





'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 		UpperCamelCase__     (       a							):
      '''simple docstring'''






      _snake_case           =				(DPMSolverMultistepScheduler,)
      _snake_case           =				(("num_inference_steps", 25),)




      def 	snake_case  (     self     , **SCREAMING_SNAKE_CASE )				->  Dict:
        __lowerCAmelCase		:    Optional[Any]			     =  {
            'num_train_timesteps': 10_00,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
            'solver_order': 2,
            'prediction_type': 'epsilon',
            'thresholding': False,
            'sample_max_value': 1.0,
            'algorithm_type': 'dpmsolver++',
            'solver_type': 'midpoint',
            'lower_order_final': False,
            'lambda_min_clipped': -float('inf' ),
            'variance_type': None,
        }

        config.update(**UpperCAmelCase__ )
        return config




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE=0     , **SCREAMING_SNAKE_CASE )				->  Union[str, Any]:
        __lowerCAmelCase		:    Union[str, Any]			     =  dict(self.forward_default_kwargs )
        __lowerCAmelCase		:    Tuple			     =  kwargs.pop('num_inference_steps'     , UpperCAmelCase__ )
        __lowerCAmelCase		:    Tuple			     =  self.dummy_sample
        __lowerCAmelCase		:    Optional[int]			     =  0.1 * sample
        __lowerCAmelCase		:    List[Any]			     =  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

        for scheduler_class in self.scheduler_classes:
          __lowerCAmelCase		:    Any			     =  self.get_scheduler_config(**UpperCAmelCase__ )
          __lowerCAmelCase		:    Union[str, Any]			     =  scheduler_class(**UpperCAmelCase__ )
          scheduler.set_timesteps(UpperCAmelCase__ )
          # copy over dummy past residuals
          __lowerCAmelCase		:    Union[str, Any]			     =  dummy_past_residuals[: scheduler.config.solver_order]

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

          __lowerCAmelCase     ,					__lowerCAmelCase		:    Any			     =  sample, sample
          for t in range(UpperCAmelCase__     , time_step + scheduler.config.solver_order + 1 ):
            __lowerCAmelCase		:    Tuple			     =  scheduler.step(UpperCAmelCase__     , UpperCAmelCase__     , UpperCAmelCase__     , **UpperCAmelCase__ ).prev_sample
            __lowerCAmelCase		:    Union[str, 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 )				->  List[Any]:
        pass




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE=0     , **SCREAMING_SNAKE_CASE )				->  Any:
        __lowerCAmelCase		:    List[Any]			     =  dict(self.forward_default_kwargs )
        __lowerCAmelCase		:    List[str]			     =  kwargs.pop('num_inference_steps'     , UpperCAmelCase__ )
        __lowerCAmelCase		:    Union[str, Any]			     =  self.dummy_sample
        __lowerCAmelCase		:    Any			     =  0.1 * sample
        __lowerCAmelCase		:    List[str]			     =  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

        for scheduler_class in self.scheduler_classes:
          __lowerCAmelCase		:    List[str]			     =  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		:    Optional[int]			     =  dummy_past_residuals[: scheduler.config.solver_order]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(UpperCAmelCase__ )
            __lowerCAmelCase		:    str			     =  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		:    List[Any]			     =  dummy_past_residuals[: new_scheduler.config.solver_order]

          __lowerCAmelCase		:    Dict			     =  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"




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE=None     , **SCREAMING_SNAKE_CASE )				->  List[Any]:
        if scheduler is None:
          __lowerCAmelCase		:    Optional[Any]			     =  self.scheduler_classes[0]
          __lowerCAmelCase		:    List[str]			     =  self.get_scheduler_config(**UpperCAmelCase__ )
          __lowerCAmelCase		:    Optional[Any]			     =  scheduler_class(**UpperCAmelCase__ )

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

        for i, t in enumerate(scheduler.timesteps ):
          __lowerCAmelCase		:    List[Any]			     =  model(UpperCAmelCase__     , UpperCAmelCase__ )
          __lowerCAmelCase		:    Optional[Any]			     =  scheduler.step(UpperCAmelCase__     , UpperCAmelCase__     , UpperCAmelCase__ ).prev_sample

        return sample




      def 	snake_case  (     self )				->  Optional[Any]:
        __lowerCAmelCase		:    str			     =  dict(self.forward_default_kwargs )

        __lowerCAmelCase		:    List[Any]			     =  kwargs.pop('num_inference_steps'     , UpperCAmelCase__ )

        for scheduler_class in self.scheduler_classes:
          __lowerCAmelCase		:    Optional[Any]			     =  self.get_scheduler_config()
          __lowerCAmelCase		:    Tuple			     =  scheduler_class(**UpperCAmelCase__ )

          __lowerCAmelCase		:    str			     =  self.dummy_sample
          __lowerCAmelCase		:    Dict			     =  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		:    List[str]			     =  num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          __lowerCAmelCase		:    Any			     =  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
          __lowerCAmelCase		:    Tuple			     =  dummy_past_residuals[: scheduler.config.solver_order]

