

import sys
from pathlib import Path


_lowerCamelCase				:   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(4_2)

_lowerCamelCase				:   Union[str, Any]					   =							{"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

_lowerCamelCase				:   str					   =							"zero2"
_lowerCamelCase				:   Any					   =							"zero3"
_lowerCamelCase				:   Union[str, Any]					   =							[ZEROa, ZEROa]

def 			a__     (		UpperCAmelCase							:			Tuple				, UpperCAmelCase							:			Any				, UpperCAmelCase							:			str			)  ->						Union[str, 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
  UpperCAmelCase       :							Optional[Any]  = 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
_lowerCamelCase				:   str					   =							list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 					__UpperCAmelCase     (       A_						):



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



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



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



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



   def        __magic_name__    (							self							:     List[Any],		__A							:     Any						):
     pass



   def        __magic_name__    (							self							:     Tuple,		__A							:     str,		__A							:     str,		__A							:     int = 1_0,		__A							:     bool = True,		__A							:     bool = True,		__A							:     bool = True,		):
     UpperCAmelCase       :							Optional[int]  = models[model]

     UpperCAmelCase       :							Optional[int]  = self.run_trainer(
         stage=_snake_case,		model_name=_snake_case,		eval_steps=_snake_case,		num_train_epochs=1,		distributed=_snake_case,		fpaa=_snake_case,		)

     self.do_checks(_snake_case						)

     return output_dir



   def        __magic_name__    (							self							:     Any,		__A							:     str,		__A							:     str,		__A							:     int = 1_0,		__A							:     int = 1,		__A							:     bool = True,		__A							:     bool = True,		):
     UpperCAmelCase       :							Optional[int]  = self.get_auto_remove_tmp_dir('''./xxx''',		after=_snake_case						)
     UpperCAmelCase       :							Union[str, 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(_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
     UpperCAmelCase       :							Any  = F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
     UpperCAmelCase       :							Optional[int]  = [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
     UpperCAmelCase       :							int  = self.get_launcher(_snake_case						)

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

     return output_dir





   def        __magic_name__    (							self							:     Optional[Any],		__A							:     List[str]=False						):
     UpperCAmelCase       :							Optional[int]  = min(2,		get_gpu_count()						) if distributed else 1
     return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()










"""simple docstring"""







import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCAmelCase_   						=   4
lowerCAmelCase_   						=   3
class        __A       (	A_    ):







		'''simple docstring'''





		pass





def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for shard in shards:
				for i in range(__lowerCamelCase	):
						yield {"i": i, "shard": shard}



def      __UpperCAmelCase      (    )							->      Tuple:
		lowercase__   :    int				=			int(os.environ['''RANK''']	)
		lowercase__   :    str				=			int(os.environ['''WORLD_SIZE''']	)

		lowercase__   :    List[Any]				=			ArgumentParser()
		parser.add_argument('''--streaming'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--local_rank'''						,     type=__lowerCamelCase	)
		parser.add_argument('''--num_workers'''						,     type=__lowerCamelCase						,     default=0	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Optional[Any]				=			args.streaming
		lowercase__   :    List[Any]				=			args.num_workers

		lowercase__   :    Optional[Any]				=			{'''shards''': [f"""shard_{shard_idx}""" for shard_idx in range(__lowerCamelCase	)]}
		lowercase__   :    Dict				=			IterableDataset.from_generator(__lowerCamelCase						,     gen_kwargs=__lowerCamelCase	)
		if not streaming:
				lowercase__   :    int				=			Dataset.from_list(list(__lowerCamelCase	)	)

		lowercase__   :    int				=			split_dataset_by_node(__lowerCamelCase						,     rank=__lowerCamelCase						,     world_size=__lowerCamelCase	)
		lowercase__   :    Optional[Any]				=			torch.utils.data.DataLoader(__lowerCamelCase						,     num_workers=__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			NUM_SHARDS * NUM_ITEMS_PER_SHARD
		lowercase__   :    str				=			full_size // world_size
		expected_local_size += int(rank < (full_size % world_size)	)

		lowercase__   :    str				=			sum(1 for _ in dataloader	)
		if local_size != expected_local_size:
				raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
							main()
