



"""simple docstring"""

import sys
from pathlib import Path


lowercase__					:						Union[str, Any]							=			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)

lowercase__					:						List[str]							=			{'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

lowercase__					:						Optional[Any]							=			'''zero2'''
lowercase__					:						Tuple							=			'''zero3'''
lowercase__					:						str							=			[ZEROa, ZEROa]




def __lowercase    (							_a	,    _a	,    _a					):
    # 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
    snake_case_				:  int       =			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
lowercase__					:						Optional[int]							=			list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class  _UpperCAmelCase (    lowerCAmelCase__):

 @parameterized.expand(lowercase_						,  name_func=lowercase_       )
 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: List[Any]						,  lowercase_	: int       ):
     self.run_and_check(
         stage=lowercase_						,  model=lowercase_						,  distributed=lowercase_						,  fpaa=lowercase_						,  )

 @require_torch_multi_gpu
 @parameterized.expand(lowercase_						,  name_func=lowercase_       )
 def 		_snake_case      (  self	: List[str]						,  lowercase_	: str						,  lowercase_	: List[Any]       ):
     self.run_and_check(
         stage=lowercase_						,  model=lowercase_						,  distributed=lowercase_						,  fpaa=lowercase_						,  )

 @parameterized.expand(lowercase_						,  name_func=lowercase_       )
 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: Union[str, Any]						,  lowercase_	: Union[str, Any]       ):
     self.run_and_check(
         stage=lowercase_						,  model=lowercase_						,  distributed=lowercase_						,  fpaa=lowercase_						,  )

 @require_torch_multi_gpu
 @parameterized.expand(lowercase_						,  name_func=lowercase_       )
 def 		_snake_case      (  self	: str						,  lowercase_	: List[Any]						,  lowercase_	: List[str]       ):
     self.run_and_check(
         stage=lowercase_						,  model=lowercase_						,  distributed=lowercase_						,  fpaa=lowercase_						,  )

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: Union[str, Any]       ):
     # 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 		_snake_case      (  self	: List[str]						,  lowercase_	: str						,  lowercase_	: str						,  lowercase_	: int = 10						,  lowercase_	: bool = True						,  lowercase_	: bool = True						,  lowercase_	: bool = True						,  ):
     snake_case_				:  Dict       =			models[model]

     snake_case_				:  List[str]       =			self.run_trainer(
         stage=lowercase_						,  model_name=lowercase_						,  eval_steps=lowercase_						,  num_train_epochs=1						,  distributed=lowercase_						,  fpaa=lowercase_						,  )

     self.do_checks(lowercase_       )

     return output_dir

 def 		_snake_case      (  self	: Tuple						,  lowercase_	: str						,  lowercase_	: str						,  lowercase_	: int = 10						,  lowercase_	: int = 1						,  lowercase_	: bool = True						,  lowercase_	: bool = True						,  ):
     snake_case_				:  Any       =			self.get_auto_remove_tmp_dir('''./xxx'''						,  after=lowercase_       )
     snake_case_				:  Any       =			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(lowercase_       )}\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_				:  Union[str, Any]       =			f"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json".split()
     snake_case_				:  List[Any]       =			[f"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"]
     snake_case_				:  List[str]       =			self.get_launcher(lowercase_       )

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

     return output_dir






 def 		_snake_case      (  self	: List[str]						,  lowercase_	: Union[str, 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_				:  Union[str, Any]       =			min(2						,  get_gpu_count()       ) if distributed else 1
     return f"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()










"""simple docstring"""

from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def __lowercase    (							_a					):
    return {key.lstrip('''-'''					): value for key, value in zip(unknown_args[::2]	,    unknown_args[1::2]					)}




def __lowercase    (							):
    snake_case_				:  List[str]       =			ArgumentParser(
        '''HuggingFace Datasets CLI tool'''	,    usage='''datasets-cli <command> [<args>]'''	,    allow_abbrev=_a					)
    snake_case_				:  List[Any]       =			parser.add_subparsers(help='''datasets-cli command helpers'''					)
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(_a					)
    EnvironmentCommand.register_subcommand(_a					)
    TestCommand.register_subcommand(_a					)
    RunBeamCommand.register_subcommand(_a					)
    DummyDataCommand.register_subcommand(_a					)

    # Parse args
    snake_case_,			snake_case_				:  Optional[Any]       =			parser.parse_known_args()
    if not hasattr(_a	,    '''func'''					):
        parser.print_help()
        exit(1					)
    snake_case_				:  Optional[int]       =			parse_unknown_args(_a					)

    # Run
    snake_case_				:  Optional[int]       =			args.func(_a	,    **_a					)
    service.run()


if __name__ == "__main__":
     main()





