






"""simple docstring"""



from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
      import torch

if is_tf_available():
      import tensorflow as tf






class 							__SCREAMING_SNAKE_CASE						(				_snake_case		):
       snake_case_     =							['image_processor']
       snake_case_     =							'SamImageProcessor'


       def __init__(			self      :						List[Any]			,    snake_case      :						int							):
             '''simple docstring'''





             super().__init__(lowerCAmelCase__							)
             A__						:  Union[str, Any]		  =  self.image_processor
             A__						:  Dict		  =  -10
             A__						:  List[str]		  =  self.image_processor.size["""longest_edge"""]


       def __call__(			self      :						Dict			,    snake_case      :						Optional[Any]=None			,    snake_case      :						str=None			,    snake_case      :						str=None			,    snake_case      :						Union[str, Any]=None			,    snake_case      :						Optional[Union[str, TensorType]] = None			,    **snake_case      :						Optional[Any]			,    ):
             '''simple docstring'''





             A__						:  Tuple		  =  self.image_processor(
                 lowerCAmelCase__			,    return_tensors=lowerCAmelCase__			,    **lowerCAmelCase__			,    )

             # pop arguments that are not used in the foward but used nevertheless
             A__						:  Any		  =  encoding_image_processor["""original_sizes"""]

             if hasattr(lowerCAmelCase__			,    """numpy"""							):  # Checks if Torch or TF tensor
                   A__						:  Optional[int]		  =  original_sizes.numpy()

             A__  ,  A__  ,  A__						:  Dict		  =  self._check_and_preprocess_points(
                 input_points=lowerCAmelCase__			,    input_labels=lowerCAmelCase__			,    input_boxes=lowerCAmelCase__			,    )

             A__						:  int		  =  self._normalize_and_convert(
                 lowerCAmelCase__			,    lowerCAmelCase__			,    input_points=lowerCAmelCase__			,    input_labels=lowerCAmelCase__			,    input_boxes=lowerCAmelCase__			,    return_tensors=lowerCAmelCase__			,    )

             return encoding_image_processor


       def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						Tuple			,    snake_case      :						Optional[int]			,    snake_case      :						Optional[Any]=None			,    snake_case      :						Tuple=None			,    snake_case      :						Optional[int]=None			,    snake_case      :						Optional[Any]="pt"			,    ):
             '''simple docstring'''





             if input_points is not None:
                   if len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
                         A__						:  Dict		  =  [
                             self._normalize_coordinates(self.target_size			,    lowerCAmelCase__			,    original_sizes[0]							) for point in input_points
                         ]
                   else:
                         A__						:  int		  =  [
                             self._normalize_coordinates(self.target_size			,    lowerCAmelCase__			,    lowerCAmelCase__							)
                             for point, original_size in zip(lowerCAmelCase__			,    lowerCAmelCase__							)
                         ]
                   # check that all arrays have the same shape
                   if not all(point.shape == input_points[0].shape for point in input_points							):
                         if input_labels is not None:
                               A__  ,  A__						:  Optional[Any]		  =  self._pad_points_and_labels(lowerCAmelCase__			,    lowerCAmelCase__							)

                   A__						:  Dict		  =  np.array(lowerCAmelCase__							)

             if input_labels is not None:
                   A__						:  int		  =  np.array(lowerCAmelCase__							)

             if input_boxes is not None:
                   if len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
                         A__						:  Dict		  =  [
                             self._normalize_coordinates(self.target_size			,    lowerCAmelCase__			,    original_sizes[0]			,    is_bounding_box=lowerCAmelCase__							)
                             for box in input_boxes
                         ]
                   else:
                         A__						:  List[Any]		  =  [
                             self._normalize_coordinates(self.target_size			,    lowerCAmelCase__			,    lowerCAmelCase__			,    is_bounding_box=lowerCAmelCase__							)
                             for box, original_size in zip(lowerCAmelCase__			,    lowerCAmelCase__							)
                         ]
                   A__						:  Union[str, Any]		  =  np.array(lowerCAmelCase__							)

