





'''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 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		inspect.getfile(accelerate.test_utils       )
											_snake_case:   Any  					=		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

											_snake_case:   str  					=		test_metrics


					@require_cpu
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											debug_launcher(self.test_metrics.main       ,     num_processes=1       )


					@require_cpu
					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											debug_launcher(self.test_metrics.main       )


					@require_single_gpu
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											self.test_metrics.main()




					@require_multi_gpu
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											print(f'''Found {torch.cuda.device_count()} devices.'''       )
											_snake_case:   Optional[int]  					=		['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
											with patch_environment(omp_num_threads=1       ):
																	execute_subprocess_async(__snake_case       ,     env=os.environ.copy()       )












'''simple docstring'''






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


A   : Tuple             =			logging.get_logger(__name__)

A   : Any             =			{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    "biogpt"


					def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  Tuple=4_23_84       ,     __snake_case	:  Any=10_24       ,     __snake_case	:  Optional[int]=24       ,     __snake_case	:  int=16       ,     __snake_case	:  str=40_96       ,     __snake_case	:  Dict="gelu"       ,     __snake_case	:  List[str]=0.1       ,     __snake_case	:  List[Any]=0.1       ,     __snake_case	:  List[Any]=10_24       ,     __snake_case	:  Optional[int]=0.02       ,     __snake_case	:  Union[str, Any]=1e-12       ,     __snake_case	:  List[Any]=True       ,     __snake_case	:  Union[str, Any]=True       ,     __snake_case	:  Any=0.0       ,     __snake_case	:  int=0.0       ,     __snake_case	:  Dict=1       ,     __snake_case	:  str=0       ,     __snake_case	:  List[str]=2       ,     **__snake_case	:  Optional[int]       ,     ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		vocab_size
											_snake_case:   Optional[int]  					=		max_position_embeddings
											_snake_case:   Dict  					=		hidden_size
											_snake_case:   Union[str, Any]  					=		num_hidden_layers
											_snake_case:   str  					=		num_attention_heads
											_snake_case:   Any  					=		intermediate_size
											_snake_case:   List[Any]  					=		hidden_act
											_snake_case:   int  					=		hidden_dropout_prob
											_snake_case:   List[str]  					=		attention_probs_dropout_prob
											_snake_case:   Optional[Any]  					=		initializer_range
											_snake_case:   Union[str, Any]  					=		layer_norm_eps
											_snake_case:   Dict  					=		scale_embedding
											_snake_case:   Dict  					=		use_cache
											_snake_case:   List[Any]  					=		layerdrop
											_snake_case:   int  					=		activation_dropout
											super().__init__(pad_token_id=__snake_case       ,     bos_token_id=__snake_case       ,     eos_token_id=__snake_case       ,     **__snake_case       )





