


'''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


__lowercase	:						List[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	:						Dict       =	{'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

__lowercase	:						int       =	'zero2'
__lowercase	:						Dict       =	'zero3'
__lowercase	:						List[Any]       =	[ZEROa, ZEROa]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Any   ):
					# 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			:       Dict					    =  parameterized.to_safe_name('_'.join(str(_SCREAMING_SNAKE_CASE   ) for x in param.args   )   )
					return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
__lowercase	:						List[Any]       =	list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 							__UpperCamelCase				(		lowerCAmelCase_					):




	@parameterized.expand(__a						,				name_func=__a							)
	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						self.run_and_check(
						    stage=__a						,				model=__a						,				distributed=__a						,				fpaa=__a						,				)




	@require_torch_multi_gpu
	@parameterized.expand(__a						,				name_func=__a							)
	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						self.run_and_check(
						    stage=__a						,				model=__a						,				distributed=__a						,				fpaa=__a						,				)




	@parameterized.expand(__a						,				name_func=__a							)
	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						self.run_and_check(
						    stage=__a						,				model=__a						,				distributed=__a						,				fpaa=__a						,				)




	@require_torch_multi_gpu
	@parameterized.expand(__a						,				name_func=__a							)
	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						self.run_and_check(
						    stage=__a						,				model=__a						,				distributed=__a						,				fpaa=__a						,				)




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						pass




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a = 10						,				__a = True						,				__a = True						,				__a = True						,				):

						'''simple docstring'''





						__a			:       Any					    =  models[model]

						__a			:       Union[str, Any]					    =  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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a = 10						,				__a = 1						,				__a = True						,				__a = True						,				):

						'''simple docstring'''





						__a			:       Tuple					    =  self.get_auto_remove_tmp_dir('./xxx'						,				after=__a							)
						__a			:       Optional[Any]					    =  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(__a							)}
            --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			:       int					    =  f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
						__a			:       Union[str, Any]					    =  [f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
						__a			:       Dict					    =  self.get_launcher(__a							)

						__a			:       Tuple					    =  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       __UpperCAmelCase							(   self						,				__a=False							):

						'''simple docstring'''





						__a			:       int					    =  min(2						,				get_gpu_count()							) if distributed else 1
						return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()







'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       int     		=      0
				while len(SCREAMING_SNAKE_CASE__     ) > 1:
								_SCREAMING_SNAKE_CASE :       Any     		=      0
								# Consider two files with minimum cost to be merged
								for _ in range(2     ):
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      files.index(min(SCREAMING_SNAKE_CASE__     )     )
												temp += files[min_index]
												files.pop(SCREAMING_SNAKE_CASE__     )
								files.append(SCREAMING_SNAKE_CASE__     )
								optimal_merge_cost += temp
				return optimal_merge_cost


if __name__ == "__main__":
				import doctest

				doctest.testmod()