             if input_boxes is not None:
                   if return_tensors == "pt":
                         A__						:  List[Any]		  =  torch.from_numpy(lowerCAmelCase__							)
                         # boxes batch size of 1 by default
                         A__						:  int		  =  input_boxes.unsqueeze(1							) if len(input_boxes.shape							) != 3 else input_boxes
                   elif return_tensors == "tf":
                         A__						:  Dict		  =  tf.convert_to_tensor(lowerCAmelCase__							)
                         # boxes batch size of 1 by default
                         A__						:  Tuple		  =  tf.expand_dims(lowerCAmelCase__			,    1							) if len(input_boxes.shape							) != 3 else input_boxes
                   encoding_image_processor.update({"""input_boxes""": input_boxes}							)
             if input_points is not None:
                   if return_tensors == "pt":
                         A__						:  Optional[Any]		  =  torch.from_numpy(lowerCAmelCase__							)
                         # point batch size of 1 by default
                         A__						:  Tuple		  =  input_points.unsqueeze(1							) if len(input_points.shape							) != 4 else input_points
                   elif return_tensors == "tf":
                         A__						:  Tuple		  =  tf.convert_to_tensor(lowerCAmelCase__							)
                         # point batch size of 1 by default
                         A__						:  List[str]		  =  tf.expand_dims(lowerCAmelCase__			,    1							) if len(input_points.shape							) != 4 else input_points
                   encoding_image_processor.update({"""input_points""": input_points}							)
             if input_labels is not None:
                   if return_tensors == "pt":
                         A__						:  str		  =  torch.from_numpy(lowerCAmelCase__							)
                         # point batch size of 1 by default
                         A__						:  Optional[int]		  =  input_labels.unsqueeze(1							) if len(input_labels.shape							) != 3 else input_labels
                   elif return_tensors == "tf":
                         A__						:  Union[str, Any]		  =  tf.convert_to_tensor(lowerCAmelCase__							)
                         # point batch size of 1 by default
                         A__						:  Tuple		  =  tf.expand_dims(lowerCAmelCase__			,    1							) if len(input_labels.shape							) != 3 else input_labels
                   encoding_image_processor.update({"""input_labels""": input_labels}							)

             return encoding_image_processor


       def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						List[Any]			,    snake_case      :						Any							):
             '''simple docstring'''





             A__						:  int		  =  max([point.shape[0] for point in input_points]							)
             A__						:  Union[str, Any]		  =  []
             for i, point in enumerate(lowerCAmelCase__							):
                   if point.shape[0] != expected_nb_points:
                         A__						:  Tuple		  =  np.concatenate(
                             [point, np.zeros((expected_nb_points - point.shape[0], 2)							) + self.point_pad_value]			,    axis=0							)
                         A__						:  List[str]		  =  np.append(input_labels[i]			,    [self.point_pad_value]							)
                   processed_input_points.append(lowerCAmelCase__							)
             A__						:  List[Any]		  =  processed_input_points
             return input_points, input_labels


       def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						int			,    snake_case      :						np.ndarray			,    snake_case      :						Any			,    snake_case      :						Any=False							):
             '''simple docstring'''





             A__  ,  A__						:  Any		  =  original_size
             A__  ,  A__						:  str		  =  self.image_processor._get_preprocess_shape(lowerCAmelCase__			,    longest_edge=lowerCAmelCase__							)
             A__						:  Dict		  =  deepcopy(lowerCAmelCase__							).astype(lowerCAmelCase__							)

             if is_bounding_box:
                   A__						:  int		  =  coords.reshape(-1			,    2			,    2							)

