



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class       a							(			lowercase__				):
		__lowerCAmelCase	:    Dict											=					(DDPMScheduler,)



		def 	__lowerCamelCase			(     self     :Tuple   ,**__lowercase     :Union[str, Any]  ):
						snake_case__	:      str     		=	{
						    'num_train_timesteps': 1_0_0_0,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'variance_type': 'fixed_small',
						    'clip_sample': True,
						}

						config.update(**SCREAMING_SNAKE_CASE__  )
						return config



		def 	__lowerCamelCase			(     self     :int  ):
						for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
										self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__  )



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,[0.002, 0.02, 0.2, 2]  ):
										self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__   ,beta_end=SCREAMING_SNAKE_CASE__  )



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__  )



		def 	__lowerCamelCase			(     self     :Dict  ):
						for variance in ["fixed_small", "fixed_large", "other"]:
										self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE__  )



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__  )
						for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
														self.check_over_configs(
														    thresholding=SCREAMING_SNAKE_CASE__   ,prediction_type=SCREAMING_SNAKE_CASE__   ,sample_max_value=SCREAMING_SNAKE_CASE__   ,)



		def 	__lowerCamelCase			(     self     :int  ):
						for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__  )



		def 	__lowerCamelCase			(     self     :Dict  ):
						for t in [0, 5_0_0, 9_9_9]:
										self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  )



		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      Optional[Any]     		=	self.scheduler_classes[0]
						snake_case__	:      Tuple     		=	self.get_scheduler_config()
						snake_case__	:      Any     		=	scheduler_class(**SCREAMING_SNAKE_CASE__  )

						assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_8_7  ) - 0.0_0979  )  ) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ) - 0.02  )  ) < 1e-5



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      Optional[int]     		=	self.scheduler_classes[0]
						snake_case__	:      str     		=	self.get_scheduler_config()
						snake_case__	:      Tuple     		=	scheduler_class(**SCREAMING_SNAKE_CASE__  )

						snake_case__	:      List[str]     		=	len(SCREAMING_SNAKE_CASE__  )

						snake_case__	:      List[str]     		=	self.dummy_model()
						snake_case__	:      List[str]     		=	self.dummy_sample_deter
						snake_case__	:      str     		=	torch.manual_seed(0  )

						for t in reversed(range(SCREAMING_SNAKE_CASE__  )  ):
										# 1. predict noise residual
										snake_case__	:      Optional[int]     		=	model(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__  )

										# 2. predict previous mean of sample x_t-1
										snake_case__	:      int     		=	scheduler.step(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,generator=SCREAMING_SNAKE_CASE__  ).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										snake_case__	:      List[Any]     		=	pred_prev_sample

						snake_case__	:      List[Any]     		=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
						snake_case__	:      Optional[Any]     		=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

						assert abs(result_sum.item() - 258.9606  ) < 1e-2
						assert abs(result_mean.item() - 0.3372  ) < 1e-3



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      List[str]     		=	self.scheduler_classes[0]
						snake_case__	:      Dict     		=	self.get_scheduler_config(prediction_type='''v_prediction'''  )
						snake_case__	:      Union[str, Any]     		=	scheduler_class(**SCREAMING_SNAKE_CASE__  )

						snake_case__	:      str     		=	len(SCREAMING_SNAKE_CASE__  )

						snake_case__	:      Optional[Any]     		=	self.dummy_model()
						snake_case__	:      Any     		=	self.dummy_sample_deter
						snake_case__	:      Any     		=	torch.manual_seed(0  )

						for t in reversed(range(SCREAMING_SNAKE_CASE__  )  ):
										# 1. predict noise residual
										snake_case__	:      Optional[Any]     		=	model(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__  )

										# 2. predict previous mean of sample x_t-1
										snake_case__	:      Union[str, Any]     		=	scheduler.step(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,generator=SCREAMING_SNAKE_CASE__  ).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										snake_case__	:      Optional[Any]     		=	pred_prev_sample

						snake_case__	:      List[str]     		=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
						snake_case__	:      Dict     		=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

						assert abs(result_sum.item() - 202.0296  ) < 1e-2
						assert abs(result_mean.item() - 0.2631  ) < 1e-3



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Union[str, Any]     		=	self.scheduler_classes[0]
						snake_case__	:      Optional[Any]     		=	self.get_scheduler_config()
						snake_case__	:      List[str]     		=	scheduler_class(**SCREAMING_SNAKE_CASE__  )

						snake_case__	:      Any     		=	[1_0_0, 8_7, 5_0, 1, 0]

						scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__  )

						snake_case__	:      Any     		=	scheduler.timesteps

						for i, timestep in enumerate(SCREAMING_SNAKE_CASE__  ):
										if i == len(SCREAMING_SNAKE_CASE__  ) - 1:
														snake_case__	:      Union[str, Any]     		=	-1
										else:
														snake_case__	:      Union[str, Any]     		=	timesteps[i + 1]

										snake_case__	:      str     		=	scheduler.previous_timestep(SCREAMING_SNAKE_CASE__  )
										snake_case__	:      Any     		=	prev_t.item()

										self.assertEqual(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      List[Any]     		=	self.scheduler_classes[0]
						snake_case__	:      Optional[int]     		=	self.get_scheduler_config()
						snake_case__	:      Any     		=	scheduler_class(**SCREAMING_SNAKE_CASE__  )

