






"""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 						__lowerCamelCase							(					snake_case__					,				unittest.TestCase     ):





		'''simple docstring'''






		a_      :							Tuple  =				KandinskyImgaImgPipeline
		a_      :							str  =				["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
		a_      :							Dict  =				[
		    """prompt""",
		    """negative_prompt""",
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		]
		a_      :							int  =				[
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """negative_prompt""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		a_      :							List[str]  =				False





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





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





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





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





		@property
		def   lowerCamelCase			(  self				:      List[str]      ):
				return 1_00





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





		@property
		def   lowerCamelCase			(  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=10_05		,     )

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

				return text_encoder





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





		def   lowerCamelCase			(  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''': 10_00,
				    '''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   lowerCamelCase			(  self				:      List[Any]		,     a_				:      List[str]		,     a_				:      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((2_56, 2_56)      )

				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   lowerCamelCase			(  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 						__lowerCamelCase							(					unittest.TestCase     ):





		'''simple docstring'''






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





		def   lowerCamelCase			(  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_  :				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=1_00		,     height=7_68		,     width=7_68		,     strength=0.2		,     output_type="np"		,     )

				lowerCAmelCase_  :				Dict   							=     output.images[0]

				assert image.shape == (7_68, 7_68, 3)

				assert_mean_pixel_difference(__lowercase		,     __lowercase      )







"""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,
    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_vision_available, logging


if is_vision_available():
							import PIL


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




				def __init__(  self	:str    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = PILImageResampling.BILINEAR    ,     __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''': 384}
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Any       =do_resize
											__lowerCamelCase   :   List[str]       =size
											# Default value set here for backwards compatibility where the value in config is None
											__lowerCamelCase   :   Tuple       =crop_pct if crop_pct is not None else 224 / 256
											__lowerCamelCase   :   Any       =resample
											__lowerCamelCase   :   List[str]       =do_rescale
											__lowerCamelCase   :   Dict       =rescale_factor
											__lowerCamelCase   :   Any       =do_normalize
											__lowerCamelCase   :   int       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase   :   List[Any]       =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        __lowercase	(  self	:Any    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:float    ,     __lowercase	:PILImageResampling = PILImageResampling.BICUBIC    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:int    ,     ):
											__lowerCamelCase   :   int       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											if "shortest_edge" not in size:
																		raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'      )
											__lowerCamelCase   :   Optional[int]       =size['''shortest_edge''']

											if shortest_edge < 384:
																		# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																		__lowerCamelCase   :   int       =int(shortest_edge / crop_pct      )
																		__lowerCamelCase   :   str       =get_resize_output_image_size(__lowercase    ,     size=__lowercase    ,     default_to_square=__lowercase      )
																		__lowerCamelCase   :   str       =resize(image=__lowercase    ,     size=__lowercase    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )
																		# then crop to (shortest_edge, shortest_edge)
																		return center_crop(image=__lowercase    ,     size=(shortest_edge, shortest_edge)    ,     data_format=__lowercase    ,     **__lowercase      )
											else:
																		# warping (no cropping) when evaluated at 384 or larger
																		return resize(
																		    __lowercase    ,     size=(shortest_edge, shortest_edge)    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




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




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




				def        __lowercase	(  self	:int    ,     __lowercase	:ImageInput    ,     __lowercase	:bool = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = None    ,     __lowercase	:bool = None    ,     __lowercase	:float = None    ,     __lowercase	:bool = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[str, TensorType]] = None    ,     __lowercase	:ChannelDimension = ChannelDimension.FIRST    ,     **__lowercase	:Optional[Any]    ,     ):
											__lowerCamelCase   :   Optional[Any]       =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase   :   int       =crop_pct if crop_pct is not None else self.crop_pct
											__lowerCamelCase   :   List[Any]       =resample if resample is not None else self.resample
											__lowerCamelCase   :   List[Any]       =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase   :   List[Any]       =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   :   Optional[Any]       =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase   :   str       =image_std if image_std is not None else self.image_std

											__lowerCamelCase   :   str       =size if size is not None else self.size
											__lowerCamelCase   :   Optional[int]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Optional[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 or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

											if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																		raise ValueError('''crop_pct must be specified if size < 384.'''      )

											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   :   List[Any]       =[to_numpy_array(__lowercase      ) for image in images]

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

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

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

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

											__lowerCamelCase   :   Tuple       ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase    ,     tensor_type=__lowercase      )




