

'''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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowercase__            =     logging.get_logger(__name__)




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      str    						=    ["""pixel_values"""]


     def __init__(						self					:	Optional[int]	,     lowercase_					:	bool = True	,     lowercase_					:	Dict[str, int] = None	,     lowercase_					:	bool = True	,     lowercase_					:	Dict[str, int] = None	,     lowercase_					:	PILImageResampling = PILImageResampling.BICUBIC	,     lowercase_					:	bool = True	,     lowercase_					:	Union[int, float] = 1 / 255	,     lowercase_					:	bool = True	,     lowercase_					:	Optional[Union[float, List[float]]] = None	,     lowercase_					:	Optional[Union[float, List[float]]] = None	,     **lowercase_					:	Optional[int]	,     )		->      None:
      super().__init__(**lowercase_			)
      UpperCAmelCase			:      Tuple		   =					crop_size if crop_size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase			:      Union[str, Any]		   =					get_size_dict(lowercase_	,     param_name='crop_size'			)
      UpperCAmelCase			:      str		   =					size if size is not None else {'height': 224, 'width': 224}
      UpperCAmelCase			:      Tuple		   =					get_size_dict(lowercase_			)

      UpperCAmelCase			:      int		   =					do_center_crop
      UpperCAmelCase			:      str		   =					crop_size
      UpperCAmelCase			:      Any		   =					do_resize
      UpperCAmelCase			:      List[str]		   =					size
      UpperCAmelCase			:      Tuple		   =					resample
      UpperCAmelCase			:      Union[str, Any]		   =					do_rescale
      UpperCAmelCase			:      Optional[Any]		   =					rescale_factor
      UpperCAmelCase			:      Tuple		   =					do_normalize
      UpperCAmelCase			:      Any		   =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      UpperCAmelCase			:      List[Any]		   =					image_std if image_std is not None else IMAGENET_DEFAULT_STD


     def   UpperCAmelCase_			(						self					:	int	,     lowercase_					:	np.ndarray	,     lowercase_					:	Dict[str, int]	,     lowercase_					:	Optional[int] = None	,     lowercase_					:	Optional[Union[str, ChannelDimension]] = None	,     **lowercase_					:	Union[str, Any]	,     )		->      np.ndarray:
      UpperCAmelCase			:      str		   =					self.size if size is None else size
      UpperCAmelCase			:      Dict		   =					get_size_dict(lowercase_			)
      UpperCAmelCase			:      Tuple		   =					get_size_dict(lowercase_	,     param_name='crop_size'			)

      UpperCAmelCase   ,					UpperCAmelCase			:      Dict		   =					get_image_size(lowercase_			)
      UpperCAmelCase			:      Optional[int]		   =					min(lowercase_	,     lowercase_			)
      UpperCAmelCase			:      Any		   =					(size['height'] / crop_size['height']) * min_dim
      UpperCAmelCase			:      Tuple		   =					(size['width'] / crop_size['width']) * min_dim
      return center_crop(lowercase_	,     size=(cropped_height, cropped_width)	,     data_format=lowercase_	,     **lowercase_			)


     def   UpperCAmelCase_			(						self					:	Optional[Any]	,     lowercase_					:	np.ndarray	,     lowercase_					:	Dict[str, int]	,     lowercase_					:	PILImageResampling = PIL.Image.BICUBIC	,     lowercase_					:	Optional[Union[str, ChannelDimension]] = None	,     **lowercase_					:	Optional[Any]	,     )		->      np.ndarray:
      UpperCAmelCase			:      Optional[Any]		   =					get_size_dict(lowercase_			)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""			)
      return resize(
          lowercase_	,     size=(size['height'], size['width'])	,     resample=lowercase_	,     data_format=lowercase_	,     **lowercase_			)


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


     def   UpperCAmelCase_			(						self					:	str	,     lowercase_					:	np.ndarray	,     lowercase_					:	Union[float, List[float]]	,     lowercase_					:	Union[float, List[float]]	,     lowercase_					:	Optional[Union[str, ChannelDimension]] = None	,     **lowercase_					:	Optional[int]	,     )		->      np.ndarray:
      return normalize(lowercase_	,     mean=lowercase_	,     std=lowercase_	,     data_format=lowercase_	,     **lowercase_			)




