


"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
     import torch


lowerCAmelCase__				    =       logging.get_logger(__name__)


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Dict       =["pixel_values"]

       def __init__(						self		,					snake_case__ = True		,					snake_case__ = None		,					snake_case__ = PILImageResampling.BILINEAR		,					snake_case__ = True		,					snake_case__ = None		,					snake_case__ = True		,					snake_case__ = 1 / 255		,					snake_case__ = True		,					snake_case__ = None		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(**snake_case__						)
         lowerCAmelCase					:					str								=							size if size is not None else {"shortest_edge": 256}
         lowerCAmelCase					:					Tuple								=							get_size_dict(snake_case__		,					default_to_square=snake_case__						)
         lowerCAmelCase					:					Dict								=							crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowerCAmelCase					:					Dict								=							get_size_dict(snake_case__		,					param_name="crop_size"						)
         lowerCAmelCase					:					Any								=							do_resize
         lowerCAmelCase					:					Dict								=							size
         lowerCAmelCase					:					List[str]								=							resample
         lowerCAmelCase					:					Dict								=							do_center_crop
         lowerCAmelCase					:					Dict								=							crop_size
         lowerCAmelCase					:					Optional[Any]								=							do_rescale
         lowerCAmelCase					:					int								=							rescale_factor
         lowerCAmelCase					:					str								=							do_normalize
         lowerCAmelCase					:					Union[str, Any]								=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowerCAmelCase					:					Optional[Any]								=							image_std if image_std is not None else IMAGENET_STANDARD_STD

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = PILImageResampling.BICUBIC		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					int								=							get_size_dict(snake_case__		,					default_to_square=snake_case__						)
         if "shortest_edge" not in size:
           raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""						)
         lowerCAmelCase					:					Optional[int]								=							get_resize_output_image_size(snake_case__		,					size=size["shortest_edge"]		,					default_to_square=snake_case__						)
         return resize(snake_case__		,					size=snake_case__		,					resample=snake_case__		,					data_format=snake_case__		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					Union[str, Any]								=							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` and `width`. Got {size.keys()}"""						)
         return center_crop(snake_case__		,					size=(size["height"], size["width"])		,					data_format=snake_case__		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					**snake_case__						):






         """simple docstring"""





         return rescale(snake_case__		,					scale=snake_case__		,					data_format=snake_case__		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         return normalize(snake_case__		,					mean=snake_case__		,					std=snake_case__		,					data_format=snake_case__		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = ChannelDimension.FIRST		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase					:					Dict								=							size if size is not None else self.size
         lowerCAmelCase					:					int								=							get_size_dict(snake_case__		,					default_to_square=snake_case__						)
         lowerCAmelCase					:					Dict								=							resample if resample is not None else self.resample
         lowerCAmelCase					:					Dict								=							do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCAmelCase					:					Optional[int]								=							crop_size if crop_size is not None else self.crop_size
         lowerCAmelCase					:					str								=							get_size_dict(snake_case__		,					param_name="crop_size"						)
         lowerCAmelCase					:					int								=							do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase					:					Optional[Any]								=							rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase					:					Optional[Any]								=							do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase					:					int								=							image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase					:					Optional[int]								=							image_std if image_std is not None else self.image_std

         lowerCAmelCase					:					List[str]								=							make_list_of_images(snake_case__						)

         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."						)

         if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."						)

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

         if do_resize:
           lowerCAmelCase					:					Union[str, Any]								=							[self.resize(image=snake_case__		,					size=snake_case__		,					resample=snake_case__						) for image in images]

         if do_center_crop:
           lowerCAmelCase					:					Any								=							[self.center_crop(image=snake_case__		,					size=snake_case__						) for image in images]

         if do_rescale:
           lowerCAmelCase					:					Union[str, Any]								=							[self.rescale(image=snake_case__		,					scale=snake_case__						) for image in images]

         if do_normalize:
           lowerCAmelCase					:					str								=							[self.normalize(image=snake_case__		,					mean=snake_case__		,					std=snake_case__						) for image in images]

         lowerCAmelCase					:					Optional[int]								=							[to_channel_dimension_format(snake_case__		,					snake_case__						) for image in images]

         lowerCAmelCase					:					Optional[int]								=							{"pixel_values": images}
         return BatchFeature(data=snake_case__		,					tensor_type=snake_case__						)







