





"""simple docstring"""

import sys
from pathlib import Path


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

__UpperCAmelCase        ="""zero2"""
__UpperCAmelCase        ="""zero3"""
__UpperCAmelCase        =[ZEROa, ZEROa]



def        __a							(			A ,	A ,	A )       ->						Dict:



   '''simple docstring'''



   A__											=					parameterized.to_safe_name("_".join(str(A ) for x in param.args ) )
   return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
__UpperCAmelCase        =list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class       lowerCAmelCase__				(   __a  ):







 @parameterized.expand(_lowerCamelCase		,	name_func=_lowerCamelCase					)
 def  lowercase_       (   self		,	UpperCamelCase__		,	UpperCamelCase__					):

    '''simple docstring'''


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







 @require_torch_multi_gpu
 @parameterized.expand(_lowerCamelCase		,	name_func=_lowerCamelCase					)
 def  lowercase_       (   self		,	UpperCamelCase__		,	UpperCamelCase__					):

    '''simple docstring'''


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







 @parameterized.expand(_lowerCamelCase		,	name_func=_lowerCamelCase					)
 def  lowercase_       (   self		,	UpperCamelCase__		,	UpperCamelCase__					):

    '''simple docstring'''


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







 @require_torch_multi_gpu
 @parameterized.expand(_lowerCamelCase		,	name_func=_lowerCamelCase					)
 def  lowercase_       (   self		,	UpperCamelCase__		,	UpperCamelCase__					):

    '''simple docstring'''


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







 def  lowercase_       (   self		,	UpperCamelCase__					):

    '''simple docstring'''


    pass







 def  lowercase_       (   self		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__ = 10		,	UpperCamelCase__ = True		,	UpperCamelCase__ = True		,	UpperCamelCase__ = True		,	):

    '''simple docstring'''


    A__											=					models[model]

    A__											=					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  lowercase_       (   self		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__ = 10		,	UpperCamelCase__ = 1		,	UpperCamelCase__ = True		,	UpperCamelCase__ = True		,	):

    '''simple docstring'''


    A__											=					self.get_auto_remove_tmp_dir("./xxx"		,	after=_lowerCamelCase					)
    A__											=					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(_lowerCamelCase					)}
            --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__											=					f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
    A__											=					[f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
    A__											=					self.get_launcher(_lowerCamelCase					)

    A__											=					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  lowercase_       (   self		,	UpperCamelCase__=False					):

    '''simple docstring'''


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

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase							=				{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				['''Mask2FormerImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Mask2FormerForUniversalSegmentation''',
					    '''Mask2FormerModel''',
					    '''Mask2FormerPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure)





