






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   (IPNDMScheduler,)
		__snake_case      :       Any     =   (("num_inference_steps", 5_0),)







		def  A__      (					self       ,**A__		):
						_A	:				Union[str, Any]   =      {"""num_train_timesteps""": 1000}
						config.update(**A_		)
						return config







		def  A__      (					self       ,A__=0       ,**A__		):
						_A	:				List[Any]   =      dict(self.forward_default_kwargs		)
						_A	:				str   =      kwargs.pop('''num_inference_steps'''       ,A_		)
						_A	:				Union[str, Any]   =      self.dummy_sample
						_A	:				List[Any]   =      0.1 * sample
						_A	:				int   =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

						for scheduler_class in self.scheduler_classes:
										_A	:				str   =      self.get_scheduler_config(**A_		)
										_A	:				List[str]   =      scheduler_class(**A_		)
										scheduler.set_timesteps(A_		)
										# copy over dummy past residuals
										_A	:				Dict   =      dummy_past_residuals[:]

										if time_step is None:
														_A	:				Union[str, Any]   =      scheduler.timesteps[len(scheduler.timesteps		) // 2]

										with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(A_		)
														_A	:				Any   =      scheduler_class.from_pretrained(A_		)
														new_scheduler.set_timesteps(A_		)
														# copy over dummy past residuals
														_A	:				int   =      dummy_past_residuals[:]

										_A	:				Optional[Any]   =      scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample
										_A	:				str   =      new_scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample

										assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

										_A	:				Optional[int]   =      scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample
										_A	:				Any   =      new_scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample

										assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







		def  A__      (					self		):
						pass







		def  A__      (					self       ,A__=0       ,**A__		):
						_A	:				int   =      dict(self.forward_default_kwargs		)
						_A	:				str   =      kwargs.pop('''num_inference_steps'''       ,A_		)
						_A	:				Any   =      self.dummy_sample
						_A	:				List[str]   =      0.1 * sample
						_A	:				Optional[Any]   =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

						for scheduler_class in self.scheduler_classes:
										_A	:				Optional[int]   =      self.get_scheduler_config()
										_A	:				List[Any]   =      scheduler_class(**A_		)
										scheduler.set_timesteps(A_		)

										# copy over dummy past residuals (must be after setting timesteps)
										_A	:				Dict   =      dummy_past_residuals[:]

										if time_step is None:
														_A	:				str   =      scheduler.timesteps[len(scheduler.timesteps		) // 2]

										with tempfile.TemporaryDirectory() as tmpdirname:
														scheduler.save_config(A_		)
														_A	:				Tuple   =      scheduler_class.from_pretrained(A_		)
														# copy over dummy past residuals
														new_scheduler.set_timesteps(A_		)

														# copy over dummy past residual (must be after setting timesteps)
														_A	:				List[Any]   =      dummy_past_residuals[:]

										_A	:				Optional[Any]   =      scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample
										_A	:				Dict   =      new_scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample

										assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

										_A	:				Tuple   =      scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample
										_A	:				Optional[int]   =      new_scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample

										assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"







		def  A__      (					self       ,**A__		):
						_A	:				Tuple   =      self.scheduler_classes[0]
						_A	:				Tuple   =      self.get_scheduler_config(**A_		)
						_A	:				str   =      scheduler_class(**A_		)

						_A	:				Optional[Any]   =      10
						_A	:				int   =      self.dummy_model()
						_A	:				List[str]   =      self.dummy_sample_deter
						scheduler.set_timesteps(A_		)

						for i, t in enumerate(scheduler.timesteps		):
										_A	:				Union[str, Any]   =      model(A_       ,A_		)
										_A	:				Tuple   =      scheduler.step(A_       ,A_       ,A_		).prev_sample

						for i, t in enumerate(scheduler.timesteps		):
										_A	:				Union[str, Any]   =      model(A_       ,A_		)
										_A	:				Optional[int]   =      scheduler.step(A_       ,A_       ,A_		).prev_sample

						return sample







		def  A__      (					self		):
						_A	:				Dict   =      dict(self.forward_default_kwargs		)

						_A	:				Dict   =      kwargs.pop('''num_inference_steps'''       ,A_		)

						for scheduler_class in self.scheduler_classes:
										_A	:				Optional[int]   =      self.get_scheduler_config()
										_A	:				List[str]   =      scheduler_class(**A_		)

										_A	:				List[str]   =      self.dummy_sample
										_A	:				str   =      0.1 * sample

										if num_inference_steps is not None and hasattr(A_       ,'''set_timesteps'''		):
														scheduler.set_timesteps(A_		)
										elif num_inference_steps is not None and not hasattr(A_       ,'''set_timesteps'''		):
														_A	:				List[Any]   =      num_inference_steps

										# copy over dummy past residuals (must be done after set_timesteps)
										_A	:				Dict   =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
										_A	:				List[str]   =      dummy_past_residuals[:]

										_A	:				List[Any]   =      scheduler.timesteps[5]
										_A	:				int   =      scheduler.timesteps[6]

										_A	:				List[Any]   =      scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample
										_A	:				Tuple   =      scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample

										self.assertEqual(output_a.shape       ,sample.shape		)
										self.assertEqual(output_a.shape       ,output_a.shape		)

