





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		inspect.getfile(accelerate.test_utils       )
											_snake_case:   str  					=		os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_script.py']       )
											_snake_case:   Optional[int]  					=		os.path.sep.join(
											    mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_distributed_data_loop.py']       )
											_snake_case:   Union[str, Any]  					=		os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_ops.py']       )


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





											'''simple docstring'''





											print(f'''Found {torch.cuda.device_count()} devices.'''       )
											_snake_case:   Tuple  					=		['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()       )


					@require_multi_gpu
					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





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


					@require_multi_gpu
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





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




					@require_multi_gpu
					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''       )
											_snake_case:   Dict  					=		['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
											with patch_environment(omp_num_threads=1       ,     cuda_visible_devices='0,1'       ):
																	execute_subprocess_async(__snake_case       ,     env=os.environ.copy()       )


if __name__ == "__main__":
		A   : Dict             =			Accelerator()
		A   : List[str]             =			(accelerator.state.process_index + 2, 10)
		A   : List[Any]             =			torch.randint(0, 10, shape).to(accelerator.device)

		A   : List[str]             =			''

		A   : Union[str, Any]             =			accelerator.pad_across_processes(tensor)
		if tensora.shape[0] != accelerator.state.num_processes + 1:
				error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
		if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
				error_msg += "Tensors have different values."
		if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
				error_msg += "Padding was not done with the right value (0)."

		A   : Optional[Any]             =			accelerator.pad_across_processes(tensor, pad_first=True)
		if tensora.shape[0] != accelerator.state.num_processes + 1:
				error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
		A   : str             =			accelerator.state.num_processes - accelerator.state.process_index - 1
		if not torch.equal(tensora[index:], tensor):
				error_msg += "Tensors have different values."
		if not torch.all(tensora[:index] == 0):
				error_msg += "Padding was not done with the right value (0)."

		# Raise error at the end to make sure we don't stop at the first failure.
		if len(error_msg) > 0:
				raise ValueError(error_msg)












'''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       )





