



# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__snake_case    :		str  =abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[Any]):





				'''simple docstring'''

				config.addinivalue_line(
				    '''markers'''	,'''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''')
				config.addinivalue_line(
				    '''markers'''	,'''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''')
				config.addinivalue_line('''markers'''	,'''is_pipeline_test: mark test to run only when pipelines are tested''')
				config.addinivalue_line('''markers'''	,'''is_staging_test: mark test to run only in the staging environment''')
				config.addinivalue_line('''markers'''	,'''accelerate_tests: mark test that require accelerate''')
				config.addinivalue_line('''markers'''	,'''tool_tests: mark the tool tests that are run on their specific schedule''')






def      lowerCAmelCase__		(   lowerCamelCase_		:		Any):





				'''simple docstring'''

				from transformers.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(lowerCamelCase_)






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]):





				'''simple docstring'''

				from transformers.testing_utils import pytest_terminal_summary_main

				lowerCAmelCase__					:    str												=					terminalreporter.config.getoption('''--make-reports''')
				if make_reports:
								pytest_terminal_summary_main(lowerCamelCase_	,id=lowerCamelCase_)






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		List[Any]):





				'''simple docstring'''

				if exitstatus == 5:
								lowerCAmelCase__					:    Optional[Any]												=					0


# Doctest custom flag to ignore output.
__snake_case    :		List[Any]  =doctest.register_optionflag('IGNORE_RESULT')

__snake_case    :		Tuple  =doctest.OutputChecker



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






						if IGNORE_RESULT & optionflags:
										return True
						return OutputChecker.check_output(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)


__snake_case    :		int  =CustomOutputChecker
__snake_case    :		Union[str, Any]  =HfDoctestModule

__snake_case    :		Dict  =HfDocTestParser






import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		List[Any]=1024	,lowerCamelCase_		:		int=1024	,lowerCamelCase_		:		Dict=False	,**lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					AutoTokenizer.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[Any]												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''train'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    int												=					tok.pad_token_id

				def get_lens(lowerCamelCase_		:		Tuple):
								lowerCAmelCase__					:    Tuple												=					tqdm(
								    DataLoader(lowerCamelCase_	,batch_size=512	,num_workers=8	,shuffle=lowerCamelCase_	,collate_fn=ds.collate_fn)	,desc=str(ds.len_file)	,)
								lowerCAmelCase__					:    Tuple												=					[]
								for batch in dl:
												lowerCAmelCase__					:    Dict												=					batch['''input_ids'''].ne(lowerCamelCase_).sum(1).tolist()
												lowerCAmelCase__					:    Dict												=					batch['''labels'''].ne(lowerCamelCase_).sum(1).tolist()
												if consider_target:
																for src, tgt in zip(lowerCamelCase_	,lowerCamelCase_):
																				max_lens.append(max(lowerCamelCase_	,lowerCamelCase_))
												else:
																max_lens.extend(lowerCamelCase_)
								return max_lens

				lowerCAmelCase__					:    str												=					get_lens(lowerCamelCase_)
				lowerCAmelCase__					:    Tuple												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''val'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    Optional[int]												=					get_lens(lowerCamelCase_)
				pickle_save(lowerCamelCase_	,train_ds.len_file)
				pickle_save(lowerCamelCase_	,val_ds.len_file)


if __name__ == "__main__":
							fire.Fire(save_len_file)