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

      UpperCAmelCase			:      Union[str, Any]		   =					make_list_of_images(lowercase_			)

      if not valid_images(lowercase_			):
       raise ValueError(
           'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
           'torch.Tensor, tf.Tensor or jax.ndarray.'			)

      if do_center_crop and crop_size is None:
       raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'			)

      if do_resize and size is None:
       raise ValueError('Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'			)

      # All transformations expect numpy arrays.
      UpperCAmelCase			:      Union[str, Any]		   =					[to_numpy_array(lowercase_			) for image in images]

      if do_center_crop:
       UpperCAmelCase			:      Dict		   =					[self.center_crop(lowercase_	,     lowercase_	,     size=lowercase_			) for image in images]

      if do_resize:
       UpperCAmelCase			:      int		   =					[self.resize(image=lowercase_	,     size=lowercase_	,     resample=lowercase_			) for image in images]

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

      if do_normalize:
       UpperCAmelCase			:      List[Any]		   =					[self.normalize(image=lowercase_	,     mean=lowercase_	,     std=lowercase_			) for image in images]

      UpperCAmelCase			:      Union[str, Any]		   =					[to_channel_dimension_format(lowercase_	,     lowercase_			) for image in images]

      UpperCAmelCase			:      Optional[int]		   =					{'pixel_values': images}
      return BatchFeature(data=lowercase_	,     tensor_type=lowercase_			)







'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Dict    						=    (LMSDiscreteScheduler,)
     UpperCAmelCase_		:      Any    						=    10


     def   UpperCAmelCase_			(						self					:	str	,     **lowercase_					:	List[str]			)		->      str:
      UpperCAmelCase			:      Optional[Any]		   =					{
          'num_train_timesteps': 1_100,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**lowercase_			)
      return config


     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      Tuple:
      for timesteps in [10, 50, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=lowercase_			)


     def   UpperCAmelCase_			(						self					:	int			)		->      Tuple:
      for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]			):
       self.check_over_configs(beta_start=lowercase_	,     beta_end=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Dict			)		->      Union[str, Any]:
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Tuple			)		->      List[Any]:
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=lowercase_			)


     def   UpperCAmelCase_			(						self					:	str			)		->      Union[str, Any]:
      for t in [0, 500, 800]:
       self.check_over_forward(time_step=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      List[str]:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Tuple		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Any		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCAmelCase			:      str		   =					self.dummy_model()
      UpperCAmelCase			:      str		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Optional[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Union[str, Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Optional[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Optional[Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3


     def   UpperCAmelCase_			(						self					:	str			)		->      Dict:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Dict		   =					self.get_scheduler_config(prediction_type='v_prediction'			)
      UpperCAmelCase			:      Optional[int]		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCAmelCase			:      Optional[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      List[str]		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      List[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 0.0017			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3


     def   UpperCAmelCase_			(						self					:	Optional[Any]			)		->      Dict:
      UpperCAmelCase			:      Dict		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Optional[int]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      List[str]		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps	,     device=lowercase_			)

      UpperCAmelCase			:      Tuple		   =					self.dummy_model()
      UpperCAmelCase			:      Tuple		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCAmelCase			:      Any		   =					sample.to(lowercase_			)

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Dict		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Tuple		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[Any]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Union[str, Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3




     def   UpperCAmelCase_			(						self					:	Dict			)		->      Optional[Any]:
      UpperCAmelCase			:      int		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Union[str, Any]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Optional[Any]		   =					scheduler_class(**lowercase_	,     use_karras_sigmas=lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps	,     device=lowercase_			)

      UpperCAmelCase			:      List[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      Dict		   =					self.dummy_sample_deter.to(lowercase_			) * scheduler.init_noise_sigma
      UpperCAmelCase			:      Union[str, Any]		   =					sample.to(lowercase_			)

      for t in scheduler.timesteps:
       UpperCAmelCase			:      Optional[int]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[str]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[Any]		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      Any		   =					output.prev_sample

      UpperCAmelCase			:      List[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 3812.9927			) < 2E-2
      assert abs(result_mean.item() - 4.9648			) < 1E-3




