


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= (UnCLIPScheduler,)


     def  _lowerCamelCase (						self ,						**__lowerCamelCase)			->				Any:
            _A      :       Dict				    = {
                "num_train_timesteps": 1_0_0_0,
                "variance_type": "fixed_small_log",
                "clip_sample": True,
                "clip_sample_range": 1.0,
                "prediction_type": "epsilon",
            }

            config.update(**__lowerCamelCase)
            return config


     def  _lowerCamelCase (						self)			->				Dict:
            for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                   self.check_over_configs(num_train_timesteps=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				str:
            for variance in ["fixed_small_log", "learned_range"]:
                   self.check_over_configs(variance_type=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Any:
            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				int:
            for clip_sample_range in [1, 5, 1_0, 2_0]:
                   self.check_over_configs(clip_sample_range=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[Any]:
            for prediction_type in ["epsilon", "sample"]:
                   self.check_over_configs(prediction_type=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Dict:
            for time_step in [0, 5_0_0, 9_9_9]:
                   for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
                          if prev_timestep is not None and prev_timestep >= time_step:
                                 continue

                          self.check_over_forward(time_step=__lowerCamelCase ,						prev_timestep=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       List[Any]				    = self.scheduler_classes[0]
            _A      :       Dict				    = self.get_scheduler_config(variance_type="fixed_small_log")
            _A      :       Optional[Any]				    = scheduler_class(**__lowerCamelCase)

            assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.00_00e-10)) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_8_7) - 0.0_5_4_9_6_2_5)) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.9_9_9_4_9_8_7)) < 1e-5


     def  _lowerCamelCase (						self)			->				str:
            _A      :       List[Any]				    = self.scheduler_classes[0]
            _A      :       Union[str, Any]				    = self.get_scheduler_config(variance_type="learned_range")
            _A      :       List[str]				    = scheduler_class(**__lowerCamelCase)

            _A      :       Any				    = 0.5

            assert scheduler._get_variance(1 ,						predicted_variance=__lowerCamelCase) - -1_0.1_7_1_2_7_9_0 < 1e-5
            assert scheduler._get_variance(4_8_7 ,						predicted_variance=__lowerCamelCase) - -5.7_9_9_8_0_5_2 < 1e-5
            assert scheduler._get_variance(9_9_9 ,						predicted_variance=__lowerCamelCase) - -0.0_0_1_0_0_1_1 < 1e-5


     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       List[str]				    = self.scheduler_classes[0]
            _A      :       Dict				    = self.get_scheduler_config()
            _A      :       List[str]				    = scheduler_class(**__lowerCamelCase)

            _A      :       Optional[int]				    = scheduler.timesteps

            _A      :       Union[str, Any]				    = self.dummy_model()
            _A      :       List[Any]				    = self.dummy_sample_deter
            _A      :       Any				    = torch.manual_seed(0)

            for i, t in enumerate(__lowerCamelCase):
                   # 1. predict noise residual
                   _A      :       Optional[Any]				    = model(__lowerCamelCase ,						__lowerCamelCase)

                   # 2. predict previous mean of sample x_t-1
                   _A      :       Optional[int]				    = scheduler.step(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						generator=__lowerCamelCase).prev_sample

                   _A      :       List[Any]				    = pred_prev_sample

            _A      :       str				    = torch.sum(torch.abs(__lowerCamelCase))
            _A      :       Optional[Any]				    = torch.mean(torch.abs(__lowerCamelCase))

            assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5) < 1e-2
            assert abs(result_mean.item() - 0.3_2_8_4_7_4_3) < 1e-3


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       Optional[Any]				    = self.scheduler_classes[0]
            _A      :       Union[str, Any]				    = self.get_scheduler_config()
            _A      :       Dict				    = scheduler_class(**__lowerCamelCase)

            scheduler.set_timesteps(2_5)

            _A      :       Dict				    = scheduler.timesteps

            _A      :       List[Any]				    = self.dummy_model()
            _A      :       Optional[int]				    = self.dummy_sample_deter
            _A      :       str				    = torch.manual_seed(0)

            for i, t in enumerate(__lowerCamelCase):
                   # 1. predict noise residual
                   _A      :       Optional[int]				    = model(__lowerCamelCase ,						__lowerCamelCase)

                   if i + 1 == timesteps.shape[0]:
                          _A      :       List[str]				    = None
                   else:
                          _A      :       int				    = timesteps[i + 1]

