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


_lowerCamelCase					:   Optional[Any]							=		logging.get_logger(__name__)

_lowerCamelCase					:   List[Any]							=		{
    '''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}


class 						lowercase    (     a     ):
							lowercase__						:       Optional[Any]								=							"""mra"""




							def __init__(						self   :					Optional[Any]	,							_UpperCamelCase   :					Union[str, Any]=50_265	,							_UpperCamelCase   :					Dict=768	,							_UpperCamelCase   :					List[str]=12	,							_UpperCamelCase   :					List[str]=12	,							_UpperCamelCase   :					str=3_072	,							_UpperCamelCase   :					List[Any]="gelu"	,							_UpperCamelCase   :					Tuple=0.1	,							_UpperCamelCase   :					str=0.1	,							_UpperCamelCase   :					Tuple=512	,							_UpperCamelCase   :					Tuple=1	,							_UpperCamelCase   :					Optional[Any]=0.0_2	,							_UpperCamelCase   :					Tuple=1e-5	,							_UpperCamelCase   :					List[Any]="absolute"	,							_UpperCamelCase   :					Any=4	,							_UpperCamelCase   :					str="full"	,							_UpperCamelCase   :					List[str]=0	,							_UpperCamelCase   :					Any=0	,							_UpperCamelCase   :					List[Any]=1	,							_UpperCamelCase   :					Any=0	,							_UpperCamelCase   :					Optional[Any]=2	,							**_UpperCamelCase   :					str	,							)					->				Union[str, Any]:



												'''simple docstring'''

												super().__init__(pad_token_id=_UpperCamelCase	,							bos_token_id=_UpperCamelCase	,							eos_token_id=_UpperCamelCase	,							**_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       vocab_size
												SCREAMING_SNAKE_CASE    			=       max_position_embeddings
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       type_vocab_size
												SCREAMING_SNAKE_CASE    			=       layer_norm_eps
												SCREAMING_SNAKE_CASE    			=       position_embedding_type
												SCREAMING_SNAKE_CASE    			=       block_per_row
												SCREAMING_SNAKE_CASE    			=       approx_mode
												SCREAMING_SNAKE_CASE    			=       initial_prior_first_n_blocks
												SCREAMING_SNAKE_CASE    			=       initial_prior_diagonal_n_blocks





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 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       inspect.getfile(accelerate.test_utils      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_script.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(
												    mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_distributed_data_loop.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_ops.py"]      )




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



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["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()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["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(_UpperCamelCase	,							env=os.environ.copy()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__      )]
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )





							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"      )
												SCREAMING_SNAKE_CASE    			=       ["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(_UpperCamelCase	,							env=os.environ.copy()      )


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

							_lowerCamelCase					:   Optional[Any]							=		''''''

							_lowerCamelCase					:   str							=		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)."

							_lowerCamelCase					:   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."
							_lowerCamelCase					:   int							=		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)




