





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 			__UpperCamelCase    (							A__    ):
     __A	:  Union[str, Any]							  =   (DDPMParallelScheduler,)





     def 		UpperCamelCase(     self				,							**_UpperCamelCase					):
        _UpperCAmelCase           =   {
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''variance_type''': '''fixed_small''',
            '''clip_sample''': True,
        }

        config.update(**_UpperCamelCase					)
        return config





     def 		UpperCamelCase(     self					):
        for timesteps in [1, 5, 100, 1000]:
           self.check_over_configs(num_train_timesteps=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]				,							[0.002, 0.02, 0.2, 2]					):
           self.check_over_configs(beta_start=_UpperCamelCase				,							beta_end=_UpperCamelCase					)





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





     def 		UpperCamelCase(     self					):
        for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        self.check_over_configs(thresholding=_UpperCamelCase					)
        for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=_UpperCamelCase				,							prediction_type=_UpperCamelCase				,							sample_max_value=_UpperCamelCase				,							)





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





     def 		UpperCamelCase(     self					):
        for t in [0, 500, 999]:
           self.check_over_forward(time_step=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.00979					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.02					)					) < 1e-5





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   len(_UpperCamelCase					)

        _UpperCAmelCase           =   self.dummy_model()
        _UpperCAmelCase           =   self.dummy_sample_deter
        _UpperCAmelCase           =   self.dummy_sample_deter + 0.1
        _UpperCAmelCase           =   self.dummy_sample_deter - 0.1

        _UpperCAmelCase           =   samplea.shape[0]
        _UpperCAmelCase           =   torch.stack([samplea, samplea, samplea]				,							dim=0					)
        _UpperCAmelCase           =   torch.arange(_UpperCamelCase					)[0:3, None].repeat(1				,							_UpperCamelCase					)

        _UpperCAmelCase           =   model(samples.flatten(0				,							1					)				,							timesteps.flatten(0				,							1					)					)
        _UpperCAmelCase           =   scheduler.batch_step_no_noise(_UpperCamelCase				,							timesteps.flatten(0				,							1					)				,							samples.flatten(0				,							1					)					)

        _UpperCAmelCase           =   torch.sum(torch.abs(_UpperCamelCase					)					)
        _UpperCAmelCase           =   torch.mean(torch.abs(_UpperCamelCase					)					)

        assert abs(result_sum.item() - 1153.1833					) < 1e-2
        assert abs(result_mean.item() - 0.5005					) < 1e-3





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   len(_UpperCamelCase					)

        _UpperCAmelCase           =   self.dummy_model()
        _UpperCAmelCase           =   self.dummy_sample_deter
        _UpperCAmelCase           =   torch.manual_seed(0					)

        for t in reversed(range(_UpperCamelCase					)					):
           # 1. predict noise residual
           _UpperCAmelCase           =   model(_UpperCamelCase				,							_UpperCamelCase					)

           # 2. predict previous mean of sample x_t-1
           _UpperCAmelCase           =   scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							generator=_UpperCamelCase					).prev_sample

           _UpperCAmelCase           =   pred_prev_sample

        _UpperCAmelCase           =   torch.sum(torch.abs(_UpperCamelCase					)					)
        _UpperCAmelCase           =   torch.mean(torch.abs(_UpperCamelCase					)					)

        assert abs(result_sum.item() - 258.9606					) < 1e-2
        assert abs(result_mean.item() - 0.3372					) < 1e-3





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config(prediction_type='''v_prediction'''					)
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   len(_UpperCamelCase					)

        _UpperCAmelCase           =   self.dummy_model()
        _UpperCAmelCase           =   self.dummy_sample_deter
        _UpperCAmelCase           =   torch.manual_seed(0					)

        for t in reversed(range(_UpperCamelCase					)					):
           # 1. predict noise residual
           _UpperCAmelCase           =   model(_UpperCamelCase				,							_UpperCamelCase					)

           # 2. predict previous mean of sample x_t-1
           _UpperCAmelCase           =   scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							generator=_UpperCamelCase					).prev_sample

