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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCAmelCase            =		logging.get_logger(__name__)


if is_vision_available():
	import PIL





class UpperCAmelCase						( UpperCamelCase__  ):


				'''simple docstring'''






				lowerCamelCase_    = ['''pixel_values''']
				def __init__(						self		,     lowercase = True		,     lowercase = None		,     lowercase = PILImageResampling.BICUBIC		,     lowercase = True		,     lowercase = None		,     lowercase = True		,     lowercase = 1 / 2_5_5		,     lowercase = True		,     lowercase = None		,     lowercase = None		,     lowercase = True		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(**__lowerCamelCase )
						A_		:    Tuple         =				size if size is not None else {'shortest_edge': 2_2_4}
						A_		:    Any         =				get_size_dict(__lowerCamelCase		,     default_to_square=__lowerCamelCase )
						A_		:    Tuple         =				crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
						A_		:    List[str]         =				get_size_dict(__lowerCamelCase		,     default_to_square=__lowerCamelCase		,     param_name='crop_size' )

						A_		:    Union[str, Any]         =				do_resize
						A_		:    Optional[Any]         =				size
						A_		:    Tuple         =				resample
						A_		:    List[Any]         =				do_center_crop
						A_		:    Dict         =				crop_size
						A_		:    int         =				do_rescale
						A_		:    Optional[int]         =				rescale_factor
						A_		:    Optional[Any]         =				do_normalize
						A_		:    List[str]         =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						A_		:    List[Any]         =				image_std if image_std is not None else OPENAI_CLIP_STD
						A_		:    Dict         =				do_convert_rgb
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = PILImageResampling.BICUBIC		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[Any]         =				get_size_dict(__lowerCamelCase		,     default_to_square=__lowerCamelCase )
						if "shortest_edge" not in size:
								raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
						A_		:    str         =				get_resize_output_image_size(__lowerCamelCase		,     size=size['shortest_edge']		,     default_to_square=__lowerCamelCase )
						return resize(__lowerCamelCase		,     size=__lowerCamelCase		,     resample=__lowerCamelCase		,     data_format=__lowerCamelCase		,     **__lowerCamelCase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[Any]         =				get_size_dict(__lowerCamelCase )
						if "height" not in size or "width" not in size:
								raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
						return center_crop(__lowerCamelCase		,     size=(size['height'], size['width'])		,     data_format=__lowerCamelCase		,     **__lowerCamelCase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						return rescale(__lowerCamelCase		,     scale=__lowerCamelCase		,     data_format=__lowerCamelCase		,     **__lowerCamelCase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





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







				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 = None		,     lowercase = ChannelDimension.FIRST		,     **lowercase		,     ):



						"""simple docstring"""





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

						A_		:    Dict         =				make_list_of_images(__lowerCamelCase )

						if not valid_images(__lowerCamelCase ):
								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.' )

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
								A_		:    List[Any]         =				[convert_to_rgb(__lowerCamelCase ) for image in images]

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

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

						if do_center_crop:
								A_		:    int         =				[self.center_crop(image=__lowerCamelCase		,     size=__lowerCamelCase ) for image in images]

						if do_rescale:
								A_		:    List[str]         =				[self.rescale(image=__lowerCamelCase		,     scale=__lowerCamelCase ) for image in images]

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

						A_		:    Optional[int]         =				[to_channel_dimension_format(__lowerCamelCase		,     __lowerCamelCase ) for image in images]

						A_		:    str         =				{'pixel_values': images}
						return BatchFeature(data=__lowerCamelCase		,     tensor_type=__lowerCamelCase )







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 UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    int         =				UNetaDModel(
						    block_out_channels=(3_2, 6_4)		,     layers_per_block=2		,     sample_size=3_2		,     in_channels=3		,     out_channels=3		,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,     )
						return model
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    Optional[Any]         =				VQModel(
						    block_out_channels=[3_2, 6_4]		,     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 ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    List[Any]         =				CLIPTextConfig(
						    bos_token_id=0		,     eos_token_id=2		,     hidden_size=3_2		,     intermediate_size=3_7		,     layer_norm_eps=1E-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=1_0_0_0		,     )
						return CLIPTextModel(lowercase )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.dummy_uncond_unet
						A_		:    List[Any]         =				DDIMScheduler()
						A_		:    Any         =				self.dummy_vq_model

						A_		:    int         =				LDMPipeline(unet=lowercase		,     vqvae=lowercase		,     scheduler=lowercase )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

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

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				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, 6_4, 6_4, 3)
						A_		:    List[str]         =				np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
						A_		:    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 UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

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

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

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						A_		:    Tuple         =				np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
						A_		:    Dict         =				1E-2 if torch_device != 'mps' else 3E-2

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






