

"""simple docstring"""




import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 			__UpperCamelCase (   _A     ,		unittest.TestCase						):
		SCREAMING_SNAKE_CASE								=    KandinskyVaaPriorEmbaEmbPipeline
		SCREAMING_SNAKE_CASE								=    ["prompt", "image"]
		SCREAMING_SNAKE_CASE								=    ["prompt", "image"]
		SCREAMING_SNAKE_CASE								=    [
		    "num_images_per_prompt",
		    "strength",
		    "generator",
		    "num_inference_steps",
		    "latents",
		    "negative_prompt",
		    "guidance_scale",
		    "output_type",
		    "return_dict",
		]
		SCREAMING_SNAKE_CASE								=    False


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							return 3_2


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							return 3_2


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							return self.time_input_dim


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							return self.time_input_dim * 4


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							return 1_0_0


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
							return tokenizer


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							torch.manual_seed(0)
							A			=					CLIPTextConfig(
							    bos_token_id=0     ,     eos_token_id=2     ,     hidden_size=self.text_embedder_hidden_size     ,     projection_dim=self.text_embedder_hidden_size     ,     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 CLIPTextModelWithProjection(A_)


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							torch.manual_seed(0)

							A			=					{
							    "num_attention_heads": 2,
							    "attention_head_dim": 1_2,
							    "embedding_dim": self.text_embedder_hidden_size,
							    "num_layers": 1,
							}

							A			=					PriorTransformer(**A_)
							# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
							A			=					nn.Parameter(torch.ones(model.clip_std.shape))
							return model


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							torch.manual_seed(0)
							A			=					CLIPVisionConfig(
							    hidden_size=self.text_embedder_hidden_size     ,     image_size=2_2_4     ,     projection_dim=self.text_embedder_hidden_size     ,     intermediate_size=3_7     ,     num_attention_heads=4     ,     num_channels=3     ,     num_hidden_layers=5     ,     patch_size=1_4     ,     )

							A			=					CLIPVisionModelWithProjection(A_)
							return model


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							A			=					CLIPImageProcessor(
							    crop_size=2_2_4     ,     do_center_crop=A_     ,     do_normalize=A_     ,     do_resize=A_     ,     image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,     image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,     resample=3     ,     size=2_2_4     ,     )

							return image_processor


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							A			=					self.dummy_prior
							A			=					self.dummy_image_encoder
							A			=					self.dummy_text_encoder
							A			=					self.dummy_tokenizer
							A			=					self.dummy_image_processor

							A			=					UnCLIPScheduler(
							    variance_type="fixed_small_log"     ,     prediction_type="sample"     ,     num_train_timesteps=1_0_0_0     ,     clip_sample=A_     ,     clip_sample_range=1_0.0     ,     )

							A			=					{
							    "prior": prior,
							    "image_encoder": image_encoder,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "scheduler": scheduler,
							    "image_processor": image_processor,
							}

							return components


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		List[Any]=0):


							if str(A_).startswith("mps"):
												A			=					torch.manual_seed(A_)
							else:
												A			=					torch.Generator(device=A_).manual_seed(A_)

							A			=					floats_tensor((1, 3, 6_4, 6_4)     ,     rng=random.Random(A_)).to(A_)
							A			=					image.cpu().permute(0     ,     2     ,     3     ,     1)[0]
							A			=					Image.fromarray(np.uinta(A_)).convert("RGB").resize((2_5_6, 2_5_6))

							A			=					{
							    "prompt": "horse",
							    "image": init_image,
							    "strength": 0.5,
							    "generator": generator,
							    "guidance_scale": 4.0,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}
							return inputs


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							A			=					"cpu"

							A			=					self.get_dummy_components()

							A			=					self.pipeline_class(**A_)
							A			=					pipe.to(A_)

							pipe.set_progress_bar_config(disable=A_)

							A			=					pipe(**self.get_dummy_inputs(A_))
							A			=					output.image_embeds

							A			=					pipe(
							    **self.get_dummy_inputs(A_)     ,     return_dict=A_     ,     )[0]

