


'''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, logging


__A					:  Any											=      logging.get_logger(__name__)






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					['pixel_values']

    def __init__(   self    :  Tuple    ,						lowerCamelCase    :  bool = True    ,						lowerCamelCase    :  Optional[Dict[str, int]] = None    ,						lowerCamelCase    :  PILImageResampling = PILImageResampling.BILINEAR    ,						lowerCamelCase    :  bool = True    ,						lowerCamelCase    :  Dict[str, int] = None    ,						lowerCamelCase    :  bool = True    ,						lowerCamelCase    :  Union[int, float] = 1 / 2_55    ,						lowerCamelCase    :  bool = True    ,						lowerCamelCase    :  Optional[Union[float, List[float]]] = None    ,						lowerCamelCase    :  Optional[Union[float, List[float]]] = None    ,						**lowerCamelCase    :  Optional[int]    ,						)   ->  None:
      super().__init__(**lowerCamelCase		)
      lowerCAmelCase_			:      Dict			=   size if size is not None else {"""shortest_edge""": 2_56}
      lowerCAmelCase_			:      List[str]			=   get_size_dict(lowerCamelCase    ,						default_to_square=lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
      lowerCAmelCase_			:      Any			=   get_size_dict(lowerCamelCase		)
      lowerCAmelCase_			:      List[Any]			=   do_resize
      lowerCAmelCase_			:      List[Any]			=   size
      lowerCAmelCase_			:      Tuple			=   resample
      lowerCAmelCase_			:      str			=   do_center_crop
      lowerCAmelCase_			:      Any			=   crop_size
      lowerCAmelCase_			:      Tuple			=   do_rescale
      lowerCAmelCase_			:      str			=   rescale_factor
      lowerCAmelCase_			:      Optional[int]			=   do_normalize
      lowerCAmelCase_			:      Optional[Any]			=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      lowerCAmelCase_			:      Union[str, Any]			=   image_std if image_std is not None else IMAGENET_STANDARD_STD

    def __lowercase   (   self    :  str    ,						lowerCamelCase    :  np.ndarray    ,						lowerCamelCase    :  Dict[str, int]    ,						lowerCamelCase    :  PILImageResampling = PILImageResampling.BICUBIC    ,						lowerCamelCase    :  Optional[Union[str, ChannelDimension]] = None    ,						**lowerCamelCase    :  int    ,						)   ->  np.ndarray:
      lowerCAmelCase_			:      int			=   get_size_dict(lowerCamelCase    ,						default_to_square=lowerCamelCase		)
      if "shortest_edge" not in size:
        raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'		)
      lowerCAmelCase_			:      List[str]			=   get_resize_output_image_size(lowerCamelCase    ,						size=size["""shortest_edge"""]    ,						default_to_square=lowerCamelCase		)
      return resize(lowerCamelCase    ,						size=lowerCamelCase    ,						resample=lowerCamelCase    ,						data_format=lowerCamelCase    ,						**lowerCamelCase		)

    def __lowercase   (   self    :  Any    ,						lowerCamelCase    :  np.ndarray    ,						lowerCamelCase    :  Dict[str, int]    ,						lowerCamelCase    :  Optional[Union[str, ChannelDimension]] = None    ,						**lowerCamelCase    :  Union[str, Any]    ,						)   ->  np.ndarray:
      lowerCAmelCase_			:      Union[str, Any]			=   get_size_dict(lowerCamelCase		)
      return center_crop(lowerCamelCase    ,						size=(size["""height"""], size["""width"""])    ,						data_format=lowerCamelCase    ,						**lowerCamelCase		)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  np.ndarray    ,						lowerCamelCase    :  float    ,						lowerCamelCase    :  Optional[Union[str, ChannelDimension]] = None    ,						**lowerCamelCase    :  str		)   ->  np.ndarray:
      return rescale(lowerCamelCase    ,						scale=lowerCamelCase    ,						data_format=lowerCamelCase    ,						**lowerCamelCase		)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  np.ndarray    ,						lowerCamelCase    :  Union[float, List[float]]    ,						lowerCamelCase    :  Union[float, List[float]]    ,						lowerCamelCase    :  Optional[Union[str, ChannelDimension]] = None    ,						**lowerCamelCase    :  Dict    ,						)   ->  np.ndarray:
      return normalize(lowerCamelCase    ,						mean=lowerCamelCase    ,						std=lowerCamelCase    ,						data_format=lowerCamelCase    ,						**lowerCamelCase		)