             A__						:  Optional[int]		  =  coords[..., 0] * (new_w / old_w)
             A__						:  Union[str, Any]		  =  coords[..., 1] * (new_h / old_h)

             if is_bounding_box:
                   A__						:  Tuple		  =  coords.reshape(-1			,    4							)

             return coords


       def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						int=None			,    snake_case      :						Any=None			,    snake_case      :						Optional[int]=None			,    ):
             '''simple docstring'''





             if input_points is not None:
                   if hasattr(lowerCAmelCase__			,    """numpy"""							):  # Checks for TF or Torch tensor
                         A__						:  Optional[Any]		  =  input_points.numpy().tolist()

                   if not isinstance(lowerCAmelCase__			,    lowerCAmelCase__							) or not isinstance(input_points[0]			,    lowerCAmelCase__							):
                         raise ValueError("""Input points must be a list of list of floating points."""							)
                   A__						:  Optional[Any]		  =  [np.array(lowerCAmelCase__							) for input_point in input_points]
             else:
                   A__						:  Dict		  =  None

             if input_labels is not None:
                   if hasattr(lowerCAmelCase__			,    """numpy"""							):
                         A__						:  List[str]		  =  input_labels.numpy().tolist()

                   if not isinstance(lowerCAmelCase__			,    lowerCAmelCase__							) or not isinstance(input_labels[0]			,    lowerCAmelCase__							):
                         raise ValueError("""Input labels must be a list of list integers."""							)
                   A__						:  Optional[Any]		  =  [np.array(lowerCAmelCase__							) for label in input_labels]
             else:
                   A__						:  str		  =  None

             if input_boxes is not None:
                   if hasattr(lowerCAmelCase__			,    """numpy"""							):
                         A__						:  Optional[int]		  =  input_boxes.numpy().tolist()

                   if (
                       not isinstance(lowerCAmelCase__			,    lowerCAmelCase__							)
                       or not isinstance(input_boxes[0]			,    lowerCAmelCase__							)
                       or not isinstance(input_boxes[0][0]			,    lowerCAmelCase__							)
                   ):
                         raise ValueError("""Input boxes must be a list of list of list of floating points."""							)
                   A__						:  Optional[int]		  =  [np.array(lowerCAmelCase__							).astype(np.floataa							) for box in input_boxes]
             else:
                   A__						:  str		  =  None

             return input_points, input_labels, input_boxes


       @property
       def 						_UpperCamelCase       (			self      :						Optional[Any]							):
             '''simple docstring'''





             A__						:  Any		  =  self.image_processor.model_input_names
             return list(dict.fromkeys(lowerCAmelCase__							)							)


       def 						_UpperCamelCase       (			self      :						List[str]			,    *snake_case      :						Optional[int]			,    **snake_case      :						Tuple							):
             '''simple docstring'''





             return self.image_processor.post_process_masks(*lowerCAmelCase__			,    **lowerCAmelCase__							)














"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							@property
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													torch.manual_seed(0							)
													A__						:  Optional[Any]		  =  UNetaDModel(
													    block_out_channels=(32, 64)			,    layers_per_block=2			,    sample_size=32			,    in_channels=3			,    out_channels=3			,    down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,    up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,    )
													return model


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  self.dummy_uncond_unet
													A__						:  Dict		  =  ScoreSdeVeScheduler()

													A__						:  str		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Optional[int]		  =  torch.manual_seed(0							)
													A__						:  Any		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  List[str]		  =  torch.manual_seed(0							)
													A__						:  int		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case			,    return_dict=snake_case							)[
													    0
													]

													A__						:  int		  =  image[0, -3:, -3:, -1]
													A__						:  Optional[Any]		  =  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													A__						:  Optional[Any]		  =  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

													assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2






@slow
@require_torch
class 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  """google/ncsnpp-church-256"""
													A__						:  Tuple		  =  UNetaDModel.from_pretrained(snake_case							)

													A__						:  int		  =  ScoreSdeVeScheduler.from_pretrained(snake_case							)

													A__						:  List[Any]		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Dict		  =  torch.manual_seed(0							)
													A__						:  Optional[int]		  =  sde_ve(num_inference_steps=10			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  Optional[int]		  =  image[0, -3:, -3:, -1]

													assert image.shape == (1, 256, 256, 3)

													A__						:  Dict		  =  np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

													assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