           _UpperCAmelCase           =   pred_prev_sample

        _UpperCAmelCase           =   torch.sum(torch.abs(_UpperCamelCase					)					)
        _UpperCAmelCase           =   torch.mean(torch.abs(_UpperCamelCase					)					)

        assert abs(result_sum.item() - 202.0296					) < 1e-2
        assert abs(result_mean.item() - 0.2631					) < 1e-3





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   [100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=_UpperCamelCase					)

        _UpperCAmelCase           =   scheduler.timesteps

        for i, timestep in enumerate(_UpperCamelCase					):
           if i == len(_UpperCamelCase					) - 1:
              _UpperCAmelCase           =   -1
           else:
              _UpperCAmelCase           =   timesteps[i + 1]

           _UpperCAmelCase           =   scheduler.previous_timestep(_UpperCamelCase					)
           _UpperCAmelCase           =   prev_t.item()

           self.assertEqual(_UpperCamelCase				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   [100, 87, 50, 51, 0]

        with self.assertRaises(_UpperCamelCase				,							msg='''`custom_timesteps` must be in descending order.'''					):
           scheduler.set_timesteps(timesteps=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   [100, 87, 50, 1, 0]
        _UpperCAmelCase           =   len(_UpperCamelCase					)

        with self.assertRaises(_UpperCamelCase				,							msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''					):
           scheduler.set_timesteps(num_inference_steps=_UpperCamelCase				,							timesteps=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   [scheduler.config.num_train_timesteps]

        with self.assertRaises(
            _UpperCamelCase				,							msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,							):
           scheduler.set_timesteps(timesteps=_UpperCamelCase					)






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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  int							  =   ["""pixel_values"""]





     def __init__(     self				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = PILImageResampling.BICUBIC				,							_UpperCamelCase = True				,							_UpperCamelCase = True				,							_UpperCamelCase = 1 / 255				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        super().__init__(**_UpperCamelCase					)
        _UpperCAmelCase           =   size if size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        _UpperCAmelCase           =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							default_to_square=_UpperCamelCase				,							param_name='''crop_size'''					)

        _UpperCAmelCase           =   do_resize
        _UpperCAmelCase           =   do_rescale
        _UpperCAmelCase           =   do_normalize
        _UpperCAmelCase           =   do_center_crop
        _UpperCAmelCase           =   crop_size
        _UpperCAmelCase           =   size
        _UpperCAmelCase           =   resample
        _UpperCAmelCase           =   rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _UpperCAmelCase           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = PILImageResampling.BILINEAR				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if "shortest_edge" in size:
           _UpperCAmelCase           =   get_resize_output_image_size(_UpperCamelCase				,							size=size['''shortest_edge''']				,							default_to_square=_UpperCamelCase					)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
           _UpperCAmelCase           =   (size['''height'''], size['''width'''])
        else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''					)
        return resize(_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        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(_UpperCamelCase				,							size=(size['''height'''], size['''width'''])				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase					):
        return rescale(_UpperCamelCase				,							scale=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        return normalize(_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = ChannelDimension.FIRST				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase           =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase           =   do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase           =   do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase           =   crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							param_name='''crop_size'''				,							default_to_square=_UpperCamelCase					)
        _UpperCAmelCase           =   resample if resample is not None else self.resample
        _UpperCAmelCase           =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase           =   image_std if image_std is not None else self.image_std

        _UpperCAmelCase           =   size if size is not None else self.size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if not is_batched(_UpperCamelCase					):
           _UpperCAmelCase           =   [images]

        if not valid_images(_UpperCamelCase					):
           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.'''					)

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

        if do_resize:
           _UpperCAmelCase           =   [self.resize(image=_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase					) for image in images]

        if do_center_crop:
           _UpperCAmelCase           =   [self.center_crop(image=_UpperCamelCase				,							size=_UpperCamelCase					) for image in images]

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

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

        _UpperCAmelCase           =   [to_channel_dimension_format(_UpperCamelCase				,							_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   {'''pixel_values''': images}
        return BatchFeature(data=_UpperCamelCase				,							tensor_type=_UpperCamelCase					)