


# 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


_snake_case					:		Tuple      =    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)

_snake_case					:		List[str]      =    {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

_snake_case					:		List[Any]      =    "zero2"
_snake_case					:		Optional[Any]      =    "zero3"
_snake_case					:		Optional[int]      =    [ZEROa, ZEROa]







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# 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
							__snake_case		:      Optional[int]										=       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
_snake_case					:		List[str]      =    list(itertools.product(stages, models.keys()))

@slow
@require_deepspeed
@require_torch_gpu
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


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

							@require_torch_multi_gpu
							@parameterized.expand(lowerCamelCase   ,     name_func=lowerCamelCase    )
							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  List[str]    )     ->   str:
														self.run_and_check(
														    stage=lowerCamelCase   ,     model=lowerCamelCase   ,     distributed=lowerCamelCase   ,     fpaa=lowerCamelCase   ,     )

							@parameterized.expand(lowerCamelCase   ,     name_func=lowerCamelCase    )
							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  Dict    )     ->   Any:
														self.run_and_check(
														    stage=lowerCamelCase   ,     model=lowerCamelCase   ,     distributed=lowerCamelCase   ,     fpaa=lowerCamelCase   ,     )

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

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  int    )     ->   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	:  Union[str, Any]   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  int = 10   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  bool = True   ,     )     ->   Dict:
														__snake_case		:      List[str]										=       models[model]

														__snake_case		:      Tuple										=       self.run_trainer(
														    stage=lowerCamelCase   ,     model_name=lowerCamelCase   ,     eval_steps=lowerCamelCase   ,     num_train_epochs=1   ,     distributed=lowerCamelCase   ,     fpaa=lowerCamelCase   ,     )

														self.do_checks(lowerCamelCase    )

														return output_dir

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

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

														return output_dir






							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Tuple=False    )     ->   Dict:
														# 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)
														__snake_case		:      int										=       min(2   ,     get_gpu_count()    ) if distributed else 1
														return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()





from ..utils import DummyObject, requires_backends

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							int					  =  ["speech"]

							def __init__(							self	:  List[Any]   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Dict:
														requires_backends(self   ,     ["speech"]    )

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[Any]					  =  ["speech"]

							def __init__(							self	:  int   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  List[Any]    )     ->   Optional[int]:
														requires_backends(self   ,     ["speech"]    )

