
"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 		a__						(      a_						):
   __lowerCAmelCase            =				(DDPMScheduler,)
   def 			__magic_name__			(			self	,    **_a				):
          lowercase    :				str       		=							{
              "num_train_timesteps": 1_000,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
              "variance_type": "fixed_small",
              "clip_sample": True,
          }

          config.update(**_a				)
          return config
   def 			__magic_name__			(			self				):
          for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a				)
   def 			__magic_name__			(			self				):
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]	,    [0.0_0_2, 0.0_2, 0.2, 2]				):
                 self.check_over_configs(beta_start=_a	,    beta_end=_a				)
   def 			__magic_name__			(			self				):
          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a				)
   def 			__magic_name__			(			self				):
          for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a				)
   def 			__magic_name__			(			self				):
          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a				)
   def 			__magic_name__			(			self				):
          self.check_over_configs(thresholding=_a				)
          for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=_a	,    prediction_type=_a	,    sample_max_value=_a	,    )
   def 			__magic_name__			(			self				):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a				)
   def 			__magic_name__			(			self				):
          for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							self.scheduler_classes[0]
          lowercase    :				Optional[Any]       		=							self.get_scheduler_config()
          lowercase    :				str       		=							scheduler_class(**_a				)

          assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_0_9_7_9				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.0_2				)				) < 1E-5
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.scheduler_classes[0]
          lowercase    :				Any       		=							self.get_scheduler_config()
          lowercase    :				List[str]       		=							scheduler_class(**_a				)

          lowercase    :				Optional[int]       		=							len(_a				)

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

          for t in reversed(range(_a				)				):
                 # 1. predict noise residual
                 lowercase    :				int       		=							model(_a	,    _a				)

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

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase    :				Dict       		=							pred_prev_sample

          lowercase    :				Any       		=							torch.sum(torch.abs(_a				)				)
          lowercase    :				Any       		=							torch.mean(torch.abs(_a				)				)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6				) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2				) < 1E-3
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.scheduler_classes[0]
          lowercase    :				Dict       		=							self.get_scheduler_config(prediction_type="v_prediction"				)
          lowercase    :				Dict       		=							scheduler_class(**_a				)

          lowercase    :				Optional[int]       		=							len(_a				)

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

          for t in reversed(range(_a				)				):
                 # 1. predict noise residual
                 lowercase    :				int       		=							model(_a	,    _a				)

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

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase    :				Tuple       		=							pred_prev_sample

          lowercase    :				List[str]       		=							torch.sum(torch.abs(_a				)				)
          lowercase    :				List[Any]       		=							torch.mean(torch.abs(_a				)				)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6				) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1				) < 1E-3
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.scheduler_classes[0]
          lowercase    :				List[Any]       		=							self.get_scheduler_config()
          lowercase    :				Optional[Any]       		=							scheduler_class(**_a				)

          lowercase    :				Any       		=							[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_a				)

          lowercase    :				Union[str, Any]       		=							scheduler.timesteps

          for i, timestep in enumerate(_a				):
                 if i == len(_a				) - 1:
                        lowercase    :				Dict       		=							-1
                 else:
                        lowercase    :				List[str]       		=							timesteps[i + 1]

                 lowercase    :				List[Any]       		=							scheduler.previous_timestep(_a				)
                 lowercase    :				Tuple       		=							prev_t.item()

                 self.assertEqual(_a	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.scheduler_classes[0]
          lowercase    :				List[str]       		=							self.get_scheduler_config()
          lowercase    :				List[Any]       		=							scheduler_class(**_a				)

          lowercase    :				Union[str, Any]       		=							[100, 87, 50, 51, 0]

          with self.assertRaises(_a	,    msg="`custom_timesteps` must be in descending order."				):
                 scheduler.set_timesteps(timesteps=_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.scheduler_classes[0]
          lowercase    :				Optional[int]       		=							self.get_scheduler_config()
          lowercase    :				str       		=							scheduler_class(**_a				)

          lowercase    :				Optional[Any]       		=							[100, 87, 50, 1, 0]
          lowercase    :				List[Any]       		=							len(_a				)

          with self.assertRaises(_a	,    msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."				):
                 scheduler.set_timesteps(num_inference_steps=_a	,    timesteps=_a				)

