


# 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


UpperCAmelCase_   :				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)

UpperCAmelCase_   :				Optional[int]						      = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

UpperCAmelCase_   :				Tuple						      = 'zero2'
UpperCAmelCase_   :				str						      = 'zero3'
UpperCAmelCase_   :				Union[str, Any]						      = [ZEROa, ZEROa]







def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Any						,					__A				:    Dict       )			->	Dict:

       """simple docstring"""

       a_      :	Tuple							=						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
UpperCAmelCase_   :				Tuple						      = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      @parameterized.expand(SCREAMING_SNAKE_CASE__  ,							name_func=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : str  )	->    Any:
             self.run_and_check(
                 stage=SCREAMING_SNAKE_CASE__  ,							model=SCREAMING_SNAKE_CASE__  ,							distributed=SCREAMING_SNAKE_CASE__  ,							fpaa=SCREAMING_SNAKE_CASE__  ,							)
      @require_torch_multi_gpu
      @parameterized.expand(SCREAMING_SNAKE_CASE__  ,							name_func=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : str  )	->    Optional[Any]:
             self.run_and_check(
                 stage=SCREAMING_SNAKE_CASE__  ,							model=SCREAMING_SNAKE_CASE__  ,							distributed=SCREAMING_SNAKE_CASE__  ,							fpaa=SCREAMING_SNAKE_CASE__  ,							)
      @parameterized.expand(SCREAMING_SNAKE_CASE__  ,							name_func=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Dict  )	->    Tuple:
             self.run_and_check(
                 stage=SCREAMING_SNAKE_CASE__  ,							model=SCREAMING_SNAKE_CASE__  ,							distributed=SCREAMING_SNAKE_CASE__  ,							fpaa=SCREAMING_SNAKE_CASE__  ,							)
      @require_torch_multi_gpu
      @parameterized.expand(SCREAMING_SNAKE_CASE__  ,							name_func=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  )	->    str:
             self.run_and_check(
                 stage=SCREAMING_SNAKE_CASE__  ,							model=SCREAMING_SNAKE_CASE__  ,							distributed=SCREAMING_SNAKE_CASE__  ,							fpaa=SCREAMING_SNAKE_CASE__  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]  )	->    Optional[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  SCREAMING_SNAKE_CASE							( self       : str  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : int = 1_0  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							)	->    Any:
             a_      :	Optional[Any]							=						models[model]

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

             self.do_checks(SCREAMING_SNAKE_CASE__  )

             return output_dir
      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : int = 1_0  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							)	->    Optional[Any]:
             a_      :	Any							=						self.get_auto_remove_tmp_dir('./xxx'  ,							after=SCREAMING_SNAKE_CASE__  )
             a_      :	List[str]							=						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(SCREAMING_SNAKE_CASE__  )}
            --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_      :	Dict							=						F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
             a_      :	str							=						[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
             a_      :	List[Any]							=						self.get_launcher(SCREAMING_SNAKE_CASE__  )

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

             return output_dir

      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[Any]=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_      :	Tuple							=						min(2  ,							get_gpu_count()  ) if distributed else 1
             return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()






def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    int						,					__A				:    int       )			->	float:

       """simple docstring"""

       a_      :	Optional[int]							=						(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
       # formula for sum of series
       return total







def  SCREAMING_SNAKE_CASE_		(    )			->	Optional[Any]:

       """simple docstring"""

       print(sum_of_series(1						,					1						,					10       )       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


