





"""simple docstring"""






import sys
from pathlib import Path


A	     =					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)

A	     =					{'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

A	     =					'''zero2'''
A	     =					'''zero3'''
A	     =					[ZEROa, ZEROa]

def       __A (     a_  :str				,    a_  :Optional[int]				,    a_  :Optional[Any])						->						Optional[Any]:
    # 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
    __a	:  Any         =					parameterized.to_safe_name('''_'''.join(str(a_) for x in param.args))
    return F"""{func.__name__}_{param_based_name}"""


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






@slow
@require_deepspeed
@require_torch_gpu
class 			__lowercase     (     _UpperCamelCase							):







     '''simple docstring'''




     @parameterized.expand(_UpperCAmelCase		,     name_func=_UpperCAmelCase       )
     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
         self.run_and_check(
             stage=_UpperCAmelCase		,     model=_UpperCAmelCase		,     distributed=_UpperCAmelCase		,     fpaa=_UpperCAmelCase		,     )







     @require_torch_multi_gpu
     @parameterized.expand(_UpperCAmelCase		,     name_func=_UpperCAmelCase       )
     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
         self.run_and_check(
             stage=_UpperCAmelCase		,     model=_UpperCAmelCase		,     distributed=_UpperCAmelCase		,     fpaa=_UpperCAmelCase		,     )







     @parameterized.expand(_UpperCAmelCase		,     name_func=_UpperCAmelCase       )
     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
         self.run_and_check(
             stage=_UpperCAmelCase		,     model=_UpperCAmelCase		,     distributed=_UpperCAmelCase		,     fpaa=_UpperCAmelCase		,     )







     @require_torch_multi_gpu
     @parameterized.expand(_UpperCAmelCase		,     name_func=_UpperCAmelCase       )
     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
         self.run_and_check(
             stage=_UpperCAmelCase		,     model=_UpperCAmelCase		,     distributed=_UpperCAmelCase		,     fpaa=_UpperCAmelCase		,     )







     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
         # 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		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = 10		,     _UpperCAmelCase = True		,     _UpperCAmelCase = True		,     _UpperCAmelCase = True		,     ):
         __a	:  Tuple         =					models[model]

         __a	:  Any         =					self.run_trainer(
             stage=_UpperCAmelCase		,     model_name=_UpperCAmelCase		,     eval_steps=_UpperCAmelCase		,     num_train_epochs=1		,     distributed=_UpperCAmelCase		,     fpaa=_UpperCAmelCase		,     )

         self.do_checks(_UpperCAmelCase       )

         return output_dir







     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = 10		,     _UpperCAmelCase = 1		,     _UpperCAmelCase = True		,     _UpperCAmelCase = True		,     ):
         __a	:  Optional[int]         =					self.get_auto_remove_tmp_dir('''./xxx'''		,     after=_UpperCAmelCase       )
         __a	:  Any         =					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(_UpperCAmelCase       )}
            --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
         __a	:  Tuple         =					f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
         __a	:  List[Any]         =					[f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
         __a	:  Tuple         =					self.get_launcher(_UpperCAmelCase       )

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

         return output_dir







     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=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)
         __a	:  List[str]         =					min(2		,     get_gpu_count()       ) if distributed else 1
         return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()






"""simple docstring"""






def       __A (     a_  :int				,    a_  :int)						->						str:
				if a < 0 or b < 0:
								raise ValueError('''the value of both inputs must be positive''')

				__a	:  Union[str, Any]         =					str(bin(a_))[2:]  # remove the leading "0b"
				__a	:  Union[str, Any]         =					str(bin(a_))[2:]  # remove the leading "0b"

				__a	:  Optional[Any]         =					max(len(a_)				,    len(a_))

				return "0b" + "".join(
				    str(int(char_a != char_b))
				    for char_a, char_b in zip(a_binary.zfill(a_)				,    b_binary.zfill(a_)))


if __name__ == "__main__":
		import doctest

		doctest.testmod()