





'''simple docstring'''

# 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


__lowerCAmelCase							:     Tuple		     =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)

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

__lowerCAmelCase							:     List[str]		     ="zero2"
__lowerCAmelCase							:     Union[str, Any]		     ="zero3"
__lowerCAmelCase							:     List[Any]		     =[ZEROa, ZEROa]





def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : str	):
    # 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__    =      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
__lowerCAmelCase							:     List[Any]		     =list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class        UpperCAmelCase     (	UpperCamelCase__    ):
 @parameterized.expand(lowercase_						,	name_func=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Union[str, Any]						,	lowercase_				:Dict							)->							Optional[Any]:
     self.run_and_check(
         stage=lowercase_						,	model=lowercase_						,	distributed=lowercase_						,	fpaa=lowercase_						,	)
 @require_torch_multi_gpu
 @parameterized.expand(lowercase_						,	name_func=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:int						,	lowercase_				:Any							)->							Optional[int]:
     self.run_and_check(
         stage=lowercase_						,	model=lowercase_						,	distributed=lowercase_						,	fpaa=lowercase_						,	)
 @parameterized.expand(lowercase_						,	name_func=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:str						,	lowercase_				:Tuple							)->							List[Any]:
     self.run_and_check(
         stage=lowercase_						,	model=lowercase_						,	distributed=lowercase_						,	fpaa=lowercase_						,	)
 @require_torch_multi_gpu
 @parameterized.expand(lowercase_						,	name_func=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:List[Any]						,	lowercase_				:Union[str, Any]							)->							Tuple:
     self.run_and_check(
         stage=lowercase_						,	model=lowercase_						,	distributed=lowercase_						,	fpaa=lowercase_						,	)
 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:Union[str, Any]							)->							int:
     # 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 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:str						,	lowercase_				:str						,	lowercase_				:int = 10						,	lowercase_				:bool = True						,	lowercase_				:bool = True						,	lowercase_				:bool = True						,	)->							List[str]:
     A__    =      models[model]

     A__    =      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 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:str						,	lowercase_				:str						,	lowercase_				:int = 10						,	lowercase_				:int = 1						,	lowercase_				:bool = True						,	lowercase_				:bool = True						,	)->							int:
     A__    =      self.get_auto_remove_tmp_dir("./xxx"						,	after=lowercase_							)
     A__    =      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
     A__    =      F"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json".split()
     A__    =      [F"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"]
     A__    =      self.get_launcher(lowercase_							)

     A__    =      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 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:int=False							)->							str:
     # 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__    =      min(2						,	get_gpu_count()							) if distributed else 1
     return F"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()










'''simple docstring'''

from .imports import is_tqdm_available


if is_tqdm_available():
  from tqdm.auto import tqdm as _tqdm

from ..state import PartialState





def 						UpperCamelCase    (							_lowerCamelCase : bool = True						,	*_lowerCamelCase : Optional[int]						,	**_lowerCamelCase : Optional[Any]	):
    if not is_tqdm_available():
        raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."	)
    A__    =      False
    if main_process_only:
        A__    =      PartialState().local_process_index == 0
    return _tqdm(*_lowerCamelCase						,	**_lowerCamelCase						,	disable=_lowerCamelCase	)