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							outputs.logits

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

           if is_torch_tensor(snake_case__						):
             lowerCAmelCase					:					int								=							target_sizes.numpy()

           lowerCAmelCase					:					Dict								=							[]

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

         return semantic_segmentation









"""simple docstring"""







import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Optional[int]       =(DEISMultistepScheduler,)
       a  :    str       =(("num_inference_steps", 25),)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							{
             "num_train_timesteps": 1_000,
             "beta_start": 0.0001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "solver_order": 2,
         }

         config.update(**snake_case__						)
         return config

       def 	lowercase__							(						self		,					snake_case__=0		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase					:					List[str]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)
         lowerCAmelCase					:					List[str]								=							self.dummy_sample
         lowerCAmelCase					:					int								=							0.1 * sample
         lowerCAmelCase					:					Tuple								=							[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					Any								=							self.get_scheduler_config(**snake_case__						)
           lowerCAmelCase					:					List[str]								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)
           # copy over dummy past residuals
           lowerCAmelCase					:					str								=							dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(snake_case__						)
             lowerCAmelCase					:					List[str]								=							scheduler_class.from_pretrained(snake_case__						)
             new_scheduler.set_timesteps(snake_case__						)
             # copy over dummy past residuals
             lowerCAmelCase					:					Optional[int]								=							dummy_past_residuals[: new_scheduler.config.solver_order]

           lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							sample, sample
           for t in range(snake_case__		,					time_step + scheduler.config.solver_order + 1						):
             lowerCAmelCase					:					Any								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
             lowerCAmelCase					:					str								=							new_scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

             assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       def 	lowercase__							(						self		,					snake_case__=0		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase					:					List[str]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)
         lowerCAmelCase					:					List[str]								=							self.dummy_sample
         lowerCAmelCase					:					Optional[int]								=							0.1 * sample
         lowerCAmelCase					:					List[Any]								=							[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					int								=							self.get_scheduler_config()
           lowerCAmelCase					:					Any								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)

           # copy over dummy past residuals (must be after setting timesteps)
           lowerCAmelCase					:					Union[str, Any]								=							dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(snake_case__						)
             lowerCAmelCase					:					Any								=							scheduler_class.from_pretrained(snake_case__						)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(snake_case__						)

             # copy over dummy past residual (must be after setting timesteps)
             lowerCAmelCase					:					Optional[int]								=							dummy_past_residuals[: new_scheduler.config.solver_order]

           lowerCAmelCase					:					Union[str, Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
           lowerCAmelCase					:					int								=							new_scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

           assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

       def 	lowercase__							(						self		,					snake_case__=None		,					**snake_case__						):






         """simple docstring"""





         if scheduler is None:
           lowerCAmelCase					:					List[str]								=							self.scheduler_classes[0]
           lowerCAmelCase					:					Optional[int]								=							self.get_scheduler_config(**snake_case__						)
           lowerCAmelCase					:					Any								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					List[str]								=							self.scheduler_classes[0]
         lowerCAmelCase					:					Tuple								=							self.get_scheduler_config(**snake_case__						)
         lowerCAmelCase					:					List[str]								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					int								=							10
         lowerCAmelCase					:					List[str]								=							self.dummy_model()
         lowerCAmelCase					:					List[Any]								=							self.dummy_sample_deter
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase					:					Any								=							model(snake_case__		,					snake_case__						)
           lowerCAmelCase					:					List[Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__						).prev_sample

         return sample

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							dict(self.forward_default_kwargs						)

         lowerCAmelCase					:					Optional[Any]								=							kwargs.pop("num_inference_steps"		,					snake_case__						)

