





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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase     :     int							=	logging.get_logger(__name__)


if is_vision_available():
     import PIL







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : int            =      ["""pixel_values"""]







      def __init__(   self		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = PILImageResampling.BICUBIC		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = 1 / 2_5_5		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = True		,				**lowerCAmelCase__		,				)       ->						None:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)
        a__     : Dict  =size if size is not None else {"shortest_edge": 2_2_4}
        a__     : int  =get_size_dict(lowerCAmelCase__		,				default_to_square=lowerCAmelCase__							)
        a__     : List[str]  =crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
        a__     : str  =get_size_dict(lowerCAmelCase__		,				default_to_square=lowerCAmelCase__		,				param_name="crop_size"							)

        a__     : Union[str, Any]  =do_resize
        a__     : Tuple  =size
        a__     : List[str]  =resample
        a__     : int  =do_center_crop
        a__     : Any  =crop_size
        a__     : Tuple  =do_rescale
        a__     : Optional[int]  =rescale_factor
        a__     : str  =do_normalize
        a__     : Optional[Any]  =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        a__     : List[str]  =image_std if image_std is not None else OPENAI_CLIP_STD
        a__     : str  =do_convert_rgb







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = PILImageResampling.BICUBIC		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						np.ndarray:



        '''simple docstring'''




        a__     : List[Any]  =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()}'''							)
        a__     : Dict  =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		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						np.ndarray:



        '''simple docstring'''




        a__     : str  =get_size_dict(lowerCAmelCase__							)
        if "height" not in size or "width" not in size:
          raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
        return center_crop(lowerCAmelCase__		,				size=(size["height"], size["width"])		,				data_format=lowerCAmelCase__		,				**lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						Dict:



        '''simple docstring'''




        return rescale(lowerCAmelCase__		,				scale=lowerCAmelCase__		,				data_format=lowerCAmelCase__		,				**lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						np.ndarray:



        '''simple docstring'''




        return normalize(lowerCAmelCase__		,				mean=lowerCAmelCase__		,				std=lowerCAmelCase__		,				data_format=lowerCAmelCase__		,				**lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = ChannelDimension.FIRST		,				**lowerCAmelCase__		,				)       ->						PIL.Image.Image:



        '''simple docstring'''




        a__     : List[str]  =do_resize if do_resize is not None else self.do_resize
        a__     : Optional[Any]  =size if size is not None else self.size
        a__     : int  =get_size_dict(lowerCAmelCase__		,				param_name="size"		,				default_to_square=lowerCAmelCase__							)
        a__     : str  =resample if resample is not None else self.resample
        a__     : Tuple  =do_center_crop if do_center_crop is not None else self.do_center_crop
        a__     : Optional[Any]  =crop_size if crop_size is not None else self.crop_size
        a__     : Any  =get_size_dict(lowerCAmelCase__		,				param_name="crop_size"		,				default_to_square=lowerCAmelCase__							)
        a__     : Tuple  =do_rescale if do_rescale is not None else self.do_rescale
        a__     : Optional[int]  =rescale_factor if rescale_factor is not None else self.rescale_factor
        a__     : Union[str, Any]  =do_normalize if do_normalize is not None else self.do_normalize
        a__     : int  =image_mean if image_mean is not None else self.image_mean
        a__     : List[Any]  =image_std if image_std is not None else self.image_std
        a__     : Tuple  =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        a__     : Optional[int]  =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."							)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
          a__     : List[str]  =[convert_to_rgb(lowerCAmelCase__							) for image in images]

        # All transformations expect numpy arrays.
        a__     : List[str]  =[to_numpy_array(lowerCAmelCase__							) for image in images]

        if do_resize:
          a__     : List[Any]  =[self.resize(image=lowerCAmelCase__		,				size=lowerCAmelCase__		,				resample=lowerCAmelCase__							) for image in images]

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

        if do_rescale:
          a__     : List[Any]  =[self.rescale(image=lowerCAmelCase__		,				scale=lowerCAmelCase__							) for image in images]

        if do_normalize:
          a__     : Optional[Any]  =[self.normalize(image=lowerCAmelCase__		,				mean=lowerCAmelCase__		,				std=lowerCAmelCase__							) for image in images]

        a__     : Union[str, Any]  =[to_channel_dimension_format(lowerCAmelCase__		,				lowerCAmelCase__							) for image in images]

        a__     : List[str]  ={"pixel_values": images}
        return BatchFeature(data=lowerCAmelCase__		,				tensor_type=lowerCAmelCase__							)













import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Any            =      (PNDMScheduler,)
      _lowercase    : str            =      (("""num_inference_steps""", 50),)







      def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : Dict  ={
            "num_train_timesteps": 1_0_0_0,
            "beta_start": 0.00_01,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**lowerCAmelCase__							)
        return config







      def        _lowercase  (   self		,				lowerCAmelCase__=0		,				**lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =dict(self.forward_default_kwargs							)
        a__     : Tuple  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)
        a__     : List[str]  =self.dummy_sample
        a__     : List[str]  =0.1 * sample
        a__     : str  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          a__     : int  =self.get_scheduler_config(**lowerCAmelCase__							)
          a__     : Union[str, Any]  =scheduler_class(**lowerCAmelCase__							)
          scheduler.set_timesteps(lowerCAmelCase__							)
          # copy over dummy past residuals
          a__     : Any  =dummy_past_residuals[:]

