

import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import DPTImageProcessor






class        UpperCAmelCase__		(       unittest.TestCase       ):






  """simple docstring"""


  def __init__(							self						:		List[Any]	,   __lowerCamelCase						:		Dict	,   __lowerCamelCase						:		List[Any]=7	,   __lowerCamelCase						:		Union[str, Any]=3	,   __lowerCamelCase						:		Union[str, Any]=18	,   __lowerCamelCase						:		Union[str, Any]=30	,   __lowerCamelCase						:		Optional[int]=400	,   __lowerCamelCase						:		Dict=True	,   __lowerCamelCase						:		List[Any]=None	,   __lowerCamelCase						:		Union[str, Any]=True	,   __lowerCamelCase						:		Dict=[0.5, 0.5, 0.5]	,   __lowerCamelCase						:		List[Any]=[0.5, 0.5, 0.5]	,   )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__    =    size if size is not None else {'''height''': 18, '''width''': 18}
       SCREAMING_SNAKE_CASE__    =    parent
       SCREAMING_SNAKE_CASE__    =    batch_size
       SCREAMING_SNAKE_CASE__    =    num_channels
       SCREAMING_SNAKE_CASE__    =    image_size
       SCREAMING_SNAKE_CASE__    =    min_resolution
       SCREAMING_SNAKE_CASE__    =    max_resolution
       SCREAMING_SNAKE_CASE__    =    do_resize
       SCREAMING_SNAKE_CASE__    =    size
       SCREAMING_SNAKE_CASE__    =    do_normalize
       SCREAMING_SNAKE_CASE__    =    image_mean
       SCREAMING_SNAKE_CASE__    =    image_std







  def       lowercase_		(							self						:		str					)		->							Optional[int]:
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }






@require_torch
@require_vision
class        UpperCAmelCase__		(       UpperCamelCase__						,     unittest.TestCase       ):






  """simple docstring"""


  a           =					DPTImageProcessor if is_vision_available() else None






  def       lowercase_		(							self						:		Any					)		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__    =    DPTImageProcessingTester(self					)






  @property
  def       lowercase_		(							self						:		List[str]					)		->							Any:
       return self.image_processor_tester.prepare_image_processor_dict()






  def       lowercase_		(							self						:		List[str]					)		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__    =    self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(__lowerCamelCase	,   '''image_mean'''					)					)
       self.assertTrue(hasattr(__lowerCamelCase	,   '''image_std'''					)					)
       self.assertTrue(hasattr(__lowerCamelCase	,   '''do_normalize'''					)					)
       self.assertTrue(hasattr(__lowerCamelCase	,   '''do_resize'''					)					)
       self.assertTrue(hasattr(__lowerCamelCase	,   '''size'''					)					)






  def       lowercase_		(							self						:		Optional[int]					)		->							Tuple:
       SCREAMING_SNAKE_CASE__    =    self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size	,   {'''height''': 18, '''width''': 18}					)

       SCREAMING_SNAKE_CASE__    =    self.image_processing_class.from_dict(self.image_processor_dict	,   size=42					)
       self.assertEqual(image_processor.size	,   {'''height''': 42, '''width''': 42}					)






  def       lowercase_		(							self						:		str					)		->							Tuple:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__    =    self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       SCREAMING_SNAKE_CASE__    =    prepare_image_inputs(self.image_processor_tester	,   equal_resolution=__lowerCamelCase					)
       for image in image_inputs:
            self.assertIsInstance(__lowerCamelCase	,   Image.Image					)

       # Test not batched input
       SCREAMING_SNAKE_CASE__    =    image_processing(image_inputs[0]	,   return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape	,   (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,   )

       # Test batched
       SCREAMING_SNAKE_CASE__    =    image_processing(__lowerCamelCase	,   return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape	,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,   )






  def       lowercase_		(							self						:		Dict					)		->							Union[str, Any]:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__    =    self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__    =    prepare_image_inputs(self.image_processor_tester	,   equal_resolution=__lowerCamelCase	,   numpify=__lowerCamelCase					)
       for image in image_inputs:
            self.assertIsInstance(__lowerCamelCase	,   np.ndarray					)

       # Test not batched input
       SCREAMING_SNAKE_CASE__    =    image_processing(image_inputs[0]	,   return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape	,   (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,   )

       # Test batched
       SCREAMING_SNAKE_CASE__    =    image_processing(__lowerCamelCase	,   return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape	,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,   )







  def       lowercase_		(							self						:		Optional[Any]					)		->							str:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__    =    self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__    =    prepare_image_inputs(self.image_processor_tester	,   equal_resolution=__lowerCamelCase	,   torchify=__lowerCamelCase					)
       for image in image_inputs:
            self.assertIsInstance(__lowerCamelCase	,   torch.Tensor					)

       # Test not batched input
       SCREAMING_SNAKE_CASE__    =    image_processing(image_inputs[0]	,   return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape	,   (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,   )

       # Test batched
       SCREAMING_SNAKE_CASE__    =    image_processing(__lowerCamelCase	,   return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape	,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )	,   )









import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DPMSolverSinglestepScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''num_inference_steps''', 25),)



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''solver_order''': 2,
            '''prediction_type''': '''epsilon''',
            '''thresholding''': False,
            '''sample_max_value''': 1.0,
            '''algorithm_type''': '''dpmsolver++''',
            '''solver_type''': '''midpoint''',
            '''lambda_min_clipped''': -float('''inf''' ),
            '''variance_type''': None,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )
               scheduler.set_timesteps(__lowerCamelCase )
               # copy over dummy past residuals
               A__											=						dummy_past_residuals[: scheduler.config.solver_order]

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

               A__					,	A__											=						sample, sample
               for t in range(__lowerCamelCase,time_step + scheduler.config.solver_order + 1 ):
                      A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
                      A__											=						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 				UpperCamelCase			(							self ):
        pass



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config()
               A__											=						scheduler_class(**__lowerCamelCase )
               scheduler.set_timesteps(__lowerCamelCase )

               # copy over dummy past residuals (must be after setting timesteps)
               A__											=						dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__lowerCamelCase )
                      A__											=						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)
                      A__											=						dummy_past_residuals[: new_scheduler.config.solver_order]

               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
               A__											=						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 				UpperCamelCase			(							self,__lowerCamelCase=None,**__lowerCamelCase ):
        if scheduler is None:
               A__											=						self.scheduler_classes[0]
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

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

        return sample



 def 				UpperCamelCase			(							self ):
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						50
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:] ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2574 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3

        A__											=						DEISMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverMultistepScheduler.from_config(scheduler.config )
        A__											=						UniPCMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverSinglestepScheduler.from_config(scheduler.config )

        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        thresholding=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,algorithm_type='''dpmsolver++''',solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,)



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



 def 				UpperCamelCase			(							self ):
        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    A__											=						self.full_loop(
                                        solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    assert not torch.isnan(__lowerCamelCase ).any(), "Samples have nan numbers"



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lower_order_final=__lowerCamelCase )
        self.check_over_configs(lower_order_final=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
        self.check_over_configs(lambda_min_clipped=-5.1 )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(variance_type=__lowerCamelCase )
        self.check_over_configs(variance_type='''learned_range''' )



 def 				UpperCamelCase			(							self ):
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=__lowerCamelCase,time_step=0 )



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2248 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.1453 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''',use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.0649 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(thresholding=__lowerCamelCase,dynamic_thresholding_ratio=0 )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowerCamelCase )

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

        assert sample.dtype == torch.floataa