    def __lowercase   (   self    :  Any    ,						lowerCamelCase    :  ImageInput    ,						lowerCamelCase    :  Optional[bool] = None    ,						lowerCamelCase    :  Dict[str, int] = None    ,						lowerCamelCase    :  PILImageResampling = None    ,						lowerCamelCase    :  bool = None    ,						lowerCamelCase    :  Dict[str, int] = None    ,						lowerCamelCase    :  Optional[bool] = None    ,						lowerCamelCase    :  Optional[float] = None    ,						lowerCamelCase    :  Optional[bool] = None    ,						lowerCamelCase    :  Optional[Union[float, List[float]]] = None    ,						lowerCamelCase    :  Optional[Union[float, List[float]]] = None    ,						lowerCamelCase    :  Optional[Union[str, TensorType]] = None    ,						lowerCamelCase    :  Union[str, ChannelDimension] = ChannelDimension.FIRST    ,						**lowerCamelCase    :  Optional[int]    ,						)   ->  Union[str, Any]:
      lowerCAmelCase_			:      List[Any]			=   do_resize if do_resize is not None else self.do_resize
      lowerCAmelCase_			:      Tuple			=   size if size is not None else self.size
      lowerCAmelCase_			:      List[Any]			=   get_size_dict(lowerCamelCase    ,						default_to_square=lowerCamelCase		)
      lowerCAmelCase_			:      Dict			=   resample if resample is not None else self.resample
      lowerCAmelCase_			:      str			=   do_center_crop if do_center_crop is not None else self.do_center_crop
      lowerCAmelCase_			:      Any			=   crop_size if crop_size is not None else self.crop_size
      lowerCAmelCase_			:      Dict			=   get_size_dict(lowerCamelCase		)
      lowerCAmelCase_			:      List[Any]			=   do_rescale if do_rescale is not None else self.do_rescale
      lowerCAmelCase_			:      List[str]			=   rescale_factor if rescale_factor is not None else self.rescale_factor
      lowerCAmelCase_			:      Tuple			=   do_normalize if do_normalize is not None else self.do_normalize
      lowerCAmelCase_			:      Dict			=   image_mean if image_mean is not None else self.image_mean
      lowerCAmelCase_			:      Dict			=   image_std if image_std is not None else self.image_std

      lowerCAmelCase_			:      Optional[Any]			=   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:
        raise ValueError("""Size must be specified if do_resize is True."""		)

      if do_center_crop and crop_size is None:
        raise ValueError("""Crop size must be specified if do_center_crop is True."""		)

      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.
      lowerCAmelCase_			:      Tuple			=   [to_numpy_array(lowerCamelCase		) for image in images]

      if do_resize:
        lowerCAmelCase_			:      Optional[Any]			=   [self.resize(image=lowerCamelCase    ,						size=lowerCamelCase    ,						resample=lowerCamelCase		) for image in images]

      if do_center_crop:
        lowerCAmelCase_			:      Tuple			=   [self.center_crop(image=lowerCamelCase    ,						size=lowerCamelCase		) for image in images]

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

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

      lowerCAmelCase_			:      Any			=   [to_channel_dimension_format(lowerCamelCase    ,						lowerCamelCase		) for image in images]

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







'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					(IPNDMScheduler,)
    lowercase    =					(('num_inference_steps', 50),)

    def __lowercase   (   self    :  Optional[Any]    ,						**lowerCamelCase    :  int		)   ->  Tuple:
      lowerCAmelCase_			:      Dict			=   {"""num_train_timesteps""": 10_00}
      config.update(**lowerCamelCase		)
      return config