						snake_case__	:      Optional[int]     		=	[1_0_0, 8_7, 5_0, 5_1, 0]

						with self.assertRaises(SCREAMING_SNAKE_CASE__   ,msg='''`custom_timesteps` must be in descending order.'''  ):
										scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      List[Any]     		=	self.scheduler_classes[0]
						snake_case__	:      int     		=	self.get_scheduler_config()
						snake_case__	:      int     		=	scheduler_class(**SCREAMING_SNAKE_CASE__  )

						snake_case__	:      Optional[int]     		=	[1_0_0, 8_7, 5_0, 1, 0]
						snake_case__	:      Optional[Any]     		=	len(SCREAMING_SNAKE_CASE__  )

						with self.assertRaises(SCREAMING_SNAKE_CASE__   ,msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''  ):
										scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE__   ,timesteps=SCREAMING_SNAKE_CASE__  )






		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      List[str]     		=	self.scheduler_classes[0]
						snake_case__	:      List[Any]     		=	self.get_scheduler_config()
						snake_case__	:      List[str]     		=	scheduler_class(**SCREAMING_SNAKE_CASE__  )

						snake_case__	:      Optional[Any]     		=	[scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    SCREAMING_SNAKE_CASE__   ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''   ,):
										scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__  )





import copy
from typing import Dict, List, Optional

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


UpperCAmelCase_      : Optional[int]  					=       {
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

UpperCAmelCase_      : List[str]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Any        =						'''mask2former'''
      snake_case__				:     Any        =						['''swin''']
      snake_case__				:     str        =						{'''hidden_size''': '''hidden_dim'''}
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict] = None     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_2_4     ,							SCREAMING_SNAKE_CASE__				:       str = "relu"     ,							SCREAMING_SNAKE_CASE__				:       int = 6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0     ,							SCREAMING_SNAKE_CASE__				:       int = 8     ,							SCREAMING_SNAKE_CASE__				:       float = 0.0     ,							SCREAMING_SNAKE_CASE__				:       int = 2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       int = 4     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_5     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.1     ,							SCREAMING_SNAKE_CASE__				:       float = 2.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       int = 1_2_5_4_4     ,							SCREAMING_SNAKE_CASE__				:       float = 3.0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.75     ,							SCREAMING_SNAKE_CASE__				:       float = 0.02     ,							SCREAMING_SNAKE_CASE__				:       float = 1.0     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       List[int] = [4, 8, 1_6, 3_2]     ,							SCREAMING_SNAKE_CASE__				:       bool = None     ,							**SCREAMING_SNAKE_CASE__				:       int     ,							)		->			List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'     )
                    a_					:		Dict							=  CONFIG_MAPPING['swin'](
                        image_size=2_2_4     ,							in_channels=3     ,							patch_size=4     ,							embed_dim=9_6     ,							depths=[2, 2, 1_8, 2]     ,							num_heads=[3, 6, 1_2, 2_4]     ,							window_size=7     ,							drop_path_rate=0.3     ,							use_absolute_embeddings=SCREAMING_SNAKE_CASE__     ,							out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,							)

             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		Any							=  backbone_config.pop('model_type'     )
                    a_					:		Optional[Any]							=  CONFIG_MAPPING[backbone_model_type]
                    a_					:		List[str]							=  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 Mask2Former. """
                        F"""Supported model types: {",".join(self.backbones_supported     )}"""     )

             a_					:		Dict							=  backbone_config
             a_					:		List[str]							=  feature_size
             a_					:		List[str]							=  mask_feature_size
             a_					:		int							=  hidden_dim
             a_					:		Dict							=  encoder_feedforward_dim
             a_					:		str							=  activation_function
             a_					:		List[str]							=  encoder_layers
             a_					:		List[str]							=  decoder_layers
             a_					:		Dict							=  num_attention_heads
             a_					:		str							=  dropout
             a_					:		Tuple							=  dim_feedforward
             a_					:		List[str]							=  pre_norm
             a_					:		Optional[int]							=  enforce_input_projection
             a_					:		Any							=  common_stride
             a_					:		Optional[int]							=  ignore_value
             a_					:		int							=  num_queries
             a_					:		Tuple							=  no_object_weight
             a_					:		Dict							=  class_weight
             a_					:		Optional[int]							=  mask_weight
             a_					:		Optional[int]							=  dice_weight
             a_					:		str							=  train_num_points
             a_					:		List[str]							=  oversample_ratio
             a_					:		List[Any]							=  importance_sample_ratio
             a_					:		Any							=  init_std
             a_					:		Union[str, Any]							=  init_xavier_std
             a_					:		Union[str, Any]							=  use_auxiliary_loss
             a_					:		Dict							=  feature_strides
             a_					:		List[str]							=  output_auxiliary_logits
             a_					:		Dict							=  decoder_layers

             super().__init__(**SCREAMING_SNAKE_CASE__     )
      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       str     ,							SCREAMING_SNAKE_CASE__				:       PretrainedConfig     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			List[Any]:
             return cls(
                 backbone_config=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)

      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict[str, any]:
             a_					:		Optional[int]							=  copy.deepcopy(self.__dict__     )
             a_					:		List[Any]							=  self.backbone_config.to_dict()
             a_					:		Optional[Any]							=  self.__class__.model_type
             return output


