


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				List[str]	    =    (DDIMParallelScheduler,)
							__UpperCAmelCase       :				Tuple	    =    (("eta", 0.0), ("num_inference_steps", 50))

							def        __snake_case   (				self			:    int       ,      **lowerCamelCase			:    Optional[Any]							)      ->       Optional[Any]:
														__snake_case						:     Any									=  {
														    "num_train_timesteps": 1000,
														    "beta_start": 0.00_01,
														    "beta_end": 0.02,
														    "beta_schedule": "linear",
														    "clip_sample": True,
														}

														config.update(**lowerCamelCase							)
														return config

							def        __snake_case   (				self			:    Optional[Any]       ,      **lowerCamelCase			:    List[str]							)      ->       Any:
														__snake_case						:     Optional[int]									=  self.scheduler_classes[0]
														__snake_case						:     Optional[int]									=  self.get_scheduler_config(**lowerCamelCase							)
														__snake_case						:     Optional[int]									=  scheduler_class(**lowerCamelCase							)

														__snake_case					,     __snake_case						:     List[Any]									=  10, 0.0

														__snake_case						:     Union[str, Any]									=  self.dummy_model()
														__snake_case						:     Union[str, Any]									=  self.dummy_sample_deter

														scheduler.set_timesteps(lowerCamelCase							)

														for t in scheduler.timesteps:
																					__snake_case						:     List[Any]									=  model(lowerCamelCase       ,      lowerCamelCase							)
																					__snake_case						:     List[str]									=  scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							).prev_sample

														return sample

							def        __snake_case   (				self			:    Tuple							)      ->       Dict:
														for timesteps in [100, 500, 1000]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase							)

							def        __snake_case   (				self			:    Dict							)      ->       int:
														for steps_offset in [0, 1]:
																					self.check_over_configs(steps_offset=lowerCamelCase							)

														__snake_case						:     Optional[int]									=  self.scheduler_classes[0]
														__snake_case						:     Optional[Any]									=  self.get_scheduler_config(steps_offset=1							)
														__snake_case						:     List[str]									=  scheduler_class(**lowerCamelCase							)
														scheduler.set_timesteps(5							)
														assert torch.equal(scheduler.timesteps       ,      torch.LongTensor([801, 601, 401, 201, 1]							)							)

							def        __snake_case   (				self			:    List[Any]							)      ->       int:
														for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,      [0.0_02, 0.02, 0.2, 2]							):
																					self.check_over_configs(beta_start=lowerCamelCase       ,      beta_end=lowerCamelCase							)

							def        __snake_case   (				self			:    List[Any]							)      ->       str:
														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=lowerCamelCase							)

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

							def        __snake_case   (				self			:    Optional[Any]							)      ->       List[Any]:
														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple							)      ->       List[Any]:
														for timestep_spacing in ["trailing", "leading"]:
																					self.check_over_configs(timestep_spacing=lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple							)      ->       Any:
														for rescale_betas_zero_snr in [True, False]:
																					self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase							)

							def        __snake_case   (				self			:    Dict							)      ->       List[str]:
														self.check_over_configs(thresholding=lowerCamelCase							)
														for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "v_prediction"]:
																												self.check_over_configs(
																												    thresholding=lowerCamelCase       ,      prediction_type=lowerCamelCase       ,      sample_max_value=lowerCamelCase       ,      )

							def        __snake_case   (				self			:    int							)      ->       Union[str, Any]:
														for t in [1, 10, 49]:
																					self.check_over_forward(time_step=lowerCamelCase							)

							def        __snake_case   (				self			:    int							)      ->       List[Any]:
														for t, num_inference_steps in zip([1, 10, 50]       ,      [10, 50, 500]							):
																					self.check_over_forward(time_step=lowerCamelCase       ,      num_inference_steps=lowerCamelCase							)

							def        __snake_case   (				self			:    Dict							)      ->       str:
														for t, eta in zip([1, 10, 49]       ,      [0.0, 0.5, 1.0]							):
																					self.check_over_forward(time_step=lowerCamelCase       ,      eta=lowerCamelCase							)

