





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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
 import torch

if is_vision_available():
 import PIL


_lowerCAmelCase				:   int						       =						logging.get_logger(__name__)

def   __snake_case    (       _lowerCAmelCase			:			np.ndarray					,					_lowerCAmelCase			:			Union[int, Iterable[int]]					,					_lowerCAmelCase			:			bool					,					_lowerCAmelCase			:			int						)			->    Tuple[int, int]:
   def constraint_to_multiple_of(_lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			Optional[Any]=0					,					_lowerCAmelCase			:			List[Any]=None						):
      A_       :					List[Any]        =      round(val / multiple						) * multiple

      if max_val is not None and x > max_val:
         A_       :					Optional[int]        =      math.floor(val / multiple						) * multiple

      if x < min_val:
         A_       :					Dict        =      math.ceil(val / multiple						) * multiple

      return x

   A_       :					Dict        =      (output_size, output_size) if isinstance(_lowerCAmelCase					,					_lowerCAmelCase						) else output_size

   A_   ,    A_       :					Optional[Any]        =      get_image_size(_lowerCAmelCase						)
   A_   ,    A_       :					Optional[int]        =      output_size

   # determine new height and width
   A_       :					Optional[Any]        =      output_height / input_height
   A_       :					int        =      output_width / input_width

   if keep_aspect_ratio:
      # scale as little as possible
      if abs(1 - scale_width						) < abs(1 - scale_height						):
         # fit width
         A_       :					str        =      scale_width
      else:
         # fit height
         A_       :					List[Any]        =      scale_height

   A_       :					Optional[int]        =      constraint_to_multiple_of(scale_height * input_height					,					multiple=_lowerCAmelCase						)
   A_       :					Dict        =      constraint_to_multiple_of(scale_width * input_width					,					multiple=_lowerCAmelCase						)

   return (new_height, new_width)





class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  ['''pixel_values''']



     def __init__(				self							:Dict      ,   snake_case							:bool = True      ,   snake_case							:Dict[str, int] = None      ,   snake_case							:PILImageResampling = PILImageResampling.BILINEAR      ,   snake_case							:bool = False      ,   snake_case							:int = 1      ,   snake_case							:bool = True      ,   snake_case							:Union[int, float] = 1 / 255      ,   snake_case							:bool = True      ,   snake_case							:Optional[Union[float, List[float]]] = None      ,   snake_case							:Optional[Union[float, List[float]]] = None      ,   **snake_case							:Dict      ,   ):





        '''simple docstring'''




        super().__init__(**snake_case	)
        A_       :					Optional[int]        =      size if size is not None else {"height": 384, "width": 384}
        A_       :					Tuple        =      get_size_dict(snake_case	)
        A_       :					int        =      do_resize
        A_       :					Dict        =      size
        A_       :					Tuple        =      keep_aspect_ratio
        A_       :					int        =      ensure_multiple_of
        A_       :					Union[str, Any]        =      resample
        A_       :					str        =      do_rescale
        A_       :					Dict        =      rescale_factor
        A_       :					List[Any]        =      do_normalize
        A_       :					Dict        =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        A_       :					List[Any]        =      image_std if image_std is not None else IMAGENET_STANDARD_STD



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:np.ndarray      ,   snake_case							:Dict[str, int]      ,   snake_case							:bool = False      ,   snake_case							:int = 1      ,   snake_case							:PILImageResampling = PILImageResampling.BICUBIC      ,   snake_case							:Optional[Union[str, ChannelDimension]] = None      ,   **snake_case							:Tuple      ,   ):





        '''simple docstring'''




