

import unittest

import numpy as np
from datasets import load_dataset

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, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import BeitImageProcessor

class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase=7					,					lowercase=3					,					lowercase=18					,					lowercase=30					,					lowercase=400					,					lowercase=True					,					lowercase=None					,					lowercase=True					,					lowercase=None					,					lowercase=True					,					lowercase=[0.5, 0.5, 0.5]					,					lowercase=[0.5, 0.5, 0.5]					,					lowercase=False					,					)     ->  List[Any]:
							'''simple docstring'''






							A__						=	size if size is not None else {"height": 20, "width": 20}
							A__						=	crop_size if crop_size is not None else {"height": 18, "width": 18}
							A__						=	parent
							A__						=	batch_size
							A__						=	num_channels
							A__						=	image_size
							A__						=	min_resolution
							A__						=	max_resolution
							A__						=	do_resize
							A__						=	size
							A__						=	do_center_crop
							A__						=	crop_size
							A__						=	do_normalize
							A__						=	image_mean
							A__						=	image_std
							A__						=	do_reduce_labels


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							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_reduce_labels": self.do_reduce_labels,
							}



def     lowerCAmelCase__							(		)	->					List[Any]:




				'''simple docstring'''





				A__						=	load_dataset("hf-internal-testing/fixtures_ade20k"		,				split="test"					)

				A__						=	Image.open(dataset[0]["file"]					)
				A__						=	Image.open(dataset[1]["file"]					)

				return image, map



def     lowerCAmelCase__							(		)	->					int:




				'''simple docstring'''





				A__						=	load_dataset("hf-internal-testing/fixtures_ade20k"		,				split="test"					)

				A__						=	Image.open(ds[0]["file"]					)
				A__						=	Image.open(ds[1]["file"]					)
				A__						=	Image.open(ds[2]["file"]					)
				A__						=	Image.open(ds[3]["file"]					)

				return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class     a__  (    snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				BeitImageProcessor if is_vision_available() else None


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	BeitImageProcessingTester(self				)


			@property
			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(lowercase					,					"do_resize"				)				)
							self.assertTrue(hasattr(lowercase					,					"size"				)				)
							self.assertTrue(hasattr(lowercase					,					"do_center_crop"				)				)
							self.assertTrue(hasattr(lowercase					,					"center_crop"				)				)
							self.assertTrue(hasattr(lowercase					,					"do_normalize"				)				)
							self.assertTrue(hasattr(lowercase					,					"image_mean"				)				)
							self.assertTrue(hasattr(lowercase					,					"image_std"				)				)


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size					,					{"height": 20, "width": 20}				)
							self.assertEqual(image_processor.crop_size					,					{"height": 18, "width": 18}				)
							self.assertEqual(image_processor.do_reduce_labels					,					lowercase				)

							A__						=	self.image_processing_class.from_dict(
							    self.image_processor_dict					,					size=42					,					crop_size=84					,					reduce_labels=lowercase				)
							self.assertEqual(image_processor.size					,					{"height": 42, "width": 42}				)
							self.assertEqual(image_processor.crop_size					,					{"height": 84, "width": 84}				)
							self.assertEqual(image_processor.do_reduce_labels					,					lowercase				)


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase				)
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					Image.Image				)

							# Test not batched input
							A__						=	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
							A__						=	image_processing(lowercase					,					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    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase					,					numpify=lowercase				)
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					np.ndarray				)

							# Test not batched input
							A__						=	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
							A__						=	image_processing(lowercase					,					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    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase					,					torchify=lowercase				)
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					torch.Tensor				)

							# Test not batched input
							A__						=	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
							A__						=	image_processing(lowercase					,					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    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=lowercase					,					torchify=lowercase				)
							A__						=	[]
							for image in image_inputs:
											self.assertIsInstance(lowercase					,					torch.Tensor				)
											maps.append(torch.zeros(image.shape[-2:]				).long()				)

							# Test not batched input
							A__						=	image_processing(image_inputs[0]					,					maps[0]					,					return_tensors="pt"				)
							self.assertEqual(
							    encoding["pixel_values"].shape					,					(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)
							self.assertEqual(
							    encoding["labels"].shape					,					(
							        1,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)
							self.assertEqual(encoding["labels"].dtype					,					torch.long				)
							self.assertTrue(encoding["labels"].min().item() >= 0				)
							self.assertTrue(encoding["labels"].max().item() <= 255				)