							def        __snake_case   (				self			:    Any							)      ->       Any:
														__snake_case						:     Any									=  self.scheduler_classes[0]
														__snake_case						:     Dict									=  self.get_scheduler_config()
														__snake_case						:     Optional[int]									=  scheduler_class(**lowerCamelCase							)

														assert torch.sum(torch.abs(scheduler._get_variance(0       ,      0							) - 0.0							)							) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(420       ,      400							) - 0.1_47_71							)							) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(980       ,      960							) - 0.3_24_60							)							) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(0       ,      0							) - 0.0							)							) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(487       ,      486							) - 0.0_09_79							)							) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(999       ,      998							) - 0.02							)							) < 1E-5

							def        __snake_case   (				self			:    Dict							)      ->       List[Any]:
														__snake_case						:     Any									=  self.scheduler_classes[0]
														__snake_case						:     Dict									=  self.get_scheduler_config()
														__snake_case						:     Optional[int]									=  scheduler_class(**lowerCamelCase							)

														__snake_case					,     __snake_case						:     str									=  10, 0.0
														scheduler.set_timesteps(lowerCamelCase							)

														__snake_case						:     Dict									=  self.dummy_model()
														__snake_case						:     List[Any]									=  self.dummy_sample_deter
														__snake_case						:     int									=  self.dummy_sample_deter + 0.1
														__snake_case						:     str									=  self.dummy_sample_deter - 0.1

														__snake_case						:     Dict									=  samplea.shape[0]
														__snake_case						:     str									=  torch.stack([samplea, samplea, samplea]       ,      dim=0							)
														__snake_case						:     int									=  torch.arange(lowerCamelCase							)[0:3, None].repeat(1       ,      lowerCamelCase							)

														__snake_case						:     Union[str, Any]									=  model(samples.flatten(0       ,      1							)       ,      timesteps.flatten(0       ,      1							)							)
														__snake_case						:     Any									=  scheduler.batch_step_no_noise(lowerCamelCase       ,      timesteps.flatten(0       ,      1							)       ,      samples.flatten(0       ,      1							)       ,      lowerCamelCase							)

														__snake_case						:     Optional[Any]									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     List[Any]									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 11_47.79_04							) < 1E-2
														assert abs(result_mean.item() - 0.49_82							) < 1E-3

							def        __snake_case   (				self			:    str							)      ->       Optional[int]:
														__snake_case						:     Union[str, Any]									=  self.full_loop()

														__snake_case						:     Dict									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     Dict									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 1_72.00_67							) < 1E-2
														assert abs(result_mean.item() - 0.22_39_67							) < 1E-3

							def        __snake_case   (				self			:    str							)      ->       Dict:
														__snake_case						:     Any									=  self.full_loop(prediction_type="v_prediction"							)

														__snake_case						:     Optional[int]									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     int									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 52.53_02							) < 1E-2
														assert abs(result_mean.item() - 0.06_84							) < 1E-3

							def        __snake_case   (				self			:    Dict							)      ->       Tuple:
														# We specify different beta, so that the first alpha is 0.99
														__snake_case						:     str									=  self.full_loop(set_alpha_to_one=lowerCamelCase       ,      beta_start=0.01							)
														__snake_case						:     Dict									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     Dict									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 1_49.82_95							) < 1E-2
														assert abs(result_mean.item() - 0.19_51							) < 1E-3






							def        __snake_case   (				self			:    Optional[Any]							)      ->       List[Any]:
														# We specify different beta, so that the first alpha is 0.99
														__snake_case						:     Union[str, Any]									=  self.full_loop(set_alpha_to_one=lowerCamelCase       ,      beta_start=0.01							)
														__snake_case						:     Optional[Any]									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     Dict									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 1_49.07_84							) < 1E-2
														assert abs(result_mean.item() - 0.19_41							) < 1E-3








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					)