'''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


SCREAMING_SNAKE_CASE_:     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)

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

SCREAMING_SNAKE_CASE_:     Union[str, Any]  ='zero2'
SCREAMING_SNAKE_CASE_:     Dict  ='zero3'
SCREAMING_SNAKE_CASE_:     Optional[Any]  =[ZEROa, ZEROa]





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]					,    snake_case_       : Optional[Any]					,    snake_case_       : Optional[int]			)    ->      Optional[int]:


     '''simple docstring'''




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


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






@slow
@require_deepspeed
@require_torch_gpu
class        __A      ( UpperCamelCase__	):



    @parameterized.expand(__a   ,			name_func=__a )
    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Dict   ,			__a    :    Any ):
         self.run_and_check(
             stage=__a   ,			model=__a   ,			distributed=__a   ,			fpaa=__a   ,			)



    @require_torch_multi_gpu
    @parameterized.expand(__a   ,			name_func=__a )
    def 					_lowercase						(self    :    Tuple   ,			__a    :    Dict   ,			__a    :    List[Any] ):
         self.run_and_check(
             stage=__a   ,			model=__a   ,			distributed=__a   ,			fpaa=__a   ,			)



    @parameterized.expand(__a   ,			name_func=__a )
    def 					_lowercase						(self    :    Tuple   ,			__a    :    Any   ,			__a    :    str ):
         self.run_and_check(
             stage=__a   ,			model=__a   ,			distributed=__a   ,			fpaa=__a   ,			)



    @require_torch_multi_gpu
    @parameterized.expand(__a   ,			name_func=__a )
    def 					_lowercase						(self    :    int   ,			__a    :    Union[str, Any]   ,			__a    :    Any ):
         self.run_and_check(
             stage=__a   ,			model=__a   ,			distributed=__a   ,			fpaa=__a   ,			)



    def 					_lowercase						(self    :    str   ,			__a    :    Optional[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 					_lowercase						(self    :    List[str]   ,			__a    :    str   ,			__a    :    str   ,			__a    :    int = 10   ,			__a    :    bool = True   ,			__a    :    bool = True   ,			__a    :    bool = True   ,			):
         UpperCAmelCase_           =   models[model]

         UpperCAmelCase_           =   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 					_lowercase						(self    :    Any   ,			__a    :    str   ,			__a    :    str   ,			__a    :    int = 10   ,			__a    :    int = 1   ,			__a    :    bool = True   ,			__a    :    bool = True   ,			):
         UpperCAmelCase_           =   self.get_auto_remove_tmp_dir("./xxx"   ,			after=__a )
         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(__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
         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(__a )

         UpperCAmelCase_           =   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 					_lowercase						(self    :    Any   ,			__a    :    List[str]=False ):
         # 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()






'''simple docstring'''







from typing import TYPE_CHECKING

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


_lowercase :  Union[str, Any]    							=       {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  str    							=       [
							    "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 :  Dict    							=       _LazyModule(__name__, globals()["__file__"], _import_structure)



