





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


A   : int       =				{
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

A   : Union[str, Any]       =				logging.get_logger(__name__)


class   A      (A_   ):
			'''simple docstring'''




			__lowerCamelCase     :	List[str]          =   "maskformer"
			__lowerCamelCase     :	Any          =   {"hidden_size": "mask_feature_size"}
			__lowerCamelCase     :	List[str]          =   ["resnet", "swin"]
			__lowerCamelCase     :	Any          =   ["detr"]






			def __init__(      self       :   Dict							,     __lowerCAmelCase       :   int = 2_56							,     __lowerCAmelCase       :   int = 2_56							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   bool = False							,     __lowerCAmelCase       :   Optional[Dict] = None							,     __lowerCAmelCase       :   Optional[Dict] = None							,     __lowerCAmelCase       :   float = 0.0_2							,     __lowerCAmelCase       :   float = 1.0							,     __lowerCAmelCase       :   float = 1.0							,     __lowerCAmelCase       :   float = 1.0							,     __lowerCAmelCase       :   float = 2_0.0							,     __lowerCAmelCase       :   Optional[bool] = None							,     **__lowerCAmelCase       :   Optional[Any]							,     )     ->     Optional[int]:







					"""simple docstring"""






					if backbone_config is None:
							# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
							A__  				=       SwinConfig(
							    image_size=3_84							,     in_channels=3							,     patch_size=4							,     embed_dim=1_28							,     depths=[2, 2, 18, 2]							,     num_heads=[4, 8, 16, 32]							,     window_size=12							,     drop_path_rate=0.3							,     out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]							,     )

					if isinstance(snake_case__							,     snake_case__       ):
							A__  				=       backbone_config.pop("""model_type"""       )
							A__  				=       CONFIG_MAPPING[backbone_model_type]
							A__  				=       config_class.from_dict(snake_case__       )

					# verify that the backbone is supported
					if backbone_config.model_type not in self.backbones_supported:
							logger.warning_once(
							    f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
							    f'Supported model types: {",".join(self.backbones_supported       )}'       )

					if decoder_config is None:
							# fall back to https://huggingface.co/facebook/detr-resnet-50
							A__  				=       DetrConfig()
					else:
							# verify that the decoder is supported
							A__  				=       (
							    decoder_config.pop("""model_type"""       ) if isinstance(snake_case__							,     snake_case__       ) else decoder_config.model_type
							)
							if decoder_type not in self.decoders_supported:
									raise ValueError(
									    f'Transformer Decoder {decoder_type} not supported, please use one of'
									    f' {",".join(self.decoders_supported       )}'       )
							if isinstance(snake_case__							,     snake_case__       ):
									A__  				=       CONFIG_MAPPING[decoder_type]
									A__  				=       config_class.from_dict(snake_case__       )

					A__  				=       backbone_config
					A__  				=       decoder_config
					# main feature dimension for the model
					A__  				=       fpn_feature_size
					A__  				=       mask_feature_size
					# initializer
					A__  				=       init_std
					A__  				=       init_xavier_std
					# Hungarian matcher && loss
					A__  				=       cross_entropy_weight
					A__  				=       dice_weight
					A__  				=       mask_weight
					A__  				=       use_auxiliary_loss
					A__  				=       no_object_weight
					A__  				=       output_auxiliary_logits

					A__  				=       self.decoder_config.encoder_attention_heads
					A__  				=       self.decoder_config.num_hidden_layers
					super().__init__(**snake_case__       )






			@classmethod
			def        a_	(      cls       :   Optional[Any]							,     __lowerCAmelCase       :   PretrainedConfig							,     __lowerCAmelCase       :   PretrainedConfig							,     **__lowerCAmelCase       :   Tuple       )     ->     Tuple:







					"""simple docstring"""






					return cls(
					    backbone_config=snake_case__							,     decoder_config=snake_case__							,     **snake_case__							,     )







			def        a_	(      self       :   List[Any]       )     ->     Dict[str, any]:







					"""simple docstring"""






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










# 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 (							__a       :List[str]   )		->    Tuple:






		"""simple docstring"""

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







def        __lowerCamelCase (							__a       :int   )		->    Optional[int]:






		"""simple docstring"""

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







def        __lowerCamelCase (							__a       :Any   )		->    Any:






		"""simple docstring"""

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







def        __lowerCamelCase (							__a       :List[str]   )		->    List[str]:






		"""simple docstring"""

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







def        __lowerCamelCase (							__a       :Any   )		->    Any:






		"""simple docstring"""

		if state.is_main_process:
				A__  				=       torch.arange(state.num_processes + 1   ).to(state.device   )
		else:
				A__  				=       torch.arange(state.num_processes   ).to(state.device   )
		A__  				=       pad_across_processes(__a   )
		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 (							__a       :Union[str, Any]   )		->    str:






		"""simple docstring"""

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







def        __lowerCamelCase (							__a       :List[Any]   )		->    List[str]:






		"""simple docstring"""

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







def        __lowerCamelCase (							__a       :str   )		->    Optional[int]:






		"""simple docstring"""

		main()







def        __lowerCamelCase (							)		->    str:






		"""simple docstring"""

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


if __name__ == "__main__":
					main()