   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							self.scheduler_classes[0]
          lowercase    :				List[str]       		=							self.get_scheduler_config()
          lowercase    :				List[Any]       		=							scheduler_class(**_a				)

          lowercase    :				Optional[int]       		=							[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              _a	,    msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"	,    ):
                 scheduler.set_timesteps(timesteps=_a				)




"""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


_A			:       List[Any]   		=       logging.get_logger(__name__)

class 		a__						(      a_						):
   __lowerCAmelCase            =				["""pixel_values"""]
   def __init__(			self	,    _a = True	,    _a = None	,    _a = PILImageResampling.BILINEAR	,    _a = True	,    _a = 1 / 255	,    _a = True	,    _a = None	,    _a = True	,    **_a	,    ):
          super().__init__(**_a				)
          lowercase    :				Optional[Any]       		=							size if size is not None else {"shortest_edge": 224}
          lowercase    :				List[Any]       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				str       		=							crop_size if crop_size is not None else {"height": 256, "width": 256}
          lowercase    :				List[str]       		=							get_size_dict(_a	,    param_name="crop_size"				)

          lowercase    :				int       		=							do_resize
          lowercase    :				Optional[int]       		=							size
          lowercase    :				str       		=							resample
          lowercase    :				List[Any]       		=							do_rescale
          lowercase    :				Union[str, Any]       		=							rescale_factor
          lowercase    :				Optional[int]       		=							do_center_crop
          lowercase    :				Union[str, Any]       		=							crop_size
          lowercase    :				Optional[Any]       		=							do_flip_channel_order
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = PIL.Image.BILINEAR	,    _a = None	,    **_a	,    ):
          lowercase    :				List[Any]       		=							get_size_dict(_a	,    default_to_square=_a				)
          if "shortest_edge" not in size:
                 raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
          lowercase    :				Union[str, Any]       		=							get_resize_output_image_size(_a	,    size=size["shortest_edge"]	,    default_to_square=_a				)
          return resize(_a	,    size=_a	,    resample=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          lowercase    :				str       		=							get_size_dict(_a				)
          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(_a	,    size=(size["height"], size["width"])	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          return rescale(_a	,    scale=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          return flip_channel_order(_a	,    data_format=_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = ChannelDimension.FIRST	,    **_a	,    ):
          lowercase    :				Optional[Any]       		=							do_resize if do_resize is not None else self.do_resize
          lowercase    :				Tuple       		=							resample if resample is not None else self.resample
          lowercase    :				List[str]       		=							do_rescale if do_rescale is not None else self.do_rescale
          lowercase    :				List[Any]       		=							rescale_factor if rescale_factor is not None else self.rescale_factor
          lowercase    :				Union[str, Any]       		=							do_center_crop if do_center_crop is not None else self.do_center_crop
          lowercase    :				Optional[int]       		=							(
              do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
          )

          lowercase    :				str       		=							size if size is not None else self.size
          lowercase    :				Any       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				int       		=							crop_size if crop_size is not None else self.crop_size
          lowercase    :				Any       		=							get_size_dict(_a	,    param_name="crop_size"				)

          lowercase    :				int       		=							make_list_of_images(_a				)

          if not valid_images(_a				):
                 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.
          lowercase    :				Any       		=							[to_numpy_array(_a				) for image in images]

          if do_resize:
                 lowercase    :				Optional[int]       		=							[self.resize(image=_a	,    size=_a	,    resample=_a				) for image in images]

          if do_center_crop:
                 lowercase    :				str       		=							[self.center_crop(image=_a	,    size=_a				) for image in images]

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

          # the pretrained checkpoints assume images are BGR, not RGB
          if do_flip_channel_order:
                 lowercase    :				int       		=							[self.flip_channel_order(image=_a				) for image in images]

          lowercase    :				int       		=							[to_channel_dimension_format(_a	,    _a				) for image in images]

          lowercase    :				Optional[Any]       		=							{"pixel_values": images}
          return BatchFeature(data=_a	,    tensor_type=_a				)

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Optional[int]       		=							outputs.logits

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

                 if is_torch_tensor(_a				):
                        lowercase    :				Tuple       		=							target_sizes.numpy()

                 lowercase    :				List[Any]       		=							[]

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

          return semantic_segmentation


