
"""simple docstring"""







import sys
from pathlib import Path


_SCREAMING_SNAKE_CASE	       =      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)

_SCREAMING_SNAKE_CASE	       =      {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

_SCREAMING_SNAKE_CASE	       =      """zero2"""
_SCREAMING_SNAKE_CASE	       =      """zero3"""
_SCREAMING_SNAKE_CASE	       =      [ZEROa, ZEROa]
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Any:







							"""simple docstring"""


							__snake_case     			=       parameterized.to_safe_name("_".join(str(SCREAMING_SNAKE_CASE ) for x in param.args ) )
							return F'''{func.__name__}_{param_based_name}'''


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







@slow
@require_deepspeed
@require_torch_gpu
class 							__magic_name__     (		_snake_case   ):





					@parameterized.expand(snake_case_							,						name_func=snake_case_    )
					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Tuple							,						snake_case_   :							Tuple    ):
												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   :							List[str]							,						snake_case_   :							int							,						snake_case_   :							Union[str, Any]    ):
												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   :							Any							,						snake_case_   :							List[str]							,						snake_case_   :							str    ):
												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   :							int							,						snake_case_   :							Dict							,						snake_case_   :							str    ):
												self.run_and_check(
												    stage=snake_case_							,						model=snake_case_							,						distributed=snake_case_							,						fpaa=snake_case_							,						)





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							List[str]    ):
												# XXX: run_asr is premature and doesn't save any results
												# so all we check for now is that the process didn't fail
												pass





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Optional[int] = 10							,						snake_case_   :							List[Any] = True							,						snake_case_   :							Union[str, Any] = True							,						snake_case_   :							List[Any] = True							,						):
												__snake_case     			=       models[model]

												__snake_case     			=       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   :							Optional[Any]							,						snake_case_   :							int							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Any = 10							,						snake_case_   :							Optional[Any] = 1							,						snake_case_   :							Optional[int] = True							,						snake_case_   :							Tuple = True							,						):
												__snake_case     			=       self.get_auto_remove_tmp_dir("./xxx"							,						after=snake_case_    )
												__snake_case     			=       F'''\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(snake_case_    )}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '''.split()

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

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

												__snake_case     			=       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   :							Tuple							,						snake_case_   :							Optional[Any]=False    ):
												# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
												# - it won't be able to handle that
												# 2. for now testing with just 2 gpus max (since some quality tests may give different
												# results with mode gpus because we use very little data)
												__snake_case     			=       min(2							,						get_gpu_count()    ) if distributed else 1
												return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




"""simple docstring"""







import baseaa
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     bytes:







							"""simple docstring"""


							return baseaa.baaencode(string.encode("utf-8" ) )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							return baseaa.baadecode(SCREAMING_SNAKE_CASE ).decode("utf-8" )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      """Hello World!"""
		_SCREAMING_SNAKE_CASE	       =      baseaa_encode(test)
		print(encoded)

		_SCREAMING_SNAKE_CASE	       =      baseaa_decode(encoded)
		print(decoded)
