


"""simple docstring"""







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   =(IPNDMScheduler,)
       a  :				int   =(("num_inference_steps", 50),)

       def        lowercase__							(						self		,     **snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							{"num_train_timesteps": 1_000}
         config.update(**snake_case__						)
         return config

       def        lowercase__							(						self		,     snake_case__=0		,     **snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase     :     List[str]								=							kwargs.pop("num_inference_steps"		,     snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							self.dummy_sample
         lowerCAmelCase     :     Dict								=							0.1 * sample
         lowerCAmelCase     :     int								=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

           lowerCAmelCase     :     Dict								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     List[str]								=							new_scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

           lowerCAmelCase     :     Union[str, Any]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     List[Any]								=							new_scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

       def        lowercase__							(						self						):






         """simple docstring"""





         pass

       def        lowercase__							(						self		,     snake_case__=0		,     **snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase     :     List[Any]								=							kwargs.pop("num_inference_steps"		,     snake_case__						)
         lowerCAmelCase     :     int								=							self.dummy_sample
         lowerCAmelCase     :     Optional[Any]								=							0.1 * sample
         lowerCAmelCase     :     List[str]								=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase     :     Any								=							self.get_scheduler_config()
           lowerCAmelCase     :     Tuple								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)

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

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

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(snake_case__						)
             lowerCAmelCase     :     Dict								=							scheduler_class.from_pretrained(snake_case__						)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(snake_case__						)

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

           lowerCAmelCase     :     Dict								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     str								=							new_scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

           lowerCAmelCase     :     List[str]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     Any								=							new_scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

       def        lowercase__							(						self		,     **snake_case__						):






         """simple docstring"""





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

         lowerCAmelCase     :     int								=							10
         lowerCAmelCase     :     List[Any]								=							self.dummy_model()
         lowerCAmelCase     :     Tuple								=							self.dummy_sample_deter
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase     :     List[Any]								=							model(snake_case__		,     snake_case__						)
           lowerCAmelCase     :     Union[str, Any]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__						).prev_sample

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase     :     Tuple								=							model(snake_case__		,     snake_case__						)
           lowerCAmelCase     :     List[Any]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__						).prev_sample

         return sample

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							dict(self.forward_default_kwargs						)

         lowerCAmelCase     :     str								=							kwargs.pop("num_inference_steps"		,     snake_case__						)

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

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

           if num_inference_steps is not None and hasattr(snake_case__		,     "set_timesteps"						):
             scheduler.set_timesteps(snake_case__						)
           elif num_inference_steps is not None and not hasattr(snake_case__		,     "set_timesteps"						):
             lowerCAmelCase     :     List[Any]								=							num_inference_steps

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

           lowerCAmelCase     :     Union[str, Any]								=							scheduler.timesteps[5]
           lowerCAmelCase     :     List[Any]								=							scheduler.timesteps[6]

           lowerCAmelCase     :     Optional[Any]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     Any								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

           lowerCAmelCase     :     Union[str, Any]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     int								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

       def        lowercase__							(						self						):






         """simple docstring"""





         for timesteps in [100, 1_000]:
           self.check_over_configs(num_train_timesteps=snake_case__		,     time_step=snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





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







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							self.full_loop()
         lowerCAmelCase     :     Dict								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 2_540_529						) < 10










from typing import TYPE_CHECKING

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


_lowercase: Union[str, Any]      			=		{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase: Dict      			=		["BridgeTowerImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase: int      			=		[
				    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "BridgeTowerForContrastiveLearning",
				    "BridgeTowerForImageAndTextRetrieval",
				    "BridgeTowerForMaskedLM",
				    "BridgeTowerModel",
				    "BridgeTowerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_bridgetower import (
				    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BridgeTowerConfig,
				    BridgeTowerTextConfig,
				    BridgeTowerVisionConfig,
				)
				from .processing_bridgetower import BridgeTowerProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_bridgetower import BridgeTowerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bridgetower import (
								    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BridgeTowerForContrastiveLearning,
								    BridgeTowerForImageAndTextRetrieval,
								    BridgeTowerForMaskedLM,
								    BridgeTowerModel,
								    BridgeTowerPreTrainedModel,
								)


else:
				import sys

				_lowercase: Optional[Any]      			=		_LazyModule(__name__, globals()["__file__"], _import_structure)
