
"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

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



def 				lowerCamelCase__		(      __snake_case     )							->		List[Any]:


					"""simple docstring"""





					return (torch.arange(state.num_processes     ) + 1.0 + (state.num_processes * state.process_index)).to(state.device     )



def 				lowerCamelCase__		(      __snake_case     )							->		Tuple:


					"""simple docstring"""





					_UpperCamelCase										=  create_tensor(__snake_case     )
					_UpperCamelCase										=  gather(__snake_case     )
					assert gathered_tensor.tolist() == list(range(1,					state.num_processes**2 + 1     )     )



def 				lowerCamelCase__		(      __snake_case     )							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  [state.process_index]
					_UpperCamelCase										=  gather_object(__snake_case     )
					assert len(__snake_case     ) == state.num_processes, F'''{gathered_obj}, {len(__snake_case     )} != {state.num_processes}'''
					assert gathered_obj == list(range(state.num_processes     )     ), F'''{gathered_obj} != {list(range(state.num_processes     )     )}'''



def 				lowerCamelCase__		(      __snake_case     )							->		Optional[int]:


					"""simple docstring"""





					_UpperCamelCase										=  create_tensor(__snake_case     )
					_UpperCamelCase										=  broadcast(__snake_case     )
					assert broadcasted_tensor.shape == torch.Size([state.num_processes]     )
					assert broadcasted_tensor.tolist() == list(range(1,					state.num_processes + 1     )     )



def 				lowerCamelCase__		(      __snake_case     )							->		int:


					"""simple docstring"""





					if state.is_main_process:
										_UpperCamelCase										=  torch.arange(state.num_processes + 1     ).to(state.device     )
					else:
										_UpperCamelCase										=  torch.arange(state.num_processes     ).to(state.device     )
					_UpperCamelCase										=  pad_across_processes(__snake_case     )
					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 				lowerCamelCase__		(      __snake_case     )							->		Dict:


					"""simple docstring"""





					if state.num_processes != 2:
										return
					_UpperCamelCase										=  create_tensor(__snake_case     )
					_UpperCamelCase										=  reduce(__snake_case,					'''sum'''     )
					_UpperCamelCase										=  torch.tensor([4.0, 6]     ).to(state.device     )
					assert torch.allclose(__snake_case,					__snake_case     ), F'''{reduced_tensor} != {truth_tensor}'''



def 				lowerCamelCase__		(      __snake_case     )							->		Optional[int]:


					"""simple docstring"""





					if state.num_processes != 2:
										return
					_UpperCamelCase										=  create_tensor(__snake_case     )
					_UpperCamelCase										=  reduce(__snake_case,					'''mean'''     )
					_UpperCamelCase										=  torch.tensor([2.0, 3]     ).to(state.device     )
					assert torch.allclose(__snake_case,					__snake_case     ), F'''{reduced_tensor} != {truth_tensor}'''



def 				lowerCamelCase__		(      __snake_case     )							->		Any:


					"""simple docstring"""





					main()



def 				lowerCamelCase__		(      )							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase										=  PartialState()
					state.print(F'''State: {state}'''     )
					state.print('''testing gather'''     )
					test_gather(__snake_case     )
					state.print('''testing gather_object'''     )
					test_gather_object(__snake_case     )
					state.print('''testing broadcast'''     )
					test_broadcast(__snake_case     )
					state.print('''testing pad_across_processes'''     )
					test_pad_across_processes(__snake_case     )
					state.print('''testing reduce_sum'''     )
					test_reduce_sum(__snake_case     )
					state.print('''testing reduce_mean'''     )
					test_reduce_mean(__snake_case     )


if __name__ == "__main__":
				main()








"""simple docstring"""





import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


_a														=		logging.get_logger(__name__)

_a														=		{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'detr'
		lowercase__      =				['past_key_values']
		lowercase__      =				{
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		}



		def __init__( self    ,		__a=True    ,		__a=None    ,		__a=3    ,		__a=1_00    ,		__a=6    ,		__a=20_48    ,		__a=8    ,		__a=6    ,		__a=20_48    ,		__a=8    ,		__a=0.0    ,		__a=0.0    ,		__a=True    ,		__a="relu"    ,		__a=2_56    ,		__a=0.1    ,		__a=0.0    ,		__a=0.0    ,		__a=0.02    ,		__a=1.0    ,		__a=False    ,		__a="sine"    ,		__a="resnet50"    ,		__a=True    ,		__a=False    ,		__a=1    ,		__a=5    ,		__a=2    ,		__a=1    ,		__a=1    ,		__a=5    ,		__a=2    ,		__a=0.1    ,		**__a    ,		)	->	Tuple:






							'''simple docstring'''
							if backbone_config is not None and use_timm_backbone:
												raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

							if not use_timm_backbone:
												if backbone_config is None:
																	logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
																	_UpperCamelCase										=  CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
												elif isinstance(__a    ,		__a):
																	_UpperCamelCase										=  backbone_config.get('''model_type''')
																	_UpperCamelCase										=  CONFIG_MAPPING[backbone_model_type]
																	_UpperCamelCase										=  config_class.from_dict(__a)
												# set timm attributes to None
												_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  None, None, None

							_UpperCamelCase										=  use_timm_backbone
							_UpperCamelCase										=  backbone_config
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  num_queries
							_UpperCamelCase										=  d_model
							_UpperCamelCase										=  encoder_ffn_dim
							_UpperCamelCase										=  encoder_layers
							_UpperCamelCase										=  encoder_attention_heads
							_UpperCamelCase										=  decoder_ffn_dim
							_UpperCamelCase										=  decoder_layers
							_UpperCamelCase										=  decoder_attention_heads
							_UpperCamelCase										=  dropout
							_UpperCamelCase										=  attention_dropout
							_UpperCamelCase										=  activation_dropout
							_UpperCamelCase										=  activation_function
							_UpperCamelCase										=  init_std
							_UpperCamelCase										=  init_xavier_std
							_UpperCamelCase										=  encoder_layerdrop
							_UpperCamelCase										=  decoder_layerdrop
							_UpperCamelCase										=  encoder_layers
							_UpperCamelCase										=  auxiliary_loss
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  backbone
							_UpperCamelCase										=  use_pretrained_backbone
							_UpperCamelCase										=  dilation
							# Hungarian matcher
							_UpperCamelCase										=  class_cost
							_UpperCamelCase										=  bbox_cost
							_UpperCamelCase										=  giou_cost
							# Loss coefficients
							_UpperCamelCase										=  mask_loss_coefficient
							_UpperCamelCase										=  dice_loss_coefficient
							_UpperCamelCase										=  bbox_loss_coefficient
							_UpperCamelCase										=  giou_loss_coefficient
							_UpperCamelCase										=  eos_coefficient
							super().__init__(is_encoder_decoder=__a    ,		**__a)



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.encoder_attention_heads



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.d_model



		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		**__a)	->	Any:






							'''simple docstring'''
							return cls(backbone_config=__a    ,		**__a)




		def 						UpperCAmelCase							( self)	->	Dict[str, any]:






							'''simple docstring'''
							_UpperCamelCase										=  copy.deepcopy(self.__dict__)
							if output["backbone_config"] is not None:
												_UpperCamelCase										=  self.backbone_config.to_dict()
							_UpperCamelCase										=  self.__class__.model_type
							return output






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				version.parse('1.11'     )



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							        ('''pixel_mask''', {0: '''batch'''}),
							    ])



		@property
		def 						UpperCAmelCase							( self)	->	float:






							'''simple docstring'''
							return 1e-5




		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return 12






