





"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 		SCREAMING_SNAKE_CASE_				(     snake_case__							,	unittest.TestCase ):
				"""simple docstring"""
				__snake_case :							Tuple									=		KandinskyImgaImgPipeline
				__snake_case :							str									=		["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
				__snake_case :							Dict									=		[
				    """prompt""",
				    """negative_prompt""",
				    """image_embeds""",
				    """negative_image_embeds""",
				    """image""",
				]
				__snake_case :							int									=		[
				    """generator""",
				    """height""",
				    """width""",
				    """strength""",
				    """guidance_scale""",
				    """negative_prompt""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				__snake_case :							List[str]									=		False




				@property
				def        __lowercase	(  self	:Any      ):
											return 32




				@property
				def        __lowercase	(  self	:List[Any]      ):
											return 32




				@property
				def        __lowercase	(  self	:List[str]      ):
											return self.time_input_dim




				@property
				def        __lowercase	(  self	:str      ):
											return self.time_input_dim * 4




				@property
				def        __lowercase	(  self	:List[str]      ):
											return 100




				@property
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   Union[str, Any]       =XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''      )
											return tokenizer




				@property
				def        __lowercase	(  self	:Union[str, Any]      ):
											torch.manual_seed(0      )
											__lowerCamelCase   :   Any       =MCLIPConfig(
											    numDims=self.cross_attention_dim    ,     transformerDimensions=self.text_embedder_hidden_size    ,     hidden_size=self.text_embedder_hidden_size    ,     intermediate_size=37    ,     num_attention_heads=4    ,     num_hidden_layers=5    ,     vocab_size=1005    ,     )

											__lowerCamelCase   :   str       =MultilingualCLIP(__lowercase      )
											__lowerCamelCase   :   Optional[Any]       =text_encoder.eval()

											return text_encoder




				@property
				def        __lowercase	(  self	:Any      ):
											torch.manual_seed(0      )

											__lowerCamelCase   :   Union[str, Any]       ={
											    '''in_channels''': 4,
											    # Out channels is double in channels because predicts mean and variance
											    '''out_channels''': 8,
											    '''addition_embed_type''': '''text_image''',
											    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
											    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
											    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
											    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
											    '''layers_per_block''': 1,
											    '''encoder_hid_dim''': self.text_embedder_hidden_size,
											    '''encoder_hid_dim_type''': '''text_image_proj''',
											    '''cross_attention_dim''': self.cross_attention_dim,
											    '''attention_head_dim''': 4,
											    '''resnet_time_scale_shift''': '''scale_shift''',
											    '''class_embed_type''': None,
											}

											__lowerCamelCase   :   int       =UNetaDConditionModel(**__lowercase      )
											return model




				@property
				def        __lowercase	(  self	:List[Any]      ):
											return {
											    "block_out_channels": [32, 64],
											    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
											    "in_channels": 3,
											    "latent_channels": 4,
											    "layers_per_block": 1,
											    "norm_num_groups": 8,
											    "norm_type": "spatial",
											    "num_vq_embeddings": 12,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}




				@property
				def        __lowercase	(  self	:Any      ):
											torch.manual_seed(0      )
											__lowerCamelCase   :   Union[str, Any]       =VQModel(**self.dummy_movq_kwargs      )
											return model




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   int       =self.dummy_text_encoder
											__lowerCamelCase   :   List[str]       =self.dummy_tokenizer
											__lowerCamelCase   :   List[Any]       =self.dummy_unet
											__lowerCamelCase   :   List[Any]       =self.dummy_movq

											__lowerCamelCase   :   Optional[Any]       ={
											    '''num_train_timesteps''': 1000,
											    '''beta_schedule''': '''linear''',
											    '''beta_start''': 0.00085,
											    '''beta_end''': 0.012,
											    '''clip_sample''': False,
											    '''set_alpha_to_one''': False,
											    '''steps_offset''': 0,
											    '''prediction_type''': '''epsilon''',
											    '''thresholding''': False,
											}

