
"""simple docstring"""


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
      import PIL

if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					List[Any]   =			['''pixel_values''']






       def __init__(  self :							List[Any]       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Dict[str, int] = None       ,      UpperCAmelCase_ :							PILImageResampling = PILImageResampling.BILINEAR       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Union[int, float] = 1 / 255       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Dict[str, int] = None       ,      UpperCAmelCase_ :							bool = True       ,      **UpperCAmelCase_ :							str       ,      )->  None:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_			)
              UpperCamelCase					 =		size if size is not None else {"shortest_edge": 224}
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      default_to_square=UpperCAmelCase_			)
              UpperCamelCase					 =		crop_size if crop_size is not None else {"height": 256, "width": 256}
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      param_name="crop_size"			)

              UpperCamelCase					 =		do_resize
              UpperCamelCase					 =		size
              UpperCamelCase					 =		resample
              UpperCamelCase					 =		do_rescale
              UpperCamelCase					 =		rescale_factor
              UpperCamelCase					 =		do_center_crop
              UpperCamelCase					 =		crop_size
              UpperCamelCase					 =		do_flip_channel_order






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Dict[str, int]       ,      UpperCAmelCase_ :							PILImageResampling = PIL.Image.BILINEAR       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							str       ,      )->  np.ndarray:







              """simple docstring"""
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      default_to_square=UpperCAmelCase_			)
              if "shortest_edge" not in size:
                     raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"			)
              UpperCamelCase					 =		get_resize_output_image_size(UpperCAmelCase_       ,      size=size["shortest_edge"]       ,      default_to_square=UpperCAmelCase_			)
              return resize(UpperCAmelCase_       ,      size=UpperCAmelCase_       ,      resample=UpperCAmelCase_       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Dict[str, int]       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							Optional[Any]       ,      )->  np.ndarray:







              """simple docstring"""
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_			)
              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 center_crop(UpperCAmelCase_       ,      size=(size["height"], size["width"])       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Union[int, float]       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							Dict       ,      )->  Optional[Any]:







              """simple docstring"""
              return rescale(UpperCAmelCase_       ,      scale=UpperCAmelCase_       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None			)->  np.ndarray:







              """simple docstring"""
              return flip_channel_order(UpperCAmelCase_       ,      data_format=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							ImageInput       ,      UpperCAmelCase_ :							bool = None       ,      UpperCAmelCase_ :							Dict[str, int] = None       ,      UpperCAmelCase_ :							PILImageResampling = None       ,      UpperCAmelCase_ :							bool = None       ,      UpperCAmelCase_ :							float = None       ,      UpperCAmelCase_ :							bool = None       ,      UpperCAmelCase_ :							Dict[str, int] = None       ,      UpperCAmelCase_ :							bool = None       ,      UpperCAmelCase_ :							Optional[Union[str, TensorType]] = None       ,      UpperCAmelCase_ :							ChannelDimension = ChannelDimension.FIRST       ,      **UpperCAmelCase_ :							str       ,      )->  PIL.Image.Image:







              """simple docstring"""
              UpperCamelCase					 =		do_resize if do_resize is not None else self.do_resize
              UpperCamelCase					 =		resample if resample is not None else self.resample
              UpperCamelCase					 =		do_rescale if do_rescale is not None else self.do_rescale
              UpperCamelCase					 =		rescale_factor if rescale_factor is not None else self.rescale_factor
              UpperCamelCase					 =		do_center_crop if do_center_crop is not None else self.do_center_crop
              UpperCamelCase					 =		(
                  do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
              )

              UpperCamelCase					 =		size if size is not None else self.size
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      default_to_square=UpperCAmelCase_			)
              UpperCamelCase					 =		crop_size if crop_size is not None else self.crop_size
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      param_name="crop_size"			)

              UpperCamelCase					 =		make_list_of_images(UpperCAmelCase_			)

              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_rescale and rescale_factor is None:
                     raise ValueError("Rescale factor must be specified if do_rescale is True."			)

              if do_center_crop and crop_size is None:
                     raise ValueError("Crop size must be specified if do_center_crop 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]

              # the pretrained checkpoints assume images are BGR, not RGB
              if do_flip_channel_order:
                     UpperCamelCase					 =		[self.flip_channel_order(image=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_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							Optional[Any]       ,      UpperCAmelCase_ :							List[Tuple] = None			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		outputs.logits

              # Resize logits and compute semantic segmentation maps
              if target_sizes is not None:
                     if len(UpperCAmelCase_			) != len(UpperCAmelCase_			):
                            raise ValueError(
                                "Make sure that you pass in as many target sizes as the batch dimension of the logits"			)

                     if is_torch_tensor(UpperCAmelCase_			):
                            UpperCamelCase					 =		target_sizes.numpy()

                     UpperCamelCase					 =		[]

                     for idx in range(len(UpperCAmelCase_			)			):
                            UpperCamelCase					 =		torch.nn.functional.interpolate(
                                logits[idx].unsqueeze(dim=0			)       ,      size=target_sizes[idx]       ,      mode="bilinear"       ,      align_corners=UpperCAmelCase_			)
                            UpperCamelCase					 =		resized_logits[0].argmax(dim=0			)
                            semantic_segmentation.append(UpperCAmelCase_			)
              else:
                     UpperCamelCase					 =		logits.argmax(dim=1			)
                     UpperCamelCase					 =		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]			)]

              return semantic_segmentation







"""simple docstring"""


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Any   =			(EulerDiscreteScheduler,)
       UpperCamelCase_		:					Dict   =			10






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      **UpperCAmelCase_ :							str			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		{
                  "num_train_timesteps": 1_100,
                  "beta_start": 0.0001,
                  "beta_end": 0.02,
                  "beta_schedule": "linear",
              }

              config.update(**UpperCAmelCase_			)
              return config






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







              """simple docstring"""
              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Any:







              """simple docstring"""
              for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,      [0.0002, 0.002, 0.02]			):
                     self.check_over_configs(beta_start=UpperCAmelCase_       ,      beta_end=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Dict:







              """simple docstring"""
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  int:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config()
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_			)

              scheduler.set_timesteps(self.num_inference_steps			)

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for i, t in enumerate(scheduler.timesteps			):
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 10.0807			) < 1e-2
              assert abs(result_mean.item() - 0.0131			) < 1e-3






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







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config(prediction_type="v_prediction"			)
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_			)

              scheduler.set_timesteps(self.num_inference_steps			)

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for i, t in enumerate(scheduler.timesteps			):
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 0.0002			) < 1e-2
              assert abs(result_mean.item() - 2.2_676e-06			) < 1e-3






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config()
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_			)

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

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for t in scheduler.timesteps:
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 10.0807			) < 1e-2
              assert abs(result_mean.item() - 0.0131			) < 1e-3






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config()
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_       ,      use_karras_sigmas=UpperCAmelCase_			)

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

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for t in scheduler.timesteps:
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 124.52299499511719			) < 1e-2
              assert abs(result_mean.item() - 0.16213932633399963			) < 1e-3