          __lowerCAmelCase		:    Any			     =  scheduler.timesteps[5]
          __lowerCAmelCase		:    Any			     =  scheduler.timesteps[6]

          __lowerCAmelCase		:    Union[str, 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 )				->  str:
        for timesteps in [25, 50, 1_00, 9_99, 10_00]:
          self.check_over_configs(num_train_timesteps=UpperCAmelCase__ )




      def 	snake_case  (     self )				->  Union[str, Any]:
        self.check_over_configs(thresholding=UpperCAmelCase__ )
        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=UpperCAmelCase__     , prediction_type=UpperCAmelCase__     , sample_max_value=UpperCAmelCase__     , algorithm_type='dpmsolver++'     , solver_order=UpperCAmelCase__     , solver_type=UpperCAmelCase__     , )




      def 	snake_case  (     self )				->  List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=UpperCAmelCase__ )




      def 	snake_case  (     self )				->  Optional[Any]:
        for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
          for solver_type in ["midpoint", "heun"]:
            for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                  if order == 3:
                    continue
                else:
                  self.check_over_configs(
                      solver_order=UpperCAmelCase__     , solver_type=UpperCAmelCase__     , prediction_type=UpperCAmelCase__     , algorithm_type=UpperCAmelCase__     , )
                __lowerCAmelCase		:    int			     =  self.full_loop(
                    solver_order=UpperCAmelCase__     , solver_type=UpperCAmelCase__     , prediction_type=UpperCAmelCase__     , algorithm_type=UpperCAmelCase__     , )
                assert not torch.isnan(UpperCAmelCase__ ).any(), "Samples have nan numbers"




      def 	snake_case  (     self )				->  Optional[Any]:
        self.check_over_configs(lower_order_final=UpperCAmelCase__ )
        self.check_over_configs(lower_order_final=UpperCAmelCase__ )




      def 	snake_case  (     self )				->  Any:
        self.check_over_configs(lambda_min_clipped=-float('inf' ) )
        self.check_over_configs(lambda_min_clipped=-5.1 )




      def 	snake_case  (     self )				->  Dict:
        self.check_over_configs(variance_type=UpperCAmelCase__ )
        self.check_over_configs(variance_type='learned_range' )




      def 	snake_case  (     self )				->  Dict:
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
          self.check_over_forward(num_inference_steps=UpperCAmelCase__     , time_step=0 )




      def 	snake_case  (     self )				->  Any:
        __lowerCAmelCase		:    Optional[Any]			     =  self.full_loop()
        __lowerCAmelCase		:    Optional[Any]			     =  torch.mean(torch.abs(UpperCAmelCase__ ) )

        assert abs(result_mean.item() - 0.3_3_0_1 ) < 1e-3




      def 	snake_case  (     self )				->  Tuple:
        __lowerCAmelCase		:    Optional[Any]			     =  self.full_loop(thresholding=UpperCAmelCase__     , dynamic_thresholding_ratio=0.8_7     , sample_max_value=0.5 )
        __lowerCAmelCase		:    List[Any]			     =  torch.mean(torch.abs(UpperCAmelCase__ ) )

        assert abs(result_mean.item() - 1.1_3_6_4 ) < 1e-3




      def 	snake_case  (     self )				->  List[str]:
        __lowerCAmelCase		:    List[Any]			     =  self.full_loop(prediction_type='v_prediction' )
        __lowerCAmelCase		:    str			     =  torch.mean(torch.abs(UpperCAmelCase__ ) )

        assert abs(result_mean.item() - 0.2_2_5_1 ) < 1e-3




      def 	snake_case  (     self )				->  str:
        __lowerCAmelCase		:    Union[str, Any]			     =  self.full_loop(prediction_type='v_prediction'     , use_karras_sigmas=UpperCAmelCase__ )
        __lowerCAmelCase		:    str			     =  torch.mean(torch.abs(UpperCAmelCase__ ) )

        assert abs(result_mean.item() - 0.2_0_9_6 ) < 1e-3




      def 	snake_case  (     self )				->  Optional[Any]:
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        __lowerCAmelCase		:    int			     =  DPMSolverMultistepScheduler(**self.get_scheduler_config() )
        __lowerCAmelCase		:    str			     =  self.full_loop(scheduler=UpperCAmelCase__ )
        __lowerCAmelCase		:    int			     =  torch.mean(torch.abs(UpperCAmelCase__ ) )

        assert abs(result_mean.item() - 0.3_3_0_1 ) < 1e-3

        __lowerCAmelCase		:    Tuple			     =  DPMSolverSinglestepScheduler.from_config(scheduler.config )
        __lowerCAmelCase		:    Optional[int]			     =  UniPCMultistepScheduler.from_config(scheduler.config )
        __lowerCAmelCase		:    List[str]			     =  DEISMultistepScheduler.from_config(scheduler.config )
        __lowerCAmelCase		:    Any			     =  DPMSolverMultistepScheduler.from_config(scheduler.config )

