
# 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


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

_lowercase											=    """zero2"""
_lowercase											=    """zero3"""
_lowercase											=    [ZEROa, ZEROa]
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Any			,							UpperCAmelCase_	:     Tuple			,							UpperCAmelCase_	:     Dict				)			->						Optional[int]:
       # 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
       SCREAMING_SNAKE_CASE_							:				Dict		=parameterized.to_safe_name('''_'''.join(str(UpperCAmelCase_				) for x in param.args				)				)
       return f'{func.__name__}_{param_based_name}'


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


@slow
@require_deepspeed
@require_torch_gpu
class 			lowercase_		(      A       ):


       @parameterized.expand(__A						,       name_func=__A				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    List[str]:
              self.run_and_check(
                  stage=__A						,       model=__A						,       distributed=__A						,       fpaa=__A						,       )


       @require_torch_multi_gpu
       @parameterized.expand(__A						,       name_func=__A				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Any:
              self.run_and_check(
                  stage=__A						,       model=__A						,       distributed=__A						,       fpaa=__A						,       )


       @parameterized.expand(__A						,       name_func=__A				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    str:
              self.run_and_check(
                  stage=__A						,       model=__A						,       distributed=__A						,       fpaa=__A						,       )


       @require_torch_multi_gpu
       @parameterized.expand(__A						,       name_func=__A				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Optional[int]:
              self.run_and_check(
                  stage=__A						,       model=__A						,       distributed=__A						,       fpaa=__A						,       )


       def 	_snake_case				(    self						,       __A				)						->    Dict:
              # 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						,       __A						,       __A						,       __A = 10						,       __A = True						,       __A = True						,       __A = True						,       )						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Tuple		=models[model]

              SCREAMING_SNAKE_CASE_							:				int		=self.run_trainer(
                  stage=__A						,       model_name=__A						,       eval_steps=__A						,       num_train_epochs=1						,       distributed=__A						,       fpaa=__A						,       )

              self.do_checks(__A				)

              return output_dir


       def 	_snake_case				(    self						,       __A						,       __A						,       __A = 10						,       __A = 1						,       __A = True						,       __A = True						,       )						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				int		=self.get_auto_remove_tmp_dir('''./xxx'''						,       after=__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=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(__A				)}\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
              SCREAMING_SNAKE_CASE_							:				List[str]		=F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_launcher(__A				)

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

              return output_dir





       def 	_snake_case				(    self						,       __A=False				)						->    Union[str, Any]:
              # 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)
              SCREAMING_SNAKE_CASE_							:				int		=min(2						,       get_gpu_count()				) if distributed else 1
              return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()


from __future__ import annotations
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list[int | float]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     int				)			->						int | float:
       if len(UpperCAmelCase_				) == 0:
              raise ValueError('''find_max() arg is an empty sequence'''				)
       if (
           left >= len(UpperCAmelCase_				)
           or left < -len(UpperCAmelCase_				)
           or right >= len(UpperCAmelCase_				)
           or right < -len(UpperCAmelCase_				)
       ):
              raise IndexError('''list index out of range'''				)
       if left == right:
              return nums[left]
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=(left + right) >> 1  # the middle
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)  # find max in range[left, mid]
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							mid + 1			,							UpperCAmelCase_				)  # find max in range[mid + 1, right]

       return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)