										_A	:				Any   =      scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample
										_A	:				str   =      scheduler.step(A_       ,A_       ,A_       ,**A_		).prev_sample

										self.assertEqual(output_a.shape       ,sample.shape		)
										self.assertEqual(output_a.shape       ,output_a.shape		)







		def  A__      (					self		):
						for timesteps in [100, 1000]:
										self.check_over_configs(num_train_timesteps=A_       ,time_step=A_		)







		def  A__      (					self		):
						for t, num_inference_steps in zip([1, 5, 10]       ,[10, 50, 100]		):
										self.check_over_forward(num_inference_steps=A_       ,time_step=A_		)







		def  A__      (					self		):
						_A	:				List[str]   =      self.full_loop()
						_A	:				int   =      torch.mean(torch.abs(A_		)		)

						assert abs(result_mean.item() - 2540529		) < 10







import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''conditional_detr'''
     __SCREAMING_SNAKE_CASE     					=    ['''past_key_values''']
     __SCREAMING_SNAKE_CASE     					=    {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }


     def __init__(  self							,					A_=True							,					A_=None							,					A_=3							,					A_=3_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_=0.0_2							,					A_=1.0							,					A_=False							,					A_="sine"							,					A_="resnet50"							,					A_=True							,					A_=False							,					A_=2							,					A_=5							,					A_=2							,					A_=1							,					A_=1							,					A_=2							,					A_=5							,					A_=2							,					A_=0.2_5							,					**A_							,					):
       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		:							int							=     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]    )
         elif isinstance(A_							,					A_    ):
           _UpperCAmelCase		:							Tuple							=     backbone_config.get("""model_type"""    )
           _UpperCAmelCase		:							Union[str, Any]							=     CONFIG_MAPPING[backbone_model_type]
           _UpperCAmelCase		:							Optional[Any]							=     config_class.from_dict(A_    )

       _UpperCAmelCase		:							Tuple							=     use_timm_backbone
       _UpperCAmelCase		:							List[str]							=     backbone_config
       _UpperCAmelCase		:							Any							=     num_channels
       _UpperCAmelCase		:							Tuple							=     num_queries
       _UpperCAmelCase		:							Dict							=     d_model
       _UpperCAmelCase		:							Dict							=     encoder_ffn_dim
       _UpperCAmelCase		:							Tuple							=     encoder_layers
       _UpperCAmelCase		:							List[Any]							=     encoder_attention_heads
       _UpperCAmelCase		:							Union[str, Any]							=     decoder_ffn_dim
       _UpperCAmelCase		:							Optional[int]							=     decoder_layers
       _UpperCAmelCase		:							List[str]							=     decoder_attention_heads
       _UpperCAmelCase		:							Optional[Any]							=     dropout
       _UpperCAmelCase		:							str							=     attention_dropout
       _UpperCAmelCase		:							Union[str, Any]							=     activation_dropout
       _UpperCAmelCase		:							Union[str, Any]							=     activation_function
       _UpperCAmelCase		:							int							=     init_std
       _UpperCAmelCase		:							Union[str, Any]							=     init_xavier_std
       _UpperCAmelCase		:							Tuple							=     encoder_layerdrop
       _UpperCAmelCase		:							Tuple							=     decoder_layerdrop
       _UpperCAmelCase		:							Tuple							=     encoder_layers
       _UpperCAmelCase		:							Any							=     auxiliary_loss
       _UpperCAmelCase		:							List[str]							=     position_embedding_type
       _UpperCAmelCase		:							int							=     backbone
       _UpperCAmelCase		:							Any							=     use_pretrained_backbone
       _UpperCAmelCase		:							int							=     dilation
       # Hungarian matcher
       _UpperCAmelCase		:							Dict							=     class_cost
       _UpperCAmelCase		:							Optional[Any]							=     bbox_cost
       _UpperCAmelCase		:							Tuple							=     giou_cost
       # Loss coefficients
       _UpperCAmelCase		:							Union[str, Any]							=     mask_loss_coefficient
       _UpperCAmelCase		:							int							=     dice_loss_coefficient
       _UpperCAmelCase		:							List[Any]							=     cls_loss_coefficient
       _UpperCAmelCase		:							List[Any]							=     bbox_loss_coefficient
       _UpperCAmelCase		:							int							=     giou_loss_coefficient
       _UpperCAmelCase		:							Optional[int]							=     focal_alpha
       super().__init__(is_encoder_decoder=A_							,					**A_    )


     @property
     def        __snake_case(  self    ):
       return self.encoder_attention_heads


     @property
     def        __snake_case(  self    ):
       return self.d_model




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     copy.deepcopy(self.__dict__    )
       if self.backbone_config is not None:
         _UpperCAmelCase		:							Optional[int]							=     self.backbone_config.to_dict()
       _UpperCAmelCase		:							Dict							=     self.__class__.model_type
       return output


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    version.parse('''1.11'''     )


     @property
     def        __snake_case(  self    ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]    )


     @property
     def        __snake_case(  self    ):
       return 1e-5




     @property
     def        __snake_case(  self    ):
       return 12





