
"""simple docstring"""


import logging

from transformers.configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE      =     logging.getLogger(__name__)



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					List[Any]   =			'''masked_bert'''






       def __init__(  self :							str       ,      UpperCAmelCase_ :							Optional[Any]=30_522       ,      UpperCAmelCase_ :							str=768       ,      UpperCAmelCase_ :							Tuple=12       ,      UpperCAmelCase_ :							Any=12       ,      UpperCAmelCase_ :							int=3_072       ,      UpperCAmelCase_ :							Tuple="gelu"       ,      UpperCAmelCase_ :							List[Any]=0.1       ,      UpperCAmelCase_ :							Tuple=0.1       ,      UpperCAmelCase_ :							List[str]=512       ,      UpperCAmelCase_ :							Tuple=2       ,      UpperCAmelCase_ :							List[Any]=0.02       ,      UpperCAmelCase_ :							Tuple=1e-12       ,      UpperCAmelCase_ :							int=0       ,      UpperCAmelCase_ :							Tuple="topK"       ,      UpperCAmelCase_ :							Union[str, Any]="constant"       ,      UpperCAmelCase_ :							Tuple=0.0       ,      **UpperCAmelCase_ :							Optional[Any]       ,      )->  Tuple:







              """simple docstring"""
              super().__init__(pad_token_id=UpperCAmelCase_       ,      **UpperCAmelCase_			)

              UpperCamelCase					 =		vocab_size
              UpperCamelCase					 =		hidden_size
              UpperCamelCase					 =		num_hidden_layers
              UpperCamelCase					 =		num_attention_heads
              UpperCamelCase					 =		hidden_act
              UpperCamelCase					 =		intermediate_size
              UpperCamelCase					 =		hidden_dropout_prob
              UpperCamelCase					 =		attention_probs_dropout_prob
              UpperCamelCase					 =		max_position_embeddings
              UpperCamelCase					 =		type_vocab_size
              UpperCamelCase					 =		initializer_range
              UpperCamelCase					 =		layer_norm_eps
              UpperCamelCase					 =		pruning_method
              UpperCamelCase					 =		mask_init
              UpperCamelCase					 =		mask_scale







"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment



@require_huggingface_suite
class   __a  (      unittest.TestCase	):






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		inspect.getfile(accelerate.test_utils			)
              UpperCamelCase					 =		os.path.sep.join(
                  mod_file.split(os.path.sep			)[:-1] + ["scripts", "external_deps", "test_metrics.py"]			)

              from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

              UpperCamelCase					 =		test_metrics






       @require_cpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Union[str, Any]:







              """simple docstring"""
              debug_launcher(self.test_metrics.main       ,      num_processes=1			)






       @require_cpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  List[str]:







              """simple docstring"""
              debug_launcher(self.test_metrics.main			)






       @require_single_gpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  str:







              """simple docstring"""
              self.test_metrics.main()






       @require_multi_gpu
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Optional[Any]:







              """simple docstring"""
              print(f"Found {torch.cuda.device_count()} devices."			)
              UpperCamelCase					 =		["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
              with patch_environment(omp_num_threads=1			):
                     execute_subprocess_async(UpperCAmelCase_       ,      env=os.environ.copy()			)





