


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 				_UpperCamelCase			(    _a    ):

   '''simple docstring'''







   a_					:				Dict     =			(IPNDMScheduler,)
   a_					:				str     =			(('''num_inference_steps''', 50),)

   def 	_snake_case     (	self	:			List[str]							,	**_lowerCamelCase	:			str				):






         '''simple docstring'''



         __lowerCamelCase  :   List[str]							    =    {"""num_train_timesteps""": 1_0_0_0}
         config.update(**__UpperCamelCase				)
         return config

   def 	_snake_case     (	self	:			int							,	_lowerCamelCase	:			int=0							,	**_lowerCamelCase	:			Optional[Any]				):






         '''simple docstring'''



         __lowerCamelCase  :   Optional[Any]							    =    dict(self.forward_default_kwargs				)
         __lowerCamelCase  :   Optional[int]							    =    kwargs.pop("""num_inference_steps"""							,	__UpperCamelCase				)
         __lowerCamelCase  :   List[Any]							    =    self.dummy_sample
         __lowerCamelCase  :   Union[str, 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  :   Union[str, Any]							    =    self.get_scheduler_config(**__UpperCamelCase				)
               __lowerCamelCase  :   Union[str, Any]							    =    scheduler_class(**__UpperCamelCase				)
               scheduler.set_timesteps(__UpperCamelCase				)
               # copy over dummy past residuals
               __lowerCamelCase  :   int							    =    dummy_past_residuals[:]

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

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

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

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

               __lowerCamelCase  :   Tuple							    =    scheduler.step(__UpperCamelCase							,	__UpperCamelCase							,	__UpperCamelCase							,	**__UpperCamelCase				).prev_sample
               __lowerCamelCase  :   int							    =    new_scheduler.step(__UpperCamelCase							,	__UpperCamelCase							,	__UpperCamelCase							,	**__UpperCamelCase				).prev_sample

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

   def 	_snake_case     (	self	:			Optional[int]				):






         '''simple docstring'''



         pass

   def 	_snake_case     (	self	:			Union[str, Any]							,	_lowerCamelCase	:			List[Any]=0							,	**_lowerCamelCase	:			Optional[int]				):






         '''simple docstring'''



         __lowerCamelCase  :   Optional[int]							    =    dict(self.forward_default_kwargs				)
         __lowerCamelCase  :   Any							    =    kwargs.pop("""num_inference_steps"""							,	__UpperCamelCase				)
         __lowerCamelCase  :   Any							    =    self.dummy_sample
         __lowerCamelCase  :   Any							    =    0.1 * sample
         __lowerCamelCase  :   Tuple							    =    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
               __lowerCamelCase  :   Dict							    =    self.get_scheduler_config()
               __lowerCamelCase  :   List[Any]							    =    scheduler_class(**__UpperCamelCase				)
               scheduler.set_timesteps(__UpperCamelCase				)

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

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

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

                     # copy over dummy past residual (must be after setting timesteps)
                     __lowerCamelCase  :   Union[str, Any]							    =    dummy_past_residuals[:]

               __lowerCamelCase  :   Tuple							    =    scheduler.step(__UpperCamelCase							,	__UpperCamelCase							,	__UpperCamelCase							,	**__UpperCamelCase				).prev_sample
               __lowerCamelCase  :   Optional[Any]							    =    new_scheduler.step(__UpperCamelCase							,	__UpperCamelCase							,	__UpperCamelCase							,	**__UpperCamelCase				).prev_sample

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

               __lowerCamelCase  :   int							    =    scheduler.step(__UpperCamelCase							,	__UpperCamelCase							,	__UpperCamelCase							,	**__UpperCamelCase				).prev_sample
               __lowerCamelCase  :   List[Any]							    =    new_scheduler.step(__UpperCamelCase							,	__UpperCamelCase							,	__UpperCamelCase							,	**__UpperCamelCase				).prev_sample

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

   def 	_snake_case     (	self	:			Tuple							,	**_lowerCamelCase	:			Dict				):






