"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, 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_vision_available, logging


if is_vision_available():
 import PIL


lowercase__ 	=     logging.get_logger(__name__)




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		["""pixel_values"""]



      def __init__(						self   ,      lowercase = True   ,      lowercase = None   ,      lowercase = PIL.Image.BICUBIC   ,      lowercase = True   ,      lowercase = None   ,      lowercase = 1 / 2_55   ,      lowercase = True   ,      lowercase = True   ,      lowercase = None   ,      lowercase = None   ,      **lowercase   ,      )     ->      None:





        '''simple docstring'''





        super().__init__(**lowercase)
        a__:					str      							=     size if size is not None else {'height': 2_56, 'width': 2_56}
        a__:					str      							=     get_size_dict(lowercase)
        a__:					List[str]      							=     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
        a__:					List[str]      							=     get_size_dict(lowercase   ,      param_name='crop_size')

        a__:					Tuple      							=     do_resize
        a__:					Tuple      							=     size
        a__:					Union[str, Any]      							=     resample
        a__:					int      							=     do_center_crop
        a__:					Tuple      							=     crop_size
        a__:					Optional[int]      							=     do_rescale
        a__:					Optional[Any]      							=     rescale_factor
        a__:					Optional[int]      							=     do_normalize
        a__:					Any      							=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        a__:					Optional[Any]      							=     image_std if image_std is not None else IMAGENET_STANDARD_STD



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = PIL.Image.BICUBIC   ,      lowercase = None   ,      **lowercase   ,      )     ->      np.ndarray:





        '''simple docstring'''





        a__:					int      							=     get_size_dict(lowercase)
        if "height" not in size or "width" not in size:
          raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}')
        return resize(
            lowercase   ,      size=(size['height'], size['width'])   ,      resample=lowercase   ,      data_format=lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      **lowercase   ,      )     ->      np.ndarray:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     get_size_dict(lowercase)
        if "height" not in size or "width" not in size:
          raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}')
        return center_crop(lowercase   ,      size=(size['height'], size['width'])   ,      data_format=lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      **lowercase   ,      )     ->      Dict:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      **lowercase   ,      )     ->      np.ndarray:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None   ,      lowercase = None   ,      lowercase=None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = ChannelDimension.FIRST   ,      **lowercase   ,      )     ->      PIL.Image.Image:





        '''simple docstring'''





        a__:					Any      							=     do_resize if do_resize is not None else self.do_resize
        a__:					Union[str, Any]      							=     resample if resample is not None else self.resample
        a__:					int      							=     do_center_crop if do_center_crop is not None else self.do_center_crop
        a__:					Optional[Any]      							=     do_rescale if do_rescale is not None else self.do_rescale
        a__:					Dict      							=     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__:					Union[str, Any]      							=     image_mean if image_mean is not None else self.image_mean
        a__:					Any      							=     image_std if image_std is not None else self.image_std

        a__:					str      							=     size if size is not None else self.size
        a__:					Optional[int]      							=     get_size_dict(lowercase)
        a__:					str      							=     crop_size if crop_size is not None else self.crop_size
        a__:					Optional[int]      							=     get_size_dict(lowercase   ,      param_name='crop_size')

        a__:					List[Any]      							=     make_list_of_images(lowercase)

        if not valid_images(lowercase):
          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_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.
        a__:					Optional[int]      							=     [to_numpy_array(lowercase) for image in images]

        if do_resize:
          a__:					str      							=     [self.resize(image=lowercase   ,      size=lowercase   ,      resample=lowercase) for image in images]

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

        if do_rescale:
          a__:					Tuple      							=     [self.rescale(image=lowercase   ,      scale=lowercase) for image in images]

        if do_normalize:
          a__:					Any      							=     [self.normalize(image=lowercase   ,      mean=lowercase   ,      std=lowercase) for image in images]

        a__:					Dict      							=     [to_channel_dimension_format(lowercase   ,      lowercase) for image in images]

        a__:					Union[str, Any]      							=     {'pixel_values': images}
        return BatchFeature(data=lowercase   ,      tensor_type=lowercase)



"""simple docstring"""
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        __snake_case	(					unittest.TestCase    ):



      @property
      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					str      							=     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   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					List[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   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					Optional[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=10_00   ,      )
        return CLIPTextModel(lowercase)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.dummy_uncond_unet
        a__:					Optional[int]      							=     DDIMScheduler()
        a__:					Optional[int]      							=     self.dummy_vq_model

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

        a__:					str      							=     torch.manual_seed(0)
        a__:					Dict      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy').images

        a__:					Union[str, Any]      							=     torch.manual_seed(0)
        a__:					int      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy'   ,      return_dict=lowercase)[0]

        a__:					Union[str, Any]      							=     image[0, -3:, -3:, -1]
        a__:					int      							=     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        a__:					int      							=     np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
        a__:					Optional[Any]      							=     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        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256')
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					List[str]      							=     torch.manual_seed(0)
        a__:					Optional[int]      							=     ldm(generator=lowercase   ,      num_inference_steps=5   ,      output_type='numpy').images

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

        assert image.shape == (1, 2_56, 2_56, 3)
        a__:					int      							=     np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
        a__:					Any      							=     1e-2 if torch_device != 'mps' else 3e-2

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


