



# 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


__A       =						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)

__A       =						{'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

__A       =						'''zero2'''
__A       =						'''zero3'''
__A       =						[ZEROa, ZEROa]



def 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Tuple    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       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
__A       =						list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 		lowercase						(			snake_case__):






	"""simple docstring"""


	@parameterized.expand(__UpperCAmelCase						,  name_func=__UpperCAmelCase					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						str					)      ->			Tuple:
							self.run_and_check(
							    stage=__UpperCAmelCase						,  model=__UpperCAmelCase						,  distributed=__UpperCAmelCase						,  fpaa=__UpperCAmelCase						,  )




	@require_torch_multi_gpu
	@parameterized.expand(__UpperCAmelCase						,  name_func=__UpperCAmelCase					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						int					)      ->			Union[str, Any]:
							self.run_and_check(
							    stage=__UpperCAmelCase						,  model=__UpperCAmelCase						,  distributed=__UpperCAmelCase						,  fpaa=__UpperCAmelCase						,  )




	@parameterized.expand(__UpperCAmelCase						,  name_func=__UpperCAmelCase					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						Optional[int]						,  __UpperCAmelCase     :						Optional[Any]					)      ->			int:
							self.run_and_check(
							    stage=__UpperCAmelCase						,  model=__UpperCAmelCase						,  distributed=__UpperCAmelCase						,  fpaa=__UpperCAmelCase						,  )




	@require_torch_multi_gpu
	@parameterized.expand(__UpperCAmelCase						,  name_func=__UpperCAmelCase					)
	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						str					)      ->			Union[str, Any]:
							self.run_and_check(
							    stage=__UpperCAmelCase						,  model=__UpperCAmelCase						,  distributed=__UpperCAmelCase						,  fpaa=__UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						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   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						int = 10						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						bool = True						,  )      ->			Union[str, Any]:
							UpperCAmelCase_=       models[model]

							UpperCAmelCase_=       self.run_trainer(
							    stage=__UpperCAmelCase						,  model_name=__UpperCAmelCase						,  eval_steps=__UpperCAmelCase						,  num_train_epochs=1						,  distributed=__UpperCAmelCase						,  fpaa=__UpperCAmelCase						,  )

							self.do_checks(__UpperCAmelCase					)

							return output_dir




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

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

							return output_dir




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						Optional[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)
							UpperCAmelCase_=       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 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int    )				->			list[list[int]]:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       []
						UpperCAmelCase_=       0
						UpperCAmelCase_=       sum(lowerCAmelCase_    )
						create_state_space_tree(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						return result



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : list[list[int]]      ,lowerCAmelCase_       : int      ,)				->			None:






						'''simple docstring'''



						if sum(lowerCAmelCase_    ) > max_sum or (remaining_nums_sum + sum(lowerCAmelCase_    )) < max_sum:
												return
						if sum(lowerCAmelCase_    ) == max_sum:
												result.append(lowerCAmelCase_    )
												return
						for index in range(lowerCAmelCase_      ,len(lowerCAmelCase_    )    ):
												create_state_space_tree(
												    lowerCAmelCase_      ,lowerCAmelCase_      ,index + 1      ,[*path, nums[index]]      ,lowerCAmelCase_      ,remaining_nums_sum - nums[index]      ,)
__A       =						[3, 34, 4, 12, 5, 2]
__A       =						9
__A       =						generate_sum_of_subsets_soln(nums, max_sum)



print(*result)