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase					:					Dict								=							self.get_scheduler_config()
           lowerCAmelCase					:					List[Any]								=							scheduler_class(**snake_case__						)

           lowerCAmelCase					:					int								=							self.dummy_sample
           lowerCAmelCase					:					int								=							0.1 * sample

           if num_inference_steps is not None and hasattr(snake_case__		,					"set_timesteps"						):
             scheduler.set_timesteps(snake_case__						)
           elif num_inference_steps is not None and not hasattr(snake_case__		,					"set_timesteps"						):
             lowerCAmelCase					:					List[str]								=							num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           lowerCAmelCase					:					int								=							[residual + 0.2, residual + 0.15, residual + 0.10]
           lowerCAmelCase					:					Tuple								=							dummy_past_residuals[: scheduler.config.solver_order]

           lowerCAmelCase					:					int								=							scheduler.timesteps[5]
           lowerCAmelCase					:					str								=							scheduler.timesteps[6]

           lowerCAmelCase					:					str								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample
           lowerCAmelCase					:					Union[str, Any]								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

           self.assertEqual(output_a.shape		,					sample.shape						)
           self.assertEqual(output_a.shape		,					output_a.shape						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							DEISMultistepScheduler(**self.get_scheduler_config()						)
         lowerCAmelCase					:					Dict								=							self.full_loop(scheduler=snake_case__						)
         lowerCAmelCase					:					Optional[Any]								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

         lowerCAmelCase					:					List[str]								=							DPMSolverSinglestepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					Any								=							DPMSolverMultistepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					str								=							UniPCMultistepScheduler.from_config(scheduler.config						)
         lowerCAmelCase					:					Union[str, Any]								=							DEISMultistepScheduler.from_config(scheduler.config						)

         lowerCAmelCase					:					Union[str, Any]								=							self.full_loop(scheduler=snake_case__						)
         lowerCAmelCase					:					str								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

       def 	lowercase__							(						self						):






         """simple docstring"""





         for timesteps in [25, 50, 100, 999, 1_000]:
           self.check_over_configs(num_train_timesteps=snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.check_over_configs(thresholding=snake_case__						)
         for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
             for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=snake_case__		,					prediction_type=snake_case__		,					sample_max_value=snake_case__		,					algorithm_type="deis"		,					solver_order=snake_case__		,					solver_type=snake_case__		,					)

       def 	lowercase__							(						self						):






         """simple docstring"""





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

       def 	lowercase__							(						self						):






         """simple docstring"""





         for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
             for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=snake_case__		,					solver_type=snake_case__		,					prediction_type=snake_case__		,					algorithm_type=snake_case__		,					)
                 lowerCAmelCase					:					Any								=							self.full_loop(
                     solver_order=snake_case__		,					solver_type=snake_case__		,					prediction_type=snake_case__		,					algorithm_type=snake_case__		,					)
                 assert not torch.isnan(snake_case__						).any(), "Samples have nan numbers"

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.check_over_configs(lower_order_final=snake_case__						)
         self.check_over_configs(lower_order_final=snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
           self.check_over_forward(num_inference_steps=snake_case__		,					time_step=0						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							self.full_loop()
         lowerCAmelCase					:					Dict								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.23916						) < 1e-3

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.full_loop(prediction_type="v_prediction"						)
         lowerCAmelCase					:					List[Any]								=							torch.mean(torch.abs(snake_case__						)						)

         assert abs(result_mean.item() - 0.091						) < 1e-3







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							self.scheduler_classes[0]
         lowerCAmelCase					:					str								=							self.get_scheduler_config(thresholding=snake_case__		,					dynamic_thresholding_ratio=0						)
         lowerCAmelCase					:					Optional[Any]								=							scheduler_class(**snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							10
         lowerCAmelCase					:					Tuple								=							self.dummy_model()
         lowerCAmelCase					:					List[str]								=							self.dummy_sample_deter.half()
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase					:					Union[str, Any]								=							model(snake_case__		,					snake_case__						)
           lowerCAmelCase					:					Tuple								=							scheduler.step(snake_case__		,					snake_case__		,					snake_case__						).prev_sample

         assert sample.dtype == torch.floataa