                   # 2. predict previous mean of sample x_t-1
                   _A      :       Any				    = scheduler.step(
                       __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						prev_timestep=__lowerCamelCase ,						generator=__lowerCamelCase).prev_sample

                   _A      :       Dict				    = pred_prev_sample

            _A      :       Dict				    = torch.sum(torch.abs(__lowerCamelCase))
            _A      :       Any				    = torch.mean(torch.abs(__lowerCamelCase))

            assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3) < 1e-2
            assert abs(result_mean.item() - 0.3_3_6_2_0_3_8) < 1e-3


     def  _lowerCamelCase (						self)			->				str:
            pass


     def  _lowerCamelCase (						self)			->				int:
            pass




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


snake_case							:       Union[str, Any]     			=      logging.get_logger(__name__)







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    List[Any]          =			['''pixel_values''']
					def __init__(      self   :Optional[int]			,__snake_case   :bool = True			,__snake_case   :Optional[Dict[str, int]] = None			,__snake_case   :PILImageResampling = PILImageResampling.BICUBIC			,__snake_case   :bool = True			,__snake_case   :bool = True			,__snake_case   :Union[int, float] = 1 / 2_55			,__snake_case   :Dict[str, int] = None			,__snake_case   :bool = True			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,**__snake_case   :Dict			,)					->    None:
												super().__init__(**__snake_case      )
												a__             =     size if size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case      )
												a__             =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case			,default_to_square=__snake_case			,param_name='crop_size'      )

												a__             =     do_resize
												a__             =     do_rescale
												a__             =     do_normalize
												a__             =     do_center_crop
												a__             =     crop_size
												a__             =     size
												a__             =     resample
												a__             =     rescale_factor
												a__             =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												a__             =     image_std if image_std is not None else IMAGENET_DEFAULT_STD
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :PILImageResampling = PILImageResampling.BILINEAR			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :List[Any]			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )

												if "shortest_edge" in size:
																			a__             =     get_resize_output_image_size(__snake_case			,size=size['shortest_edge']			,default_to_square=__snake_case      )
																			# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
												elif "height" in size and "width" in size:
																			a__             =     (size['height'], size['width'])
												else:
																			raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'      )
												return resize(__snake_case			,size=__snake_case			,resample=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )
												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(__snake_case			,size=(size['height'], size['width'])			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :np.ndarray			,__snake_case   :float			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :int      )					->    np.ndarray:
												return rescale(__snake_case			,scale=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :np.ndarray			,__snake_case   :Union[float, List[float]]			,__snake_case   :Union[float, List[float]]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												return normalize(__snake_case			,mean=__snake_case			,std=__snake_case			,data_format=__snake_case			,**__snake_case      )



					def  lowerCamelCase__(      self   :Any			,__snake_case   :ImageInput			,__snake_case   :Optional[bool] = None			,__snake_case   :Dict[str, int] = None			,__snake_case   :PILImageResampling = None			,__snake_case   :bool = None			,__snake_case   :int = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[float] = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[str, TensorType]] = None			,__snake_case   :Union[str, ChannelDimension] = ChannelDimension.FIRST			,**__snake_case   :Optional[int]			,)					->    BatchFeature:
												a__             =     do_resize if do_resize is not None else self.do_resize
												a__             =     do_rescale if do_rescale is not None else self.do_rescale
												a__             =     do_normalize if do_normalize is not None else self.do_normalize
												a__             =     do_center_crop if do_center_crop is not None else self.do_center_crop
												a__             =     crop_size if crop_size is not None else self.crop_size
												a__             =     get_size_dict(__snake_case			,param_name='crop_size'			,default_to_square=__snake_case      )
												a__             =     resample if resample is not None else self.resample
												a__             =     rescale_factor if rescale_factor is not None else self.rescale_factor
												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(__snake_case      )

												if not is_batched(__snake_case      ):
																			a__             =     [images]

												if not valid_images(__snake_case      ):
																			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.
												a__             =     [to_numpy_array(__snake_case      ) for image in images]

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

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

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

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

												a__             =     [to_channel_dimension_format(__snake_case			,__snake_case      ) for image in images]

												a__             =     {'pixel_values': images}
												return BatchFeature(data=__snake_case			,tensor_type=__snake_case      )




