
import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      (EulerDiscreteScheduler,)
							UpperCAmelCase__						     =      10

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {
									    'num_train_timesteps': 11_00,
									    'beta_start': 0.00_01,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									}

									config.update(**SCREAMING_SNAKE_CASE_    )
									return config

							def _lowercase					(self    ):

									"""simple docstring"""







									for timesteps in [10, 50, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]       ,       [0.00_02, 0.0_02, 0.02]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,       beta_end=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									scheduler.set_timesteps(self.num_inference_steps    )

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE_						 = sample.to(SCREAMING_SNAKE_CASE_    )

									for i, t in enumerate(scheduler.timesteps    ):
											SCREAMING_SNAKE_CASE_						 = scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = output.prev_sample

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 10.08_07    ) < 1e-2
									assert abs(result_mean.item() - 0.01_31    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(prediction_type='''v_prediction'''    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									scheduler.set_timesteps(self.num_inference_steps    )

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE_						 = sample.to(SCREAMING_SNAKE_CASE_    )

									for i, t in enumerate(scheduler.timesteps    ):
											SCREAMING_SNAKE_CASE_						 = scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = output.prev_sample

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 0.00_02    ) < 1e-2
									assert abs(result_mean.item() - 2.2_6_7_6e-0_6    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									scheduler.set_timesteps(self.num_inference_steps       ,       device=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									SCREAMING_SNAKE_CASE_						 = sample.to(SCREAMING_SNAKE_CASE_    )

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE_						 = scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = output.prev_sample

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 10.08_07    ) < 1e-2
									assert abs(result_mean.item() - 0.01_31    ) < 1e-3







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_       ,       use_karras_sigmas=SCREAMING_SNAKE_CASE_    )

									scheduler.set_timesteps(self.num_inference_steps       ,       device=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									SCREAMING_SNAKE_CASE_						 = sample.to(SCREAMING_SNAKE_CASE_    )

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE_						 = scheduler.scale_model_input(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = output.prev_sample

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19    ) < 1e-2
									assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63    ) < 1e-3

"""simple docstring"""




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


lowerCAmelCase__							=				{
    '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
}

lowerCAmelCase__							=				logging.get_logger(__name__)





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''maskformer'''
							UpperCAmelCase__						     =      {'''hidden_size''': '''mask_feature_size'''}
							UpperCAmelCase__						     =      ['''resnet''', '''swin''']
							UpperCAmelCase__						     =      ['''detr''']

							def __init__(self       ,       SCREAMING_SNAKE_CASE_ = 2_56       ,       SCREAMING_SNAKE_CASE_ = 2_56       ,       SCREAMING_SNAKE_CASE_ = 0.1       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = 0.02       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 20.0       ,       SCREAMING_SNAKE_CASE_ = None       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									if backbone_config is None:
											# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
											SCREAMING_SNAKE_CASE_						 = 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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = backbone_config.pop('''model_type'''    )
											SCREAMING_SNAKE_CASE_						 = CONFIG_MAPPING[backbone_model_type]
											SCREAMING_SNAKE_CASE_						 = config_class.from_dict(SCREAMING_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
											SCREAMING_SNAKE_CASE_						 = DetrConfig()
									else:
											# verify that the decoder is supported
											SCREAMING_SNAKE_CASE_						 = (
											    decoder_config.pop('''model_type'''    ) if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
													SCREAMING_SNAKE_CASE_						 = CONFIG_MAPPING[decoder_type]
													SCREAMING_SNAKE_CASE_						 = config_class.from_dict(SCREAMING_SNAKE_CASE_    )

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

									SCREAMING_SNAKE_CASE_						 = self.decoder_config.encoder_attention_heads
									SCREAMING_SNAKE_CASE_						 = self.decoder_config.num_hidden_layers
									super().__init__(**SCREAMING_SNAKE_CASE_    )

							@classmethod
							def _lowercase					(cls       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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







							def _lowercase					(self    ):

									"""simple docstring"""







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