"""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


_a      :		List[str]           =   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)

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

_a      :		str           =   'zero2'
_a      :		Any           =   'zero3'
_a      :		Dict           =   [ZEROa, ZEROa]





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       str			,_lowerCamelCase						:       Optional[int]	)       ->      List[str]:
  # 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						:       Union[str, 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
_a      :		Tuple           =   list(itertools.product(stages, models.keys()))






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






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






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






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






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






     def     __A  (		self				,     a__  ):
       # 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     __A  (		self				,     a__				,     a__				,     a__ = 10				,     a__ = True				,     a__ = True				,     a__ = True				,     ):
       _lowerCAmelCase						:       Union[str, Any]  			=     models[model]

       _lowerCAmelCase						:       Union[str, Any]  			=     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     __A  (		self				,     a__				,     a__				,     a__ = 10				,     a__ = 1				,     a__ = True				,     a__ = True				,     ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.get_auto_remove_tmp_dir("""./xxx"""				,     after=a__  )
       _lowerCAmelCase						:       Tuple  			=     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(a__  )}\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
       _lowerCAmelCase						:       int  			=     F"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json".split()
       _lowerCAmelCase						:       Union[str, Any]  			=     [F"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"]
       _lowerCAmelCase						:       Tuple  			=     self.get_launcher(a__  )

       _lowerCAmelCase						:       Dict  			=     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     __A  (		self				,     a__=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						:       str  			=     min(2				,     get_gpu_count()  ) if distributed else 1
       return F"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a      :		List[Any]           =   logging.get_logger(__name__)

_a      :		Any           =   {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			int							=		"swinv2"

     _UpperCamelCase							:			List[str]							=		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self				,     a__=224				,     a__=4				,     a__=3				,     a__=96				,     a__=[2, 2, 6, 2]				,     a__=[3, 6, 12, 24]				,     a__=7				,     a__=4.0				,     a__=True				,     a__=0.0				,     a__=0.0				,     a__=0.1				,     a__="gelu"				,     a__=False				,     a__=0.0_2				,     a__=1e-5				,     a__=32				,     **a__				,     ):
       super().__init__(**a__  )

       _lowerCAmelCase						:       int  			=     image_size
       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       Any  			=     num_channels
       _lowerCAmelCase						:       List[Any]  			=     embed_dim
       _lowerCAmelCase						:       Optional[int]  			=     depths
       _lowerCAmelCase						:       List[Any]  			=     len(a__  )
       _lowerCAmelCase						:       Any  			=     num_heads
       _lowerCAmelCase						:       Tuple  			=     window_size
       _lowerCAmelCase						:       Tuple  			=     mlp_ratio
       _lowerCAmelCase						:       Any  			=     qkv_bias
       _lowerCAmelCase						:       Optional[int]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Tuple  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       str  			=     drop_path_rate
       _lowerCAmelCase						:       List[str]  			=     hidden_act
       _lowerCAmelCase						:       List[str]  			=     use_absolute_embeddings
       _lowerCAmelCase						:       Optional[int]  			=     layer_norm_eps
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:       Tuple  			=     int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:       Tuple  			=     (0, 0, 0, 0)




