import os
from typing import List







def  __lowerCAmelCase							(						__SCREAMING_SNAKE_CASE    : List[str]						,				__SCREAMING_SNAKE_CASE    : str					):







    '''simple docstring'''





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







def  __lowerCAmelCase							(						__SCREAMING_SNAKE_CASE    : List[str]					):







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







def  __lowerCAmelCase							(						__SCREAMING_SNAKE_CASE    : str						,				__SCREAMING_SNAKE_CASE    : str						,				__SCREAMING_SNAKE_CASE    : bool						,				__SCREAMING_SNAKE_CASE    : str = None					):







    '''simple docstring'''





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

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

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

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

    return diff_from_example






'''simple docstring'''
import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowerCamelCase	 =			[(1,)]


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[int]      =   bool(os.getenv("RUN_SLOW"     ,							0							)							)
	if run_slow:
		assert True
	else:
		assert False, "should have been skipped"


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   bool(os.getenv("RUN_SLOW"     ,							0							)							)
	if run_slow and torch_device == "cuda":
		assert True
	else:
		assert False, "should have been skipped"
@require_torch
class  _snake_case						(unittest.TestCase):






			@slow
			@require_torch_gpu
			def 						UpperCamelCase__				(   self			):
				check_slow_torch_cuda()






			@require_torch_gpu
			@slow
			def 						UpperCamelCase__				(   self			):
				check_slow_torch_cuda()






			@parameterized.expand(_snake_case			)
			@slow
			def 						UpperCamelCase__				(   self       ,_snake_case=None			):
				check_slow()




@slow
@require_torch_gpu
def       a__    (  )			-> List[Any]:


	"""simple docstring"""





	check_slow_torch_cuda()


@require_torch_gpu
@slow
def       a__    (  )			-> List[str]:


	"""simple docstring"""





	check_slow_torch_cuda()


@slow
@pytest.mark.parametrize("param"     ,							[1]							)
def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> List[Any]:


	"""simple docstring"""





	check_slow()





@pytest.mark.parametrize("param"     ,							[1]							)
@slow
def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Any:


	"""simple docstring"""





	check_slow()




