_lowerCAmelCase				,				"main"	)	)
   else:
      A_					:				Optional[Any]          =			clean_lines(get_function_contents_by_name(_lowerCAmelCase				,				"training_function"	)	)
      A_					:				Any          =			clean_lines(get_function_contents_by_name(_lowerCAmelCase				,				"training_function"	)	)
      A_					:				int          =			clean_lines(get_function_contents_by_name(_lowerCAmelCase				,				"training_function"	)	)
      if secondary_filename is not None:
         A_					:				List[str]          =			clean_lines(
             get_function_contents_by_name(_lowerCAmelCase				,				"training_function"	)	)

   A_					:				str          =			"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
   A_					:				List[str]          =			[]
   A_					:				List[Any]          =			[]  # We keep track of the idxs just in case it's a repeated statement
   A_					:				str          =			iter(_lowerCAmelCase	)
   for i in range(len(_lowerCAmelCase	) - 1	):
      if i not in passed_idxs:
         A_					:				Tuple          =			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
               A_					:				Any          =			next(_lowerCAmelCase	)

    # Extract out just the new parts from the full_file_training_func
   A_					:				int          =			[]
   A_					:				Union[str, Any]          =			[]  # 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
   A_					:				List[Any]          =			[line for line in new_feature_code if line not in new_full_example_parts]
   if secondary_filename is not None:
      A_					:				Any          =			[line for line in full_file_contents if line not in secondary_file_func]
      A_					:				Optional[int]          =			[line for line in diff_from_example if line not in diff_from_two]

   return diff_from_example







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[Any]          =			(inputs > threshold).type(inputs.type()  )
        if mask.sum() < nb_min:
           # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				str          =			mask.flatten()
        A_					:				List[Any]          =			0
        A_					:				List[str]          =			1
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				List[str]          =			mask.flatten()
        A_					:				List[str]          =			0
        A_					:				Union[str, Any]          =			1
        return mask
