



def    _lowerCAmelCase    (_lowerCAmelCase):
					return number & 1 == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()





# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


UpperCAmelCase   :							List[str]							 =Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

UpperCAmelCase   :							List[str]							 ={"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

UpperCAmelCase   :							str							 ="""zero2"""
UpperCAmelCase   :							int							 ="""zero3"""
UpperCAmelCase   :							Optional[int]							 =[ZEROa, ZEROa]



def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase):
					# customize the test name generator function as we want both params to appear in the sub-test
					# name, as by default it shows only the first param
					UpperCamelCase_		  =   parameterized.to_safe_name("_".join(str(_lowerCAmelCase) for x in param.args))
					return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
UpperCAmelCase   :							Dict							 =list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class       _lowercase   (a_      ):
				'''simple docstring'''







				@parameterized.expand(snake_case__     , name_func=snake_case__    )
				def       _lowerCamelCase					( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


									self.run_and_check(
									    stage=snake_case__     , model=snake_case__     , distributed=snake_case__     , fpaa=snake_case__     , )

				@require_torch_multi_gpu
				@parameterized.expand(snake_case__     , name_func=snake_case__    )
				def       _lowerCamelCase					( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


									self.run_and_check(
									    stage=snake_case__     , model=snake_case__     , distributed=snake_case__     , fpaa=snake_case__     , )

				@parameterized.expand(snake_case__     , name_func=snake_case__    )
				def       _lowerCamelCase					( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


									self.run_and_check(
									    stage=snake_case__     , model=snake_case__     , distributed=snake_case__     , fpaa=snake_case__     , )

				@require_torch_multi_gpu
				@parameterized.expand(snake_case__     , name_func=snake_case__    )
				def       _lowerCamelCase					( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


									self.run_and_check(
									    stage=snake_case__     , model=snake_case__     , distributed=snake_case__     , fpaa=snake_case__     , )

				def       _lowerCamelCase					( self     , snake_case__    ):

									'''simple docstring'''


									pass

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__     , snake_case__ = 10     , snake_case__ = True     , snake_case__ = True     , snake_case__ = True     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   models[model]

									UpperCamelCase_		  =   self.run_trainer(
									    stage=snake_case__     , model_name=snake_case__     , eval_steps=snake_case__     , num_train_epochs=1     , distributed=snake_case__     , fpaa=snake_case__     , )

									self.do_checks(snake_case__    )

									return output_dir

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__     , snake_case__ = 10     , snake_case__ = 1     , snake_case__ = True     , snake_case__ = True     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.get_auto_remove_tmp_dir("./xxx"     , after=snake_case__    )
									UpperCamelCase_		  =   F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(snake_case__    )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

									if fpaa:
														args.extend(["--fp16"]    )

									# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
									# hence the separate config files
									UpperCamelCase_		  =   F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
									UpperCamelCase_		  =   [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
									UpperCamelCase_		  =   self.get_launcher(snake_case__    )

									UpperCamelCase_		  =   launcher + script + args + ds_args
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
									execute_subprocess_async(snake_case__     , env=self.get_env()    )

									return output_dir

				def       _lowerCamelCase					( self     , snake_case__=False    ):

									'''simple docstring'''


									UpperCamelCase_		  =   min(2     , get_gpu_count()    ) if distributed else 1
									return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()