							# Test batched
							A__						=	image_processing(lowercase					,					lowercase					,					return_tensors="pt"				)
							self.assertEqual(
							    encoding["pixel_values"].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"],
							    )					,					)
							self.assertEqual(
							    encoding["labels"].shape					,					(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)
							self.assertEqual(encoding["labels"].dtype					,					torch.long				)
							self.assertTrue(encoding["labels"].min().item() >= 0				)
							self.assertTrue(encoding["labels"].max().item() <= 255				)

							# Test not batched input (PIL images)
							A__			, A__						=	prepare_semantic_single_inputs()

							A__						=	image_processing(lowercase					,					lowercase					,					return_tensors="pt"				)
							self.assertEqual(
							    encoding["pixel_values"].shape					,					(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)
							self.assertEqual(
							    encoding["labels"].shape					,					(
							        1,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)
							self.assertEqual(encoding["labels"].dtype					,					torch.long				)
							self.assertTrue(encoding["labels"].min().item() >= 0				)
							self.assertTrue(encoding["labels"].max().item() <= 255				)

							# Test batched input (PIL images)
							A__			, A__						=	prepare_semantic_batch_inputs()

							A__						=	image_processing(lowercase					,					lowercase					,					return_tensors="pt"				)
							self.assertEqual(
							    encoding["pixel_values"].shape					,					(
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)
							self.assertEqual(
							    encoding["labels"].shape					,					(
							        2,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )					,					)
							self.assertEqual(encoding["labels"].dtype					,					torch.long				)
							self.assertTrue(encoding["labels"].min().item() >= 0				)
							self.assertTrue(encoding["labels"].max().item() <= 255				)


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)

							# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
							A__			, A__						=	prepare_semantic_single_inputs()
							A__						=	image_processing(lowercase					,					lowercase					,					return_tensors="pt"				)
							self.assertTrue(encoding["labels"].min().item() >= 0				)
							self.assertTrue(encoding["labels"].max().item() <= 150				)

							A__						=	True
							A__						=	image_processing(lowercase					,					lowercase					,					return_tensors="pt"				)
							self.assertTrue(encoding["labels"].min().item() >= 0				)
							self.assertTrue(encoding["labels"].max().item() <= 255				)



import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Any       					=KandinskyVaaPriorPipeline
							lowercase_       :       str       					=['''prompt''']
							lowercase_       :       Union[str, Any]       					=['''prompt''', '''negative_prompt''']
							lowercase_       :       Dict       					=[
							    '''num_images_per_prompt''',
							    '''generator''',
							    '''num_inference_steps''',
							    '''latents''',
							    '''negative_prompt''',
							    '''guidance_scale''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       List[str]       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return self.time_input_dim




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




							@property
							def  A__      (				self):
												return 1_0_0




							@property
							def  A__      (				self):
												lowercase							=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
												return tokenizer




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						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  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 1_2,
												    '''embedding_dim''': self.text_embedder_hidden_size,
												    '''num_layers''': 1,
												}

												lowercase							=						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
												lowercase							=						nn.Parameter(torch.ones(model.clip_std.shape))
												return model




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						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   ,)

												lowercase							=						CLIPVisionModelWithProjection(A__)
												return model




							@property
							def  A__      (				self):
												lowercase							=						CLIPImageProcessor(
												    crop_size=2_2_4   ,do_center_crop=A__   ,do_normalize=A__   ,do_resize=A__   ,image_mean=[0.48145466, 0.4578275, 0.40821073]   ,image_std=[0.26862954, 0.26130258, 0.27577711]   ,resample=3   ,size=2_2_4   ,)

												return image_processor




							def  A__      (				self):
												lowercase							=						self.dummy_prior
												lowercase							=						self.dummy_image_encoder
												lowercase							=						self.dummy_text_encoder
												lowercase							=						self.dummy_tokenizer
												lowercase							=						self.dummy_image_processor

												lowercase							=						UnCLIPScheduler(
												    variance_type='''fixed_small_log'''   ,prediction_type='''sample'''   ,num_train_timesteps=1_0_0_0   ,clip_sample=A__   ,clip_sample_range=10.0   ,)

												lowercase							=						{
												    '''prior''': prior,
												    '''image_encoder''': image_encoder,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''scheduler''': scheduler,
												    '''image_processor''': image_processor,
												}

												return components




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''guidance_scale''': 4.0,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''np''',
												}
												return inputs




							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						pipe(**self.get_dummy_inputs(A__))
												lowercase							=						output.image_embeds

												lowercase							=						pipe(
												    **self.get_dummy_inputs(A__)   ,return_dict=A__   ,)[0]

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

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

												lowercase							=						np.array(
												    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156])

												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  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						True
												lowercase							=						False

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





							@skip_mps
							def  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						False

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