											__lowerCamelCase   :   str       =DDIMScheduler(**__lowercase      )

											__lowerCamelCase   :   str       ={
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''movq''': movq,
											}

											return components




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:List[str]    ,     __lowercase	:Dict=0      ):
											__lowerCamelCase   :   Union[str, Any]       =floats_tensor((1, self.cross_attention_dim)    ,     rng=random.Random(__lowercase      )      ).to(__lowercase      )
											__lowerCamelCase   :   Dict       =floats_tensor((1, self.cross_attention_dim)    ,     rng=random.Random(seed + 1      )      ).to(__lowercase      )
											# create init_image
											__lowerCamelCase   :   Tuple       =floats_tensor((1, 3, 64, 64)    ,     rng=random.Random(__lowercase      )      ).to(__lowercase      )
											__lowerCamelCase   :   int       =image.cpu().permute(0    ,     2    ,     3    ,     1      )[0]
											__lowerCamelCase   :   int       =Image.fromarray(np.uinta(__lowercase      )      ).convert('''RGB'''      ).resize((256, 256)      )

											if str(__lowercase      ).startswith('''mps'''      ):
																		__lowerCamelCase   :   List[str]       =torch.manual_seed(__lowercase      )
											else:
																		__lowerCamelCase   :   Any       =torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
											__lowerCamelCase   :   Dict       ={
											    '''prompt''': '''horse''',
											    '''image''': init_image,
											    '''image_embeds''': image_embeds,
											    '''negative_image_embeds''': negative_image_embeds,
											    '''generator''': generator,
											    '''height''': 64,
											    '''width''': 64,
											    '''num_inference_steps''': 10,
											    '''guidance_scale''': 7.0,
											    '''strength''': 0.2,
											    '''output_type''': '''np''',
											}
											return inputs




				def        __lowercase	(  self	:List[Any]      ):
											__lowerCamelCase   :   Union[str, Any]       ='''cpu'''

											__lowerCamelCase   :   List[Any]       =self.get_dummy_components()

											__lowerCamelCase   :   Optional[Any]       =self.pipeline_class(**__lowercase      )
											__lowerCamelCase   :   List[Any]       =pipe.to(__lowercase      )

											pipe.set_progress_bar_config(disable=__lowercase      )

											__lowerCamelCase   :   Dict       =pipe(**self.get_dummy_inputs(__lowercase      )      )
											__lowerCamelCase   :   Tuple       =output.images

											__lowerCamelCase   :   List[Any]       =pipe(
											    **self.get_dummy_inputs(__lowercase      )    ,     return_dict=__lowercase    ,     )[0]

											__lowerCamelCase   :   Dict       =image[0, -3:, -3:, -1]
											__lowerCamelCase   :   Any       =image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)

											__lowerCamelCase   :   Optional[Any]       =np.array(
											    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]      )
											assert (
											    np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
											), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
											assert (
											    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
											), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'

@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:List[str]      ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   Optional[Any]       =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/kandinsky/kandinsky_img2img_frog.npy'''      )

											__lowerCamelCase   :   Union[str, Any]       =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
											__lowerCamelCase   :   str       ='''A red cartoon frog, 4k'''

											__lowerCamelCase   :   Optional[Any]       =KandinskyPriorPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-1-prior'''    ,     torch_dtype=torch.floataa      )
											pipe_prior.to(__lowercase      )

											__lowerCamelCase   :   str       =KandinskyImgaImgPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-1'''    ,     torch_dtype=torch.floataa      )
											__lowerCamelCase   :   Tuple       =pipeline.to(__lowercase      )

											pipeline.set_progress_bar_config(disable=__lowercase      )

											__lowerCamelCase   :   List[str]       =torch.Generator(device='''cpu'''      ).manual_seed(0      )
											__lowerCamelCase     ,    __lowerCamelCase   :   Any       =pipe_prior(
											    __lowercase    ,     generator=__lowercase    ,     num_inference_steps=5    ,     negative_prompt=''''''    ,     ).to_tuple()

											__lowerCamelCase   :   Any       =pipeline(
											    __lowercase    ,     image=__lowercase    ,     image_embeds=__lowercase    ,     negative_image_embeds=__lowercase    ,     generator=__lowercase    ,     num_inference_steps=100    ,     height=768    ,     width=768    ,     strength=0.2    ,     output_type='''np'''    ,     )

											__lowerCamelCase   :   Dict       =output.images[0]

											assert image.shape == (768, 768, 3)

											assert_mean_pixel_difference(__lowercase    ,     __lowercase      )











"""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,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


