




'''simple docstring'''







from .pipeline_kandinskya_a import KandinskyVaaPipeline
from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline











'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import CLIPImageProcessor





class 					lowerCamelCase_							(	unittest.TestCase							):




					'''simple docstring'''






					def __init__(   self,  __UpperCamelCase,  __UpperCamelCase=7,  __UpperCamelCase=3,  __UpperCamelCase=18,  __UpperCamelCase=30,  __UpperCamelCase=400,  __UpperCamelCase=True,  __UpperCamelCase=None,  __UpperCamelCase=True,  __UpperCamelCase=None,  __UpperCamelCase=True,  __UpperCamelCase=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],  __UpperCamelCase=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],  __UpperCamelCase=True,  ):
							_snake_case     	=	size if size is not None else {"shortest_edge": 20}
							_snake_case     	=	crop_size if crop_size is not None else {"height": 18, "width": 18}
							_snake_case     	=	parent
							_snake_case     	=	batch_size
							_snake_case     	=	num_channels
							_snake_case     	=	image_size
							_snake_case     	=	min_resolution
							_snake_case     	=	max_resolution
							_snake_case     	=	do_resize
							_snake_case     	=	size
							_snake_case     	=	do_center_crop
							_snake_case     	=	crop_size
							_snake_case     	=	do_normalize
							_snake_case     	=	image_mean
							_snake_case     	=	image_std
							_snake_case     	=	do_convert_rgb






					def     __SCREAMING_SNAKE_CASE     (   self			):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_convert_rgb": self.do_convert_rgb,
							}






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=False			):
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
									_snake_case     	=	[]
									for i in range(self.batch_size			):
											image_inputs.append(
											    np.random.randint(
											        255,  size=(self.num_channels, self.max_resolution, self.max_resolution),  dtype=np.uinta			)			)
							else:
									_snake_case     	=	[]
									for i in range(self.batch_size			):
											_snake_case	,   _snake_case     	=	np.random.choice(np.arange(self.min_resolution,  self.max_resolution			),  2			)
											image_inputs.append(np.random.randint(255,  size=(self.num_channels, width, height),  dtype=np.uinta			)			)

							if not numpify and not torchify:
									# PIL expects the channel dimension as last dimension
									_snake_case     	=	[Image.fromarray(np.moveaxis(__UpperCamelCase,  0,  -1			)			) for x in image_inputs]

							if torchify:
									_snake_case     	=	[torch.from_numpy(__UpperCamelCase			) for x in image_inputs]

							return image_inputs





@require_torch
@require_vision
class 					lowerCamelCase_							(	UpperCAmelCase_,	unittest.TestCase							):




					'''simple docstring'''






					lowercase__   =			CLIPImageProcessor if is_vision_available() else None






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	CLIPImageProcessingTester(self			)






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							return self.image_processor_tester.prepare_image_processor_dict()






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.image_processing_class(**self.image_processor_dict			)
							self.assertTrue(hasattr(__UpperCamelCase,  "do_resize"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "size"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "do_center_crop"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "center_crop"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "do_normalize"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "image_mean"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "image_std"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "do_convert_rgb"			)			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.image_processing_class.from_dict(self.image_processor_dict			)
							self.assertEqual(image_processor.size,  {"shortest_edge": 20}			)
							self.assertEqual(image_processor.crop_size,  {"height": 18, "width": 18}			)

							_snake_case     	=	self.image_processing_class.from_dict(self.image_processor_dict,  size=42,  crop_size=84			)
							self.assertEqual(image_processor.size,  {"shortest_edge": 42}			)
							self.assertEqual(image_processor.crop_size,  {"height": 84, "width": 84}			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							pass






					def     __SCREAMING_SNAKE_CASE     (   self			):
							# Initialize image_processing
							_snake_case     	=	self.image_processing_class(**self.image_processor_dict			)
							# create random PIL images
							_snake_case     	=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase			)
							for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase,  Image.Image			)

							# Test not batched input
							_snake_case     	=	image_processing(image_inputs[0],  return_tensors="pt"			).pixel_values
							self.assertEqual(
							    encoded_images.shape,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),  )

							# Test batched
							_snake_case     	=	image_processing(__UpperCamelCase,  return_tensors="pt"			).pixel_values
							self.assertEqual(
							    encoded_images.shape,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),  )






					def     __SCREAMING_SNAKE_CASE     (   self			):
							# Initialize image_processing
							_snake_case     	=	self.image_processing_class(**self.image_processor_dict			)
							# create random numpy tensors
							_snake_case     	=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase,  numpify=__UpperCamelCase			)
							for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase,  np.ndarray			)

							# Test not batched input
							_snake_case     	=	image_processing(image_inputs[0],  return_tensors="pt"			).pixel_values
							self.assertEqual(
							    encoded_images.shape,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),  )

							# Test batched
							_snake_case     	=	image_processing(__UpperCamelCase,  return_tensors="pt"			).pixel_values
							self.assertEqual(
							    encoded_images.shape,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),  )






					def     __SCREAMING_SNAKE_CASE     (   self			):
							# Initialize image_processing
							_snake_case     	=	self.image_processing_class(**self.image_processor_dict			)
							# create random PyTorch tensors
							_snake_case     	=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase,  torchify=__UpperCamelCase			)
							for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase,  torch.Tensor			)

							# Test not batched input
							_snake_case     	=	image_processing(image_inputs[0],  return_tensors="pt"			).pixel_values
							self.assertEqual(
							    encoded_images.shape,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),  )

							# Test batched
							_snake_case     	=	image_processing(__UpperCamelCase,  return_tensors="pt"			).pixel_values
							self.assertEqual(
							    encoded_images.shape,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),  )





@require_torch
@require_vision
class 					lowerCamelCase_							(	UpperCAmelCase_,	unittest.TestCase							):




					'''simple docstring'''






					lowercase__   =			CLIPImageProcessor if is_vision_available() else None






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	CLIPImageProcessingTester(self,  num_channels=4			)
							_snake_case     	=	3






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							return self.image_processor_tester.prepare_image_processor_dict()






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.image_processing_class(**self.image_processor_dict			)
							self.assertTrue(hasattr(__UpperCamelCase,  "do_resize"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "size"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "do_center_crop"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "center_crop"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "do_normalize"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "image_mean"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "image_std"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "do_convert_rgb"			)			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							pass






					def     __SCREAMING_SNAKE_CASE     (   self			):
							# Initialize image_processing
							_snake_case     	=	self.image_processing_class(**self.image_processor_dict			)
							# create random PIL images
							_snake_case     	=	self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase			)
							for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase,  Image.Image			)

							# Test not batched input
							_snake_case     	=	image_processing(image_inputs[0],  return_tensors="pt"			).pixel_values
							self.assertEqual(
							    encoded_images.shape,  (
							        1,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),  )

							# Test batched
							_snake_case     	=	image_processing(__UpperCamelCase,  return_tensors="pt"			).pixel_values
							self.assertEqual(
							    encoded_images.shape,  (
							        self.image_processor_tester.batch_size,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    ),  )