							A			=					image[0, -1_0:]
							A			=					image_from_tuple[0, -1_0:]

							assert image.shape == (1, 3_2)

							A			=					np.array(
							    [
							        0.1_0_7_1_2_8_4,
							        1.3_3_3_0_2_7_1,
							        0.6_1_2_6_0_2_2_3,
							        -0.6_6_9_1_0_6_5,
							        -0.3_8_4_6_8_5_2,
							        -1.0_3_0_3_6_6_1,
							        0.2_2_7_1_6_1_1_1,
							        0.0_3_3_4_8_9_0_1,
							        0.3_0_0_4_0_6_7_5,
							        -0.2_4_8_0_5_0_2_9,
							    ])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


		@skip_mps
		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							A			=					torch_device == "cpu"
							A			=					True
							A			=					False

							self._test_inference_batch_single_identical(
							    test_max_difference=A_     ,     relax_max_difference=A_     ,     test_mean_pixel_difference=A_     ,     )


		@skip_mps
		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							A			=					torch_device == "cpu"
							A			=					False

							self._test_attention_slicing_forward_pass(
							    test_max_difference=A_     ,     test_mean_pixel_difference=A_     ,     )








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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)


if is_vision_available():
					import PIL

class  a     (		UpperCAmelCase ):
						_lowercase			  =	["pixel_values"]


						def __init__(				self  ,    A_ = True  ,    A_ = None  ,    A_ = PILImageResampling.BILINEAR  ,    A_ = True  ,    A_ = False  ,    A_ = True  ,    A_ = True  ,    A_ = 1 / 255  ,    A_ = True  ,    A_ = None  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										super().__init__(**A_		)

										_UpperCAmelCase				:						str       						=			size if size is not None else {"height": 2560, "width": 1920}
										if isinstance(A_  ,    (tuple, list)		):
														# The previous feature extractor size parameter was in (width, height) format
														_UpperCAmelCase				:						List[Any]       						=			size[::-1]
										_UpperCAmelCase				:						Dict       						=			get_size_dict(A_		)

										_UpperCAmelCase				:						int       						=			do_resize
										_UpperCAmelCase				:						Dict       						=			size
										_UpperCAmelCase				:						List[Any]       						=			resample
										_UpperCAmelCase				:						Union[str, Any]       						=			do_thumbnail
										_UpperCAmelCase				:						Optional[int]       						=			do_align_long_axis
										_UpperCAmelCase				:						Optional[Any]       						=			do_pad
										_UpperCAmelCase				:						int       						=			do_rescale
										_UpperCAmelCase				:						List[Any]       						=			rescale_factor
										_UpperCAmelCase				:						Union[str, Any]       						=			do_normalize
										_UpperCAmelCase				:						Any       						=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_UpperCAmelCase				:						Dict       						=			image_std if image_std is not None else IMAGENET_STANDARD_STD


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = None		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						List[str]       						=			get_image_size(A_		)
										_UpperCAmelCase						,							_UpperCAmelCase				:						Any       						=			size["height"], size["width"]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
														_UpperCAmelCase				:						Optional[int]       						=			np.rotaa(A_  ,    3		)

										if data_format is not None:
														_UpperCAmelCase				:						List[str]       						=			to_channel_dimension_format(A_  ,    A_		)

										return image


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = False  ,    A_ = None  ,    ):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[Any]       						=			size["height"], size["width"]
										_UpperCAmelCase						,							_UpperCAmelCase				:						List[Any]       						=			get_image_size(A_		)

										_UpperCAmelCase				:						Optional[Any]       						=			output_width - input_width
										_UpperCAmelCase				:						Any       						=			output_height - input_height

										if random_padding:
														_UpperCAmelCase				:						List[str]       						=			np.random.randint(low=0  ,    high=delta_height + 1		)
														_UpperCAmelCase				:						Optional[int]       						=			np.random.randint(low=0  ,    high=delta_width + 1		)
										else:
														_UpperCAmelCase				:						Union[str, Any]       						=			delta_height // 2
														_UpperCAmelCase				:						str       						=			delta_width // 2

