

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   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def __init__(			self  :					Any       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					int=7       ,						lowerCAmelCase__  :					List[str]=3       ,						lowerCAmelCase__  :					Optional[int]=18       ,						lowerCAmelCase__  :					List[Any]=30       ,						lowerCAmelCase__  :					List[str]=400       ,						lowerCAmelCase__  :					Dict=True       ,						lowerCAmelCase__  :					List[str]=None       ,						lowerCAmelCase__  :					str=True       ,						lowerCAmelCase__  :					str=None       ,						lowerCAmelCase__  :					List[Any]=True       ,						lowerCAmelCase__  :					Optional[int]=[0.5, 0.5, 0.5]       ,						lowerCAmelCase__  :					Any=[0.5, 0.5, 0.5]       ,						lowerCAmelCase__  :					Dict=False       ,						):
										SCREAMING_SNAKE_CASE_:			str  		=	size if size is not None else {'''height''': 20, '''width''': 20}
										SCREAMING_SNAKE_CASE_:			Tuple  		=	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
										SCREAMING_SNAKE_CASE_:			List[Any]  		=	parent
										SCREAMING_SNAKE_CASE_:			List[str]  		=	batch_size
										SCREAMING_SNAKE_CASE_:			str  		=	num_channels
										SCREAMING_SNAKE_CASE_:			List[Any]  		=	image_size
										SCREAMING_SNAKE_CASE_:			List[str]  		=	min_resolution
										SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max_resolution
										SCREAMING_SNAKE_CASE_:			Dict  		=	do_resize
										SCREAMING_SNAKE_CASE_:			List[str]  		=	size
										SCREAMING_SNAKE_CASE_:			Dict  		=	do_center_crop
										SCREAMING_SNAKE_CASE_:			int  		=	crop_size
										SCREAMING_SNAKE_CASE_:			int  		=	do_normalize
										SCREAMING_SNAKE_CASE_:			Dict  		=	image_mean
										SCREAMING_SNAKE_CASE_:			List[str]  		=	image_std
										SCREAMING_SNAKE_CASE_:			List[str]  		=	do_reduce_labels




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
										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       A_				(   ):
							SCREAMING_SNAKE_CASE_:			List[Any]  		=	load_dataset("hf-internal-testing/fixtures_ade20k"    ,     split="test"   )

							SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Image.open(dataset[0]["file"]   )
							SCREAMING_SNAKE_CASE_:			int  		=	Image.open(dataset[1]["file"]   )

							return image, map







def       A_				(   ):
							SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_dataset("hf-internal-testing/fixtures_ade20k"    ,     split="test"   )

							SCREAMING_SNAKE_CASE_:			Any  		=	Image.open(ds[0]["file"]   )
							SCREAMING_SNAKE_CASE_:			List[str]  		=	Image.open(ds[1]["file"]   )
							SCREAMING_SNAKE_CASE_:			Optional[int]  		=	Image.open(ds[2]["file"]   )
							SCREAMING_SNAKE_CASE_:			List[str]  		=	Image.open(ds[3]["file"]   )

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



@require_torch
@require_vision
class   __lowercase    (			__snake_case		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					BeitImageProcessor if is_vision_available() else None



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
										SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	BeitImageProcessingTester(self)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
										return self.image_processor_tester.prepare_image_processor_dict()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
										SCREAMING_SNAKE_CASE_:			str  		=	self.image_processing_class(**self.image_processor_dict)
										self.assertTrue(hasattr(a_       ,						"do_resize"))
										self.assertTrue(hasattr(a_       ,						"size"))
										self.assertTrue(hasattr(a_       ,						"do_center_crop"))
										self.assertTrue(hasattr(a_       ,						"center_crop"))
										self.assertTrue(hasattr(a_       ,						"do_normalize"))
										self.assertTrue(hasattr(a_       ,						"image_mean"))
										self.assertTrue(hasattr(a_       ,						"image_std"))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
										SCREAMING_SNAKE_CASE_:			Dict  		=	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       ,						a_)

										SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.image_processing_class.from_dict(
										    self.image_processor_dict       ,						size=42       ,						crop_size=84       ,						reduce_labels=a_)
										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       ,						a_)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
										pass



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
										SCREAMING_SNAKE_CASE_:			Dict  		=	self.image_processing_class(**self.image_processor_dict)
										# create random PIL images
										SCREAMING_SNAKE_CASE_:			Any  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=a_)
										for image in image_inputs:
																	self.assertIsInstance(a_       ,						Image.Image)

										# Test not batched input
										SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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
										SCREAMING_SNAKE_CASE_:			Any  		=	image_processing(a_       ,						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  :					Any):
										SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.image_processing_class(**self.image_processor_dict)
										# create random numpy tensors
										SCREAMING_SNAKE_CASE_:			Dict  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=a_       ,						numpify=a_)
										for image in image_inputs:
																	self.assertIsInstance(a_       ,						np.ndarray)

										# Test not batched input
										SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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
										SCREAMING_SNAKE_CASE_:			Tuple  		=	image_processing(a_       ,						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  :					Optional[int]):
										SCREAMING_SNAKE_CASE_:			Dict  		=	self.image_processing_class(**self.image_processor_dict)
										# create random PyTorch tensors
										SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=a_       ,						torchify=a_)
										for image in image_inputs:
																	self.assertIsInstance(a_       ,						torch.Tensor)

										# Test not batched input
										SCREAMING_SNAKE_CASE_:			int  		=	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
										SCREAMING_SNAKE_CASE_:			Any  		=	image_processing(a_       ,						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  :					Union[str, Any]):
										SCREAMING_SNAKE_CASE_:			List[str]  		=	self.image_processing_class(**self.image_processor_dict)
										# create random PyTorch tensors
										SCREAMING_SNAKE_CASE_:			str  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=a_       ,						torchify=a_)
										SCREAMING_SNAKE_CASE_:			Dict  		=	[]
										for image in image_inputs:
																	self.assertIsInstance(a_       ,						torch.Tensor)
																	maps.append(torch.zeros(image.shape[-2:]).long())

										# Test not batched input
										SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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
										SCREAMING_SNAKE_CASE_:			str  		=	image_processing(a_       ,						a_       ,						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)
										SCREAMING_SNAKE_CASE_:			List[Any]  		=	prepare_semantic_single_inputs()

										SCREAMING_SNAKE_CASE_:			List[str]  		=	image_processing(a_       ,						a_       ,						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)
										SCREAMING_SNAKE_CASE_:			Any  		=	prepare_semantic_batch_inputs()

										SCREAMING_SNAKE_CASE_:			Dict  		=	image_processing(a_       ,						a_       ,						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 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
										SCREAMING_SNAKE_CASE_:			Dict  		=	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
										SCREAMING_SNAKE_CASE_:			Dict  		=	prepare_semantic_single_inputs()
										SCREAMING_SNAKE_CASE_:			str  		=	image_processing(a_       ,						a_       ,						return_tensors="pt")
										self.assertTrue(encoding["labels"].min().item() >= 0)
										self.assertTrue(encoding["labels"].max().item() <= 150)

										SCREAMING_SNAKE_CASE_:			int  		=	True
										SCREAMING_SNAKE_CASE_:			Any  		=	image_processing(a_       ,						a_       ,						return_tensors="pt")
										self.assertTrue(encoding["labels"].min().item() >= 0)
										self.assertTrue(encoding["labels"].max().item() <= 255)









import torch

from diffusers import StableDiffusionPipeline


lowerCAmelCase							:  Any            =						"""path-to-your-trained-model"""
lowerCAmelCase							:  int            =						StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

lowerCAmelCase							:  Union[str, Any]            =						"""A photo of sks dog in a bucket"""
lowerCAmelCase							:  Any            =						pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