        __lowerCAmelCase		:    Optional[Any]			     =  self.full_loop(scheduler=UpperCAmelCase__ )
        __lowerCAmelCase		:    Tuple			     =  torch.mean(torch.abs(UpperCAmelCase__ ) )

        assert abs(result_mean.item() - 0.3_3_0_1 ) < 1e-3




      def 	snake_case  (     self )				->  str:
        __lowerCAmelCase		:    List[Any]			     =  self.scheduler_classes[0]
        __lowerCAmelCase		:    Tuple			     =  self.get_scheduler_config(thresholding=UpperCAmelCase__     , dynamic_thresholding_ratio=0 )
        __lowerCAmelCase		:    Optional[Any]			     =  scheduler_class(**UpperCAmelCase__ )

        __lowerCAmelCase		:    Optional[int]			     =  10
        __lowerCAmelCase		:    Dict			     =  self.dummy_model()
        __lowerCAmelCase		:    int			     =  self.dummy_sample_deter.half()
        scheduler.set_timesteps(UpperCAmelCase__ )

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

        assert sample.dtype == torch.floataa







      def 	snake_case  (     self     , **SCREAMING_SNAKE_CASE )				->  Any:
        for scheduler_class in self.scheduler_classes:
          __lowerCAmelCase		:    int			     =  self.get_scheduler_config(**UpperCAmelCase__ )
          __lowerCAmelCase		:    int			     =  scheduler_class(**UpperCAmelCase__ )

          scheduler.set_timesteps(scheduler.config.num_train_timesteps )
          assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps








"""simple docstring"""





import unittest

import numpy as np

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

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 OwlViTImageProcessor




class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def __init__(     self				:     Union[str, Any],   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Optional[int]=7,   UpperCAmelCase__				:     List[str]=3,   UpperCAmelCase__				:     List[Any]=1_8,   UpperCAmelCase__				:     Any=3_0,   UpperCAmelCase__				:     str=4_0_0,   UpperCAmelCase__				:     Union[str, Any]=True,   UpperCAmelCase__				:     Dict=None,   UpperCAmelCase__				:     int=True,   UpperCAmelCase__				:     str=None,   UpperCAmelCase__				:     Dict=True,   UpperCAmelCase__				:     str=[0.48_145_466, 0.4_578_275, 0.40_821_073],   UpperCAmelCase__				:     Dict=[0.26_862_954, 0.26_130_258, 0.27_577_711],   UpperCAmelCase__				:     Any=True,   ):
    __lowercase								=		parent
    __lowercase								=		batch_size
    __lowercase								=		num_channels
    __lowercase								=		image_size
    __lowercase								=		min_resolution
    __lowercase								=		max_resolution
    __lowercase								=		do_resize
    __lowercase								=		size if size is not None else {"height": 1_8, "width": 1_8}
    __lowercase								=		do_center_crop
    __lowercase								=		crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
    __lowercase								=		do_normalize
    __lowercase								=		image_mean
    __lowercase								=		image_std
    __lowercase								=		do_convert_rgb



 def    _lowercase					(     self				:     List[str]      ):
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_convert_rgb": self.do_convert_rgb,
    }







@require_torch
@require_vision
class    _lowerCAmelCase    (      lowercase	,unittest.TestCase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      OwlViTImageProcessor if is_vision_available() else None


 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		OwlViTImageProcessingTester(self      )


 @property
 def    _lowercase					(     self				:     int      ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def    _lowercase					(     self				:     Tuple      ):
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_resize"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "size"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_center_crop"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "center_crop"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_normalize"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "image_mean"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "image_std"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_convert_rgb"      )      )


 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size,   {"height": 1_8, "width": 1_8}      )
    self.assertEqual(image_processor.crop_size,   {"height": 1_8, "width": 1_8}      )

    __lowercase								=		self.image_processing_class.from_dict(self.image_processor_dict,   size=4_2,   crop_size=8_4      )
    self.assertEqual(image_processor.size,   {"height": 4_2, "width": 4_2}      )
    self.assertEqual(image_processor.crop_size,   {"height": 8_4, "width": 8_4}      )


 def    _lowercase					(     self				:     Any      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__      )

    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   Image.Image      )

    # Test not batched input
    __lowercase								=		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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )

    # Test batched
    __lowercase								=		image_processing(UpperCAmelCase__,   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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )


 def    _lowercase					(     self				:     Optional[Any]      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__,   numpify=UpperCAmelCase__      )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   np.ndarray      )

    # Test not batched input
    __lowercase								=		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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )

    # Test batched
    __lowercase								=		image_processing(UpperCAmelCase__,   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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )



 def    _lowercase					(     self				:     List[str]      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__,   torchify=UpperCAmelCase__      )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   torch.Tensor      )

    # Test not batched input
    __lowercase								=		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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )

    # Test batched
    __lowercase								=		image_processing(UpperCAmelCase__,   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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )




