





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCamelCase   				=			logging.get_logger(__name__)

_lowerCamelCase   				=			{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"deta"
							lowerCamelCase_           =					{
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							}


							def __init__(							self    :Dict							,				__A    :Tuple=None							,				__A    :Union[str, Any]=900							,				__A    :str=2048							,				__A    :int=6							,				__A    :Tuple=2048							,				__A    :List[Any]=8							,				__A    :List[Any]=6							,				__A    :Optional[Any]=1024							,				__A    :List[Any]=8							,				__A    :List[str]=0.0							,				__A    :int=True							,				__A    :Any="relu"							,				__A    :str=256							,				__A    :int=0.1							,				__A    :Union[str, Any]=0.0							,				__A    :Union[str, Any]=0.0							,				__A    :List[str]=0.0_2							,				__A    :Dict=1.0							,				__A    :str=True							,				__A    :Tuple=False							,				__A    :int="sine"							,				__A    :str=5							,				__A    :List[str]=4							,				__A    :Optional[int]=4							,				__A    :List[Any]=True							,				__A    :Dict=300							,				__A    :Optional[int]=True							,				__A    :List[Any]=True							,				__A    :List[Any]=1							,				__A    :List[str]=5							,				__A    :Any=2							,				__A    :Tuple=1							,				__A    :List[Any]=1							,				__A    :Any=5							,				__A    :List[Any]=2							,				__A    :Union[str, Any]=0.1							,				__A    :Optional[Any]=0.2_5							,				**__A    :int							,				)   ->   Any:



									"""simple docstring"""



									if backbone_config is None:
											logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
											SCREAMING_SNAKE_CASE__			=      CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]		)
									else:
											if isinstance(__A							,				__A		):
													SCREAMING_SNAKE_CASE__			=      backbone_config.pop("""model_type"""		)
													SCREAMING_SNAKE_CASE__			=      CONFIG_MAPPING[backbone_model_type]
													SCREAMING_SNAKE_CASE__			=      config_class.from_dict(__A		)

									SCREAMING_SNAKE_CASE__			=      backbone_config
									SCREAMING_SNAKE_CASE__			=      num_queries
									SCREAMING_SNAKE_CASE__			=      max_position_embeddings
									SCREAMING_SNAKE_CASE__			=      d_model
									SCREAMING_SNAKE_CASE__			=      encoder_ffn_dim
									SCREAMING_SNAKE_CASE__			=      encoder_layers
									SCREAMING_SNAKE_CASE__			=      encoder_attention_heads
									SCREAMING_SNAKE_CASE__			=      decoder_ffn_dim
									SCREAMING_SNAKE_CASE__			=      decoder_layers
									SCREAMING_SNAKE_CASE__			=      decoder_attention_heads
									SCREAMING_SNAKE_CASE__			=      dropout
									SCREAMING_SNAKE_CASE__			=      attention_dropout
									SCREAMING_SNAKE_CASE__			=      activation_dropout
									SCREAMING_SNAKE_CASE__			=      activation_function
									SCREAMING_SNAKE_CASE__			=      init_std
									SCREAMING_SNAKE_CASE__			=      init_xavier_std
									SCREAMING_SNAKE_CASE__			=      encoder_layerdrop
									SCREAMING_SNAKE_CASE__			=      auxiliary_loss
									SCREAMING_SNAKE_CASE__			=      position_embedding_type
									# deformable attributes
									SCREAMING_SNAKE_CASE__			=      num_feature_levels
									SCREAMING_SNAKE_CASE__			=      encoder_n_points
									SCREAMING_SNAKE_CASE__			=      decoder_n_points
									SCREAMING_SNAKE_CASE__			=      two_stage
									SCREAMING_SNAKE_CASE__			=      two_stage_num_proposals
									SCREAMING_SNAKE_CASE__			=      with_box_refine
									SCREAMING_SNAKE_CASE__			=      assign_first_stage
									if two_stage is True and with_box_refine is False:
											raise ValueError("""If two_stage is True, with_box_refine must be True."""		)
									# Hungarian matcher
									SCREAMING_SNAKE_CASE__			=      class_cost
									SCREAMING_SNAKE_CASE__			=      bbox_cost
									SCREAMING_SNAKE_CASE__			=      giou_cost
									# Loss coefficients
									SCREAMING_SNAKE_CASE__			=      mask_loss_coefficient
									SCREAMING_SNAKE_CASE__			=      dice_loss_coefficient
									SCREAMING_SNAKE_CASE__			=      bbox_loss_coefficient
									SCREAMING_SNAKE_CASE__			=      giou_loss_coefficient
									SCREAMING_SNAKE_CASE__			=      eos_coefficient
									SCREAMING_SNAKE_CASE__			=      focal_alpha
									super().__init__(is_encoder_decoder=__A							,				**__A		)


							@property
							def      _snake_case  (							self    :List[str]		)   ->   int:



									"""simple docstring"""



									return self.encoder_attention_heads


							@property
							def      _snake_case  (							self    :List[Any]		)   ->   int:



									"""simple docstring"""



									return self.d_model


							def      _snake_case  (							self    :Dict		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__			=      self.backbone_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.__class__.model_type
									return output






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   UpperCamelCase_       (     unittest.TestCase    ):


							def      _snake_case  (							self    :Any		)   ->   int:



									"""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[Any]		)   ->   Tuple:



									"""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(__A							,				env=os.environ.copy()		)


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



									"""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(__A							,				env=os.environ.copy()		)


							@require_multi_gpu
							def      _snake_case  (							self    :Optional[Any]		)   ->   Union[str, Any]:



									"""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(__A							,				env=os.environ.copy()		)


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



									"""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(__A							,				env=os.environ.copy()		)


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

	_lowerCamelCase   				=			''

	_lowerCamelCase   				=			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   				=			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   				=			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)