"""simple docstring"""



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[int]           =		(DPMSolverSinglestepScheduler,)
 _snake_case   :			Optional[Any]           =		(("""num_inference_steps""", 25),)


 def 		a__ (				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 		a__ (				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 		a__ (				self		):
        pass


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


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


 def 		a__ (				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 		a__ (				self		):
        self.check_over_configs(lower_order_final=lowerCamelCase		)
        self.check_over_configs(lower_order_final=lowerCamelCase		)


 def 		a__ (				self		):
        self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
        self.check_over_configs(lambda_min_clipped=-5.1		)


 def 		a__ (				self		):
        self.check_over_configs(variance_type=lowerCamelCase		)
        self.check_over_configs(variance_type="learned_range"		)


 def 		a__ (				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 		a__ (				self		):
        __a     				=  self.full_loop()
        __a     				=  torch.mean(torch.abs(lowerCamelCase		)		)

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


 def 		a__ (				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 		a__ (				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 		a__ (				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 		a__ (				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

"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[Any]           =		["""pixel_values"""]


 def __init__(				self  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = PILImageResampling.BILINEAR  ,						lowerCamelCase = True  ,						lowerCamelCase = 1 / 255  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase		)
        __a     				=  size if size is not None else {"shortest_edge": 384}
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  do_resize
        __a     				=  size
        # Default value set here for backwards compatibility where the value in config is None
        __a     				=  crop_pct if crop_pct is not None else 224 / 256
        __a     				=  resample
        __a     				=  do_rescale
        __a     				=  rescale_factor
        __a     				=  do_normalize
        __a     				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a     				=  image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = PILImageResampling.BICUBIC  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)
        if "shortest_edge" not in size:
               raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"		)
        __a     				=  size["shortest_edge"]

        if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               __a     				=  int(shortest_edge / crop_pct		)
               __a     				=  get_resize_output_image_size(lowerCamelCase  ,						size=lowerCamelCase  ,						default_to_square=lowerCamelCase		)
               __a     				=  resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
        else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return rescale(lowerCamelCase  ,						scale=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return normalize(lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = ChannelDimension.FIRST  ,						**lowerCamelCase  ,						):
        __a     				=  do_resize if do_resize is not None else self.do_resize
        __a     				=  crop_pct if crop_pct is not None else self.crop_pct
        __a     				=  resample if resample is not None else self.resample
        __a     				=  do_rescale if do_rescale is not None else self.do_rescale
        __a     				=  rescale_factor if rescale_factor is not None else self.rescale_factor
        __a     				=  do_normalize if do_normalize is not None else self.do_normalize
        __a     				=  image_mean if image_mean is not None else self.image_mean
        __a     				=  image_std if image_std is not None else self.image_std

        __a     				=  size if size is not None else self.size
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  make_list_of_images(lowerCamelCase		)

        if not valid_images(lowerCamelCase		):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."		)

        if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

        if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError("crop_pct must be specified if size < 384."		)

        if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."		)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."		)

        # All transformations expect numpy arrays.
        __a     				=  [to_numpy_array(lowerCamelCase		) for image in images]

        if do_resize:
               __a     				=  [self.resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						crop_pct=lowerCamelCase  ,						resample=lowerCamelCase		) for image in images]

        if do_rescale:
               __a     				=  [self.rescale(image=lowerCamelCase  ,						scale=lowerCamelCase		) for image in images]

        if do_normalize:
               __a     				=  [self.normalize(image=lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase		) for image in images]

        __a     				=  [to_channel_dimension_format(lowerCamelCase  ,						lowerCamelCase		) for image in images]

        __a     				=  {"pixel_values": images}
        return BatchFeature(data=lowerCamelCase  ,						tensor_type=lowerCamelCase		)