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Tuple=0    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Optional[Any]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      Dict			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   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[str]			=   self.get_scheduler_config(**lowerCamelCase		)
        lowerCAmelCase_			:      int			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)
        # copy over dummy past residuals
        lowerCAmelCase_			:      Optional[int]			=   dummy_past_residuals[:]

        if time_step is None:
          lowerCAmelCase_			:      Optional[Any]			=   scheduler.timesteps[len(scheduler.timesteps		) // 2]

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

        lowerCAmelCase_			:      int			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Dict			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Any			=   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 __lowercase   (   self    :  str		)   ->  Tuple:
      pass

    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  int=0    ,						**lowerCamelCase    :  Any		)   ->  int:
      lowerCAmelCase_			:      Optional[int]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      List[str]			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   0.1 * sample
      lowerCAmelCase_			:      List[Any]			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Optional[int]			=   self.get_scheduler_config()
        lowerCAmelCase_			:      Optional[int]			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)

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

        if time_step is None:
          lowerCAmelCase_			:      Optional[Any]			=   scheduler.timesteps[len(scheduler.timesteps		) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowerCamelCase		)
          lowerCAmelCase_			:      Optional[Any]			=   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)
          lowerCAmelCase_			:      Union[str, Any]			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[Any]			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      str			=   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 __lowercase   (   self    :  Optional[int]    ,						**lowerCamelCase    :  Optional[Any]		)   ->  str:
      lowerCAmelCase_			:      Optional[int]			=   self.scheduler_classes[0]
      lowerCAmelCase_			:      List[Any]			=   self.get_scheduler_config(**lowerCamelCase		)
      lowerCAmelCase_			:      str			=   scheduler_class(**lowerCamelCase		)

      lowerCAmelCase_			:      Dict			=   10
      lowerCAmelCase_			:      Optional[int]			=   self.dummy_model()
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample_deter
      scheduler.set_timesteps(lowerCamelCase		)

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Optional[Any]			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      str			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Dict			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      return sample

    def __lowercase   (   self    :  Tuple		)   ->  Dict:
      lowerCAmelCase_			:      List[Any]			=   dict(self.forward_default_kwargs		)

      lowerCAmelCase_			:      int			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Tuple			=   self.get_scheduler_config()
        lowerCAmelCase_			:      List[str]			=   scheduler_class(**lowerCamelCase		)

        lowerCAmelCase_			:      int			=   self.dummy_sample
        lowerCAmelCase_			:      Tuple			=   0.1 * sample

        if num_inference_steps is not None and hasattr(lowerCamelCase    ,						"""set_timesteps"""		):
          scheduler.set_timesteps(lowerCamelCase		)
        elif num_inference_steps is not None and not hasattr(lowerCamelCase    ,						"""set_timesteps"""		):
          lowerCAmelCase_			:      Dict			=   num_inference_steps

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

        lowerCAmelCase_			:      List[str]			=   scheduler.timesteps[5]
        lowerCAmelCase_			:      str			=   scheduler.timesteps[6]

        lowerCAmelCase_			:      Dict			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[int]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

    def __lowercase   (   self    :  Any		)   ->  int:
      for timesteps in [1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase    ,						time_step=lowerCamelCase		)

    def __lowercase   (   self    :  Optional[int]		)   ->  Optional[int]:
      for t, num_inference_steps in zip([1, 5, 10]    ,						[10, 50, 1_00]		):
        self.check_over_forward(num_inference_steps=lowerCamelCase    ,						time_step=lowerCamelCase		)





    def __lowercase   (   self    :  int		)   ->  List[Any]:
      lowerCAmelCase_			:      List[Any]			=   self.full_loop()
      lowerCAmelCase_			:      Any			=   torch.mean(torch.abs(lowerCamelCase		)		)

      assert abs(result_mean.item() - 2_54_05_29		) < 10



