






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 	snake_case__ (							_lowerCAmelCase				):
 lowercase__						:					Optional[int]											=   (IPNDMScheduler,)
 lowercase__						:					List[str]											=   (('''num_inference_steps''', 50),)
 def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Union[str, Any]:
       __magic_name__      :       List[Any]       						= {"""num_train_timesteps""": 10_00}
       config.update(**lowerCAmelCase__			)
       return config
 def       __magic_name__    (    self      ,       lowerCAmelCase__=0      ,       **lowerCAmelCase__			)							->				Optional[int]:
       __magic_name__      :       Optional[int]       						= dict(self.forward_default_kwargs			)
       __magic_name__      :       Optional[Any]       						= kwargs.pop("""num_inference_steps"""      ,       lowerCAmelCase__			)
       __magic_name__      :       List[str]       						= self.dummy_sample
       __magic_name__      :       List[Any]       						= 0.1 * sample
       __magic_name__      :       int       						= [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

       for scheduler_class in self.scheduler_classes:
             __magic_name__      :       Any       						= self.get_scheduler_config(**lowerCAmelCase__			)
             __magic_name__      :       Any       						= scheduler_class(**lowerCAmelCase__			)
             scheduler.set_timesteps(lowerCAmelCase__			)
             # copy over dummy past residuals
             __magic_name__      :       Optional[int]       						= dummy_past_residuals[:]

             if time_step is None:
                   __magic_name__      :       Optional[Any]       						= scheduler.timesteps[len(scheduler.timesteps			) // 2]

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

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

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

             __magic_name__      :       List[Any]       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample
             __magic_name__      :       List[Any]       						= new_scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

             assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"
 def       __magic_name__    (    self			)							->				List[Any]:
       pass
 def       __magic_name__    (    self      ,       lowerCAmelCase__=0      ,       **lowerCAmelCase__			)							->				Tuple:
       __magic_name__      :       str       						= dict(self.forward_default_kwargs			)
       __magic_name__      :       int       						= kwargs.pop("""num_inference_steps"""      ,       lowerCAmelCase__			)
       __magic_name__      :       Optional[Any]       						= self.dummy_sample
       __magic_name__      :       Dict       						= 0.1 * sample
       __magic_name__      :       Any       						= [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

       for scheduler_class in self.scheduler_classes:
             __magic_name__      :       List[Any]       						= self.get_scheduler_config()
             __magic_name__      :       Union[str, Any]       						= scheduler_class(**lowerCAmelCase__			)
             scheduler.set_timesteps(lowerCAmelCase__			)

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

             if time_step is None:
                   __magic_name__      :       Optional[Any]       						= scheduler.timesteps[len(scheduler.timesteps			) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowerCAmelCase__			)
                   __magic_name__      :       Union[str, Any]       						= scheduler_class.from_pretrained(lowerCAmelCase__			)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(lowerCAmelCase__			)

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

             __magic_name__      :       Optional[int]       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample
             __magic_name__      :       Union[str, Any]       						= new_scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

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

             __magic_name__      :       Tuple       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample
             __magic_name__      :       Tuple       						= new_scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

             assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"
 def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Tuple:
       __magic_name__      :       Optional[int]       						= self.scheduler_classes[0]
       __magic_name__      :       Union[str, Any]       						= self.get_scheduler_config(**lowerCAmelCase__			)
       __magic_name__      :       str       						= scheduler_class(**lowerCAmelCase__			)

       __magic_name__      :       str       						= 10
       __magic_name__      :       List[str]       						= self.dummy_model()
       __magic_name__      :       int       						= self.dummy_sample_deter
       scheduler.set_timesteps(lowerCAmelCase__			)

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

       for i, t in enumerate(scheduler.timesteps			):
             __magic_name__      :       int       						= model(lowerCAmelCase__      ,       lowerCAmelCase__			)
             __magic_name__      :       Tuple       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			).prev_sample

       return sample
 def       __magic_name__    (    self			)							->				Dict:
       __magic_name__      :       Tuple       						= dict(self.forward_default_kwargs			)

       __magic_name__      :       Tuple       						= kwargs.pop("""num_inference_steps"""      ,       lowerCAmelCase__			)

       for scheduler_class in self.scheduler_classes:
             __magic_name__      :       Optional[Any]       						= self.get_scheduler_config()
             __magic_name__      :       Dict       						= scheduler_class(**lowerCAmelCase__			)

             __magic_name__      :       int       						= self.dummy_sample
             __magic_name__      :       Dict       						= 0.1 * sample

             if num_inference_steps is not None and hasattr(lowerCAmelCase__      ,       """set_timesteps"""			):
                   scheduler.set_timesteps(lowerCAmelCase__			)
             elif num_inference_steps is not None and not hasattr(lowerCAmelCase__      ,       """set_timesteps"""			):
                   __magic_name__      :       List[str]       						= num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             __magic_name__      :       str       						= [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
             __magic_name__      :       List[str]       						= dummy_past_residuals[:]

             __magic_name__      :       int       						= scheduler.timesteps[5]
             __magic_name__      :       str       						= scheduler.timesteps[6]

             __magic_name__      :       str       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample
             __magic_name__      :       str       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

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

             __magic_name__      :       Any       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample
             __magic_name__      :       str       						= scheduler.step(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			).prev_sample

             self.assertEqual(output_a.shape      ,       sample.shape			)
             self.assertEqual(output_a.shape      ,       output_a.shape			)
 def       __magic_name__    (    self			)							->				Optional[Any]:
       for timesteps in [1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=lowerCAmelCase__      ,       time_step=lowerCAmelCase__			)
 def       __magic_name__    (    self			)							->				Optional[int]:
       for t, num_inference_steps in zip([1, 5, 10]      ,       [10, 50, 1_00]			):
             self.check_over_forward(num_inference_steps=lowerCAmelCase__      ,       time_step=lowerCAmelCase__			)







 def       __magic_name__    (    self			)							->				Union[str, Any]:
       __magic_name__      :       int       						= self.full_loop()
       __magic_name__      :       Optional[int]       						= torch.mean(torch.abs(lowerCAmelCase__			)			)

       assert abs(result_mean.item() - 2_54_05_29			) < 10








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__:					List[Any]							=     {
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[str]							=     ["ConditionalDetrFeatureExtractor"]
		__magic_name__:					int							=     ["ConditionalDetrImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[Any]							=     [
		    "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ConditionalDetrForObjectDetection",
		    "ConditionalDetrForSegmentation",
		    "ConditionalDetrModel",
		    "ConditionalDetrPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		__magic_name__:					List[str]							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
