





import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce





def       lowercase_      (		lowercase__						)   ->Optional[int]:
						return (torch.arange(state.num_processes						) + 1.0 + (state.num_processes * state.process_index)).to(state.device						)





def       lowercase_      (		lowercase__						)   ->Dict:
						_snake_case:   int  					=		create_tensor(lowercase__						)
						_snake_case:   Dict  					=		gather(lowercase__						)
						assert gathered_tensor.tolist() == list(range(1				,  state.num_processes**2 + 1						)						)





def       lowercase_      (		lowercase__						)   ->Union[str, Any]:
						_snake_case:   str  					=		[state.process_index]
						_snake_case:   Union[str, Any]  					=		gather_object(lowercase__						)
						assert len(lowercase__						) == state.num_processes, F'''{gathered_obj}, {len(lowercase__						)} != {state.num_processes}'''
						assert gathered_obj == list(range(state.num_processes						)						), F'''{gathered_obj} != {list(range(state.num_processes						)						)}'''





def       lowercase_      (		lowercase__						)   ->List[str]:
						_snake_case:   str  					=		create_tensor(lowercase__						)
						_snake_case:   Any  					=		broadcast(lowercase__						)
						assert broadcasted_tensor.shape == torch.Size([state.num_processes]						)
						assert broadcasted_tensor.tolist() == list(range(1				,  state.num_processes + 1						)						)





def       lowercase_      (		lowercase__						)   ->Dict:
						# We need to pad the tensor with one more element if we are the main process
						# to ensure that we can pad
						if state.is_main_process:
												_snake_case:   Any  					=		torch.arange(state.num_processes + 1						).to(state.device						)
						else:
												_snake_case:   List[str]  					=		torch.arange(state.num_processes						).to(state.device						)
						_snake_case:   str  					=		pad_across_processes(lowercase__						)
						assert padded_tensor.shape == torch.Size([state.num_processes + 1]						)
						if not state.is_main_process:
												assert padded_tensor.tolist() == list(range(0				,  state.num_processes						)						) + [0]





def       lowercase_      (		lowercase__						)   ->List[Any]:
						# For now runs on only two processes
						if state.num_processes != 2:
												return
						_snake_case:   Union[str, Any]  					=		create_tensor(lowercase__						)
						_snake_case:   List[str]  					=		reduce(lowercase__				,  'sum'						)
						_snake_case:   Optional[Any]  					=		torch.tensor([4.0, 6]						).to(state.device						)
						assert torch.allclose(lowercase__				,  lowercase__						), F'''{reduced_tensor} != {truth_tensor}'''





def       lowercase_      (		lowercase__						)   ->Union[str, Any]:
						# For now runs on only two processes
						if state.num_processes != 2:
												return
						_snake_case:   Dict  					=		create_tensor(lowercase__						)
						_snake_case:   Optional[int]  					=		reduce(lowercase__				,  'mean'						)
						_snake_case:   Tuple  					=		torch.tensor([2.0, 3]						).to(state.device						)
						assert torch.allclose(lowercase__				,  lowercase__						), F'''{reduced_tensor} != {truth_tensor}'''





def       lowercase_      (		lowercase__						)   ->Optional[int]:
						# For xla_spawn (TPUs)
						main()





def       lowercase_      (		)   ->Optional[Any]:
						_snake_case:   str  					=		PartialState()
						state.print(F'''State: {state}'''						)
						state.print('testing gather'						)
						test_gather(lowercase__						)
						state.print('testing gather_object'						)
						test_gather_object(lowercase__						)
						state.print('testing broadcast'						)
						test_broadcast(lowercase__						)
						state.print('testing pad_across_processes'						)
						test_pad_across_processes(lowercase__						)
						state.print('testing reduce_sum'						)
						test_reduce_sum(lowercase__						)
						state.print('testing reduce_mean'						)
						test_reduce_mean(lowercase__						)


if __name__ == "__main__":
		main()












'''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       )