										_UpperCAmelCase				:						Optional[Any]       						=			delta_height - pad_top
										_UpperCAmelCase				:						List[Any]       						=			delta_width - pad_left

										_UpperCAmelCase				:						Optional[Any]       						=			((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(A_  ,    A_  ,    data_format=A_		)


						def 				_UpperCAmelCase (				self  ,    *A_  ,    **A_		):







										'''simple docstring'''


										logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."		)
										return self.pad_image(*A_  ,    **A_		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = PILImageResampling.BICUBIC  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			get_image_size(A_		)
										_UpperCAmelCase						,							_UpperCAmelCase				:						Union[str, Any]       						=			size["height"], size["width"]

										# We always resize to the smallest of either the input or output size.
										_UpperCAmelCase				:						Dict       						=			min(A_  ,    A_		)
										_UpperCAmelCase				:						Tuple       						=			min(A_  ,    A_		)

										if height == input_height and width == input_width:
														return image

										if input_height > input_width:
														_UpperCAmelCase				:						Any       						=			int(input_width * height / input_height		)
										elif input_width > input_height:
														_UpperCAmelCase				:						Any       						=			int(input_height * width / input_width		)

										return resize(
										    A_  ,    size=(height, width)  ,    resample=A_  ,    reducing_gap=2.0  ,    data_format=A_  ,    **A_		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = PILImageResampling.BICUBIC  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			get_size_dict(A_		)
										_UpperCAmelCase				:						List[str]       						=			min(size["height"]  ,    size["width"]		)
										_UpperCAmelCase				:						Optional[Any]       						=			get_resize_output_image_size(A_  ,    size=A_  ,    default_to_square=A_		)
										_UpperCAmelCase				:						str       						=			resize(A_  ,    size=A_  ,    resample=A_  ,    data_format=A_  ,    **A_		)
										return resized_image


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


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


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


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





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = False  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = ChannelDimension.FIRST  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase				:						str       						=			size if size is not None else self.size
										if isinstance(A_  ,    (tuple, list)		):
														# Previous feature extractor had size in (width, height) format
														_UpperCAmelCase				:						List[Any]       						=			size[::-1]
										_UpperCAmelCase				:						Union[str, Any]       						=			get_size_dict(A_		)
										_UpperCAmelCase				:						Dict       						=			resample if resample is not None else self.resample
										_UpperCAmelCase				:						str       						=			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										_UpperCAmelCase				:						Optional[Any]       						=			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										_UpperCAmelCase				:						List[str]       						=			do_pad if do_pad is not None else self.do_pad
										_UpperCAmelCase				:						Optional[Any]       						=			do_rescale if do_rescale is not None else self.do_rescale
										_UpperCAmelCase				:						int       						=			rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCAmelCase				:						List[str]       						=			do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase				:						Any       						=			image_mean if image_mean is not None else self.image_mean
										_UpperCAmelCase				:						List[Any]       						=			image_std if image_std is not None else self.image_std

										_UpperCAmelCase				:						str       						=			make_list_of_images(A_		)

										if not valid_images(A_		):
														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_rescale and rescale_factor is None:
														raise ValueError("Rescale factor must be specified if do_rescale is True."		)

										if do_pad and size is None:
														raise ValueError("Size must be specified if do_pad 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.
										_UpperCAmelCase				:						Union[str, Any]       						=			[to_numpy_array(A_		) for image in images]

										if do_align_long_axis:
														_UpperCAmelCase				:						Tuple       						=			[self.align_long_axis(A_  ,    size=A_		) for image in images]

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

										if do_thumbnail:
														_UpperCAmelCase				:						Optional[Any]       						=			[self.thumbnail(image=A_  ,    size=A_		) for image in images]

										if do_pad:
														_UpperCAmelCase				:						List[Any]       						=			[self.pad_image(image=A_  ,    size=A_  ,    random_padding=A_		) for image in images]

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

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

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

										_UpperCAmelCase				:						Dict       						=			{"pixel_values": images}
										return BatchFeature(data=A_  ,    tensor_type=A_		)