        A_       :					List[Any]        =      get_size_dict(snake_case	)
        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()}"	)
        A_       :					str        =      get_resize_output_image_size(
            snake_case      ,   output_size=(size["height"], size["width"])      ,   keep_aspect_ratio=snake_case      ,   multiple=snake_case      ,   )
        return resize(snake_case      ,   size=snake_case      ,   resample=snake_case      ,   data_format=snake_case      ,   **snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:np.ndarray      ,   snake_case							:Union[int, float]      ,   snake_case							:Optional[Union[str, ChannelDimension]] = None      ,   **snake_case							:Union[str, Any]      ,   ):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   snake_case							:np.ndarray      ,   snake_case							:Union[float, List[float]]      ,   snake_case							:Union[float, List[float]]      ,   snake_case							:Optional[Union[str, ChannelDimension]] = None      ,   **snake_case							:Optional[Any]      ,   ):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:ImageInput      ,   snake_case							:bool = None      ,   snake_case							:int = None      ,   snake_case							:bool = None      ,   snake_case							:int = None      ,   snake_case							:PILImageResampling = None      ,   snake_case							:bool = None      ,   snake_case							:float = None      ,   snake_case							:bool = None      ,   snake_case							:Optional[Union[float, List[float]]] = None      ,   snake_case							:Optional[Union[float, List[float]]] = None      ,   snake_case							:Optional[Union[str, TensorType]] = None      ,   snake_case							:ChannelDimension = ChannelDimension.FIRST      ,   **snake_case							:Optional[int]      ,   ):





        '''simple docstring'''




        A_       :					Dict        =      do_resize if do_resize is not None else self.do_resize
        A_       :					Union[str, Any]        =      size if size is not None else self.size
        A_       :					Optional[Any]        =      get_size_dict(snake_case	)
        A_       :					Dict        =      keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
        A_       :					Union[str, Any]        =      ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
        A_       :					List[str]        =      resample if resample is not None else self.resample
        A_       :					List[str]        =      do_rescale if do_rescale is not None else self.do_rescale
        A_       :					Tuple        =      rescale_factor if rescale_factor is not None else self.rescale_factor
        A_       :					Optional[Any]        =      do_normalize if do_normalize is not None else self.do_normalize
        A_       :					Optional[Any]        =      image_mean if image_mean is not None else self.image_mean
        A_       :					Tuple        =      image_std if image_std is not None else self.image_std

        A_       :					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 or resample is None:
           raise ValueError("Size and resample 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 std must be specified if do_normalize is True."	)

        # All transformations expect numpy arrays.
        A_       :					Optional[Any]        =      [to_numpy_array(snake_case	) for image in images]

        if do_resize:
           A_       :					List[Any]        =      [self.resize(image=snake_case      ,   size=snake_case      ,   resample=snake_case	) for image in images]

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

        if do_normalize:
           A_       :					int        =      [self.normalize(image=snake_case      ,   mean=snake_case      ,   std=snake_case	) for image in images]

        A_       :					str        =      [to_channel_dimension_format(snake_case      ,   snake_case	) for image in images]

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





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:List[str]      ,   snake_case							:List[Tuple] = None	):





        '''simple docstring'''




        A_       :					Any        =      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	):
              A_       :					Optional[Any]        =      target_sizes.numpy()

           A_       :					Tuple        =      []

           for idx in range(len(snake_case	)	):
              A_       :					Union[str, Any]        =      torch.nn.functional.interpolate(
                  logits[idx].unsqueeze(dim=0	)      ,   size=target_sizes[idx]      ,   mode="bilinear"      ,   align_corners=snake_case	)
              A_       :					Any        =      resized_logits[0].argmax(dim=0	)
              semantic_segmentation.append(snake_case	)
        else:
           A_       :					List[Any]        =      logits.argmax(dim=1	)
           A_       :					Optional[int]        =      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

        return semantic_segmentation







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''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



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Any        =      VQModel(
            block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,   latent_channels=3      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					int        =      CLIPTextConfig(
            bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1_000      ,   )
        return CLIPTextModel(snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Dict        =      self.dummy_uncond_unet
        A_       :					Any        =      DDIMScheduler()
        A_       :					List[str]        =      self.dummy_vq_model

        A_       :					Union[str, Any]        =      LDMPipeline(unet=snake_case      ,   vqvae=snake_case      ,   scheduler=snake_case	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

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

        A_       :					Dict        =      torch.manual_seed(0	)
        A_       :					Any        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"      ,   return_dict=snake_case	)[0]

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

        assert image.shape == (1, 64, 64, 3)
        A_       :					str        =      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]	)
        A_       :					List[str]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance




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





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					int        =      LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					int        =      torch.manual_seed(0	)
        A_       :					List[str]        =      ldm(generator=snake_case      ,   num_inference_steps=5      ,   output_type="numpy"	).images

        A_       :					Tuple        =      image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        A_       :					Any        =      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]	)
        A_       :					List[Any]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