         '''simple docstring'''



         __lowerCamelCase  :   List[Any]							    =    self.scheduler_classes[0]
         __lowerCamelCase  :   Any							    =    self.get_scheduler_config(**__UpperCamelCase				)
         __lowerCamelCase  :   List[Any]							    =    scheduler_class(**__UpperCamelCase				)

         __lowerCamelCase  :   Any							    =    1_0
         __lowerCamelCase  :   Dict							    =    self.dummy_model()
         __lowerCamelCase  :   Any							    =    self.dummy_sample_deter
         scheduler.set_timesteps(__UpperCamelCase				)

         for i, t in enumerate(scheduler.timesteps				):
               __lowerCamelCase  :   str							    =    model(__UpperCamelCase							,	__UpperCamelCase				)
               __lowerCamelCase  :   int							    =    scheduler.step(__UpperCamelCase							,	__UpperCamelCase							,	__UpperCamelCase				).prev_sample

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

         return sample

   def 	_snake_case     (	self	:			Dict				):






         '''simple docstring'''



         __lowerCamelCase  :   str							    =    dict(self.forward_default_kwargs				)

         __lowerCamelCase  :   Any							    =    kwargs.pop("""num_inference_steps"""							,	__UpperCamelCase				)

         for scheduler_class in self.scheduler_classes:
               __lowerCamelCase  :   Union[str, Any]							    =    self.get_scheduler_config()
               __lowerCamelCase  :   List[str]							    =    scheduler_class(**__UpperCamelCase				)

               __lowerCamelCase  :   Tuple							    =    self.dummy_sample
               __lowerCamelCase  :   Tuple							    =    0.1 * sample

               if num_inference_steps is not None and hasattr(__UpperCamelCase							,	"""set_timesteps"""				):
                     scheduler.set_timesteps(__UpperCamelCase				)
               elif num_inference_steps is not None and not hasattr(__UpperCamelCase							,	"""set_timesteps"""				):
                     __lowerCamelCase  :   Tuple							    =    num_inference_steps

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

               __lowerCamelCase  :   Any							    =    scheduler.timesteps[5]
               __lowerCamelCase  :   Tuple							    =    scheduler.timesteps[6]

               __lowerCamelCase  :   Union[str, Any]							    =    scheduler.step(__UpperCamelCase							,	__UpperCamelCase							,	__UpperCamelCase							,	**__UpperCamelCase				).prev_sample
               __lowerCamelCase  :   List[str]							    =    scheduler.step(__UpperCamelCase							,	__UpperCamelCase							,	__UpperCamelCase							,	**__UpperCamelCase				).prev_sample

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

               __lowerCamelCase  :   Any							    =    scheduler.step(__UpperCamelCase							,	__UpperCamelCase							,	__UpperCamelCase							,	**__UpperCamelCase				).prev_sample
               __lowerCamelCase  :   Any							    =    scheduler.step(__UpperCamelCase							,	__UpperCamelCase							,	__UpperCamelCase							,	**__UpperCamelCase				).prev_sample

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

   def 	_snake_case     (	self	:			int				):






         '''simple docstring'''



         for timesteps in [1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase							,	time_step=__UpperCamelCase				)

   def 	_snake_case     (	self	:			List[str]				):






         '''simple docstring'''



         for t, num_inference_steps in zip([1, 5, 1_0]							,	[1_0, 5_0, 1_0_0]				):
               self.check_over_forward(num_inference_steps=__UpperCamelCase							,	time_step=__UpperCamelCase				)


   def 	_snake_case     (	self	:			str				):






         '''simple docstring'''



         __lowerCamelCase  :   Tuple							    =    self.full_loop()
         __lowerCamelCase  :   Dict							    =    torch.mean(torch.abs(__UpperCamelCase				)				)

         assert abs(result_mean.item() - 2_5_4_0_5_2_9				) < 1_0





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A		: Dict								=     {
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		: Optional[Any]								=     [
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

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