



# 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


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(4_2)

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

UpperCAmelCase_									=							"zero2"
UpperCAmelCase_									=							"zero3"
UpperCAmelCase_									=							[ZEROa, ZEROa]
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]				)->Tuple:
						# 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
						_lowerCAmelCase 			=  parameterized.to_safe_name('''_'''.join(str(_SCREAMING_SNAKE_CASE				) 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 		UpperCAmelCase						(						snake_case_				):





			@parameterized.expand(_lowerCAmelCase       ,     name_func=_lowerCAmelCase			)
			def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
									self.run_and_check(
									    stage=_lowerCAmelCase       ,     model=_lowerCAmelCase       ,     distributed=_lowerCAmelCase       ,     fpaa=_lowerCAmelCase       ,     )





			@require_torch_multi_gpu
			@parameterized.expand(_lowerCAmelCase       ,     name_func=_lowerCAmelCase			)
			def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
									self.run_and_check(
									    stage=_lowerCAmelCase       ,     model=_lowerCAmelCase       ,     distributed=_lowerCAmelCase       ,     fpaa=_lowerCAmelCase       ,     )





			@parameterized.expand(_lowerCAmelCase       ,     name_func=_lowerCAmelCase			)
			def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
									self.run_and_check(
									    stage=_lowerCAmelCase       ,     model=_lowerCAmelCase       ,     distributed=_lowerCAmelCase       ,     fpaa=_lowerCAmelCase       ,     )





			@require_torch_multi_gpu
			@parameterized.expand(_lowerCAmelCase       ,     name_func=_lowerCAmelCase			)
			def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
									self.run_and_check(
									    stage=_lowerCAmelCase       ,     model=_lowerCAmelCase       ,     distributed=_lowerCAmelCase       ,     fpaa=_lowerCAmelCase       ,     )





			def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
									# 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 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = 10       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = True       ,     ):
									_lowerCAmelCase 			=  models[model]

									_lowerCAmelCase 			=  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 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = 10       ,     _lowerCAmelCase = 1       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = True       ,     ):
									_lowerCAmelCase 			=  self.get_auto_remove_tmp_dir('''./xxx'''       ,     after=_lowerCAmelCase			)
									_lowerCAmelCase 			=  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
									_lowerCAmelCase 			=  F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
									_lowerCAmelCase 			=  [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
									_lowerCAmelCase 			=  self.get_launcher(_lowerCAmelCase			)

									_lowerCAmelCase 			=  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 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase=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)
									_lowerCAmelCase 			=  min(2       ,     get_gpu_count()			) if distributed else 1
									return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_									=							{"processing_layoutxlm": ["LayoutXLMProcessor"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["LayoutXLMTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["LayoutXLMTokenizerFast"]

if TYPE_CHECKING:
    from .processing_layoutxlm import LayoutXLMProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm import LayoutXLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)