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

          a__     : Any  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Dict  =new_scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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

          a__     : Optional[int]  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Union[str, Any]  =new_scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        pass







      def        _lowercase  (   self		,				lowerCAmelCase__=0		,				**lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =dict(self.forward_default_kwargs							)
        a__     : List[str]  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)
        a__     : List[str]  =self.dummy_sample
        a__     : int  =0.1 * sample
        a__     : Tuple  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          a__     : Dict  =self.get_scheduler_config()
          a__     : List[str]  =scheduler_class(**lowerCAmelCase__							)
          scheduler.set_timesteps(lowerCAmelCase__							)

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

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(lowerCAmelCase__							)
            a__     : Dict  =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__     : Optional[int]  =dummy_past_residuals[:]

          a__     : Optional[Any]  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : List[Any]  =new_scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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

          a__     : List[str]  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Any  =new_scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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







      def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.scheduler_classes[0]
        a__     : Optional[Any]  =self.get_scheduler_config(**lowerCAmelCase__							)
        a__     : Any  =scheduler_class(**lowerCAmelCase__							)

        a__     : int  =1_0
        a__     : Union[str, Any]  =self.dummy_model()
        a__     : Optional[int]  =self.dummy_sample_deter
        scheduler.set_timesteps(lowerCAmelCase__							)

        for i, t in enumerate(scheduler.prk_timesteps							):
          a__     : List[Any]  =model(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : Optional[Any]  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps							):
          a__     : int  =model(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : int  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample

        return sample







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : str  =dict(self.forward_default_kwargs							)

        a__     : Tuple  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)

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

          a__     : List[Any]  =self.dummy_sample
          a__     : Dict  =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"							):
            a__     : int  =num_inference_steps

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

          a__     : List[Any]  =scheduler.step_prk(lowerCAmelCase__		,				0		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : int  =scheduler.step_prk(lowerCAmelCase__		,				1		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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

          a__     : List[str]  =scheduler.step_plms(lowerCAmelCase__		,				0		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Dict  =scheduler.step_plms(lowerCAmelCase__		,				1		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowerCAmelCase__							)

        a__     : Optional[Any]  =self.scheduler_classes[0]
        a__     : Tuple  =self.get_scheduler_config(steps_offset=1							)
        a__     : Optional[Any]  =scheduler_class(**lowerCAmelCase__							)
        scheduler.set_timesteps(1_0							)
        assert torch.equal(
            scheduler.timesteps		,				torch.LongTensor(
                [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]							)		,				)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        for beta_start, beta_end in zip([0.00_01, 0.0_01]		,				[0.0_02, 0.02]							):
          self.check_over_configs(beta_start=lowerCAmelCase__		,				beta_end=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        for t in [1, 5, 1_0]:
          self.check_over_forward(time_step=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						List[Any]:



        '''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=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : Dict  =2_7

        for scheduler_class in self.scheduler_classes:
          a__     : Tuple  =self.dummy_sample
          a__     : Dict  =0.1 * sample

          a__     : Dict  =self.get_scheduler_config()
          a__     : int  =scheduler_class(**lowerCAmelCase__							)

          scheduler.set_timesteps(lowerCAmelCase__							)

          # before power of 3 fix, would error on first step, so we only need to do two
          for i, t in enumerate(scheduler.prk_timesteps[:2]							):
            a__     : Any  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        with self.assertRaises(lowerCAmelCase__							):
          a__     : List[Any]  =self.scheduler_classes[0]
          a__     : Dict  =self.get_scheduler_config()
          a__     : Tuple  =scheduler_class(**lowerCAmelCase__							)

          scheduler.step_plms(self.dummy_sample		,				1		,				self.dummy_sample							).prev_sample







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[Any]  =self.full_loop()
        a__     : str  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Optional[Any]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 1_98.13_18							) < 1E-2
        assert abs(result_mean.item() - 0.25_80							) < 1E-3







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : str  =self.full_loop(prediction_type="v_prediction"							)
        a__     : int  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Optional[int]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 67.39_86							) < 1E-2
        assert abs(result_mean.item() - 0.08_78							) < 1E-3







      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Tuple  =self.full_loop(set_alpha_to_one=lowerCAmelCase__		,				beta_start=0.01							)
        a__     : str  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Dict  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 2_30.03_99							) < 1E-2
        assert abs(result_mean.item() - 0.29_95							) < 1E-3







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Dict  =self.full_loop(set_alpha_to_one=lowerCAmelCase__		,				beta_start=0.01							)
        a__     : Union[str, Any]  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Union[str, Any]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 1_86.94_82							) < 1E-2
        assert abs(result_mean.item() - 0.24_34							) < 1E-3






