






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

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





def 	lowerCAmelCase_		(lowercase__						:       List[Any]     ,					lowercase__						:       Optional[int]     ,					lowercase__						:       List[str]       )		->    Any:


    '''simple docstring'''




    lowerCAmelCase__      			=						parameterized.to_safe_name('''_'''.join(str(lowercase__       ) 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  lowerCAmelCase_       (				snake_case__					):




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




       def 						__snake_case					(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   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     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   int = 10 ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							):
           lowerCAmelCase__      			=						models[model]

           lowerCAmelCase__      			=						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 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   int = 10 ,							SCREAMING_SNAKE_CASE_     :   int = 1 ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							):
           lowerCAmelCase__      			=						self.get_auto_remove_tmp_dir('''./xxx''' ,							after=SCREAMING_SNAKE_CASE_	)
           lowerCAmelCase__      			=						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(SCREAMING_SNAKE_CASE_	)}\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
           lowerCAmelCase__      			=						f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
           lowerCAmelCase__      			=						[f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
           lowerCAmelCase__      			=						self.get_launcher(SCREAMING_SNAKE_CASE_	)

           lowerCAmelCase__      			=						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 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   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)
           lowerCAmelCase__      			=						min(2 ,							get_gpu_count()	) if distributed else 1
           return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()













from pathlib import Path

import fire





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       str     ,					lowercase__						:       int       )		->    Dict:


				'''simple docstring'''




				lowerCAmelCase__      			=						Path(lowercase__       )
				lowerCAmelCase__      			=						Path(lowercase__       )
				dest_dir.mkdir(exist_ok=lowercase__       )
				for path in src_dir.iterdir():
								lowerCAmelCase__      			=						[x.rstrip() for x in list(path.open().readlines()       )][:n]
								lowerCAmelCase__      			=						dest_dir.joinpath(path.name       )
								print(lowercase__       )
								dest_path.open('''w'''       ).write('''\n'''.join(lowercase__       )       )


if __name__ == "__main__":
			fire.Fire(minify)