_UpperCamelCase					      =   logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Optional[int]									=		["""pixel_values"""]




				def __init__(  self	:Optional[Any]    ,     __lowercase	:bool = True    ,     __lowercase	:Optional[Dict[str, int]] = None    ,     __lowercase	:PILImageResampling = PILImageResampling.BILINEAR    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:bool = True    ,     __lowercase	:Union[int, float] = 1 / 255    ,     __lowercase	:bool = True    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     **__lowercase	:Tuple    ,     ):
											super().__init__(**__lowercase      )
											__lowerCamelCase   :   int       =size if size is not None else {'''shortest_edge''': 256}
											__lowerCamelCase   :   int       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											__lowerCamelCase   :   Optional[Any]       =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     param_name='''crop_size'''      )
											__lowerCamelCase   :   List[str]       =do_resize
											__lowerCamelCase   :   Optional[Any]       =size
											__lowerCamelCase   :   Tuple       =resample
											__lowerCamelCase   :   int       =do_center_crop
											__lowerCamelCase   :   Optional[Any]       =crop_size
											__lowerCamelCase   :   int       =do_rescale
											__lowerCamelCase   :   str       =rescale_factor
											__lowerCamelCase   :   int       =do_normalize
											__lowerCamelCase   :   Union[str, Any]       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase   :   Tuple       =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:PILImageResampling = PILImageResampling.BICUBIC    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:str    ,     ):
											__lowerCamelCase   :   Tuple       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											if "shortest_edge" not in size:
																		raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
											__lowerCamelCase   :   List[Any]       =get_resize_output_image_size(__lowercase    ,     size=size['''shortest_edge''']    ,     default_to_square=__lowercase      )
											return resize(__lowercase    ,     size=__lowercase    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:int    ,     ):
											__lowerCamelCase   :   List[Any]       =get_size_dict(__lowercase      )
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'      )
											return center_crop(__lowercase    ,     size=(size['''height'''], size['''width'''])    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:str    ,     __lowercase	:np.ndarray    ,     __lowercase	:float    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:str      ):
											return rescale(__lowercase    ,     scale=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:Union[str, Any]    ,     ):
											return normalize(__lowercase    ,     mean=__lowercase    ,     std=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:ImageInput    ,     __lowercase	:Optional[bool] = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:PILImageResampling = None    ,     __lowercase	:bool = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:Optional[bool] = None    ,     __lowercase	:Optional[float] = None    ,     __lowercase	:Optional[bool] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[str, TensorType]] = None    ,     __lowercase	:Union[str, ChannelDimension] = ChannelDimension.FIRST    ,     **__lowercase	:Optional[Any]    ,     ):
											__lowerCamelCase   :   Optional[Any]       =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase   :   Optional[Any]       =size if size is not None else self.size
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											__lowerCamelCase   :   List[str]       =resample if resample is not None else self.resample
											__lowerCamelCase   :   Optional[int]       =do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCamelCase   :   Any       =crop_size if crop_size is not None else self.crop_size
											__lowerCamelCase   :   Union[str, Any]       =get_size_dict(__lowercase    ,     param_name='''crop_size'''      )
											__lowerCamelCase   :   Any       =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase   :   List[str]       =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase   :   List[Any]       =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase   :   List[Any]       =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase   :   int       =image_std if image_std is not None else self.image_std

											__lowerCamelCase   :   int       =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:
																		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.'''      )

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

											if do_resize:
																		__lowerCamelCase   :   Tuple       =[self.resize(image=__lowercase    ,     size=__lowercase    ,     resample=__lowercase      ) for image in images]

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

											if do_rescale:
																		__lowerCamelCase   :   Optional[Any]       =[self.rescale(image=__lowercase    ,     scale=__lowercase      ) for image in images]

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

											__lowerCamelCase   :   List[str]       =[to_channel_dimension_format(__lowercase    ,     __lowercase      ) for image in images]

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




				def        __lowercase	(  self	:Dict    ,     __lowercase	:Dict    ,     __lowercase	:List[Tuple] = None      ):
											__lowerCamelCase   :   Dict       =outputs.logits

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

																		if is_torch_tensor(__lowercase      ):
																									__lowerCamelCase   :   Optional[int]       =target_sizes.numpy()

																		__lowerCamelCase   :   List[Any]       =[]

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

											return semantic_segmentation




