


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 SegformerImageProcessor


class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





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





	def       snake_case__(	self:	Any ):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_reduce_labels": self.do_reduce_labels,
			}


def 			SCREAMING_SNAKE_CASE__     (					)      ->       Tuple:




		'''simple docstring'''
		lowercase__				:			Any            =		load_dataset('hf-internal-testing/fixtures_ade20k'      ,						split='test' )

		lowercase__				:			List[Any]            =		Image.open(dataset[0]['file'] )
		lowercase__				:			Optional[int]            =		Image.open(dataset[1]['file'] )

		return image, map


def 			SCREAMING_SNAKE_CASE__     (					)      ->       List[Any]:




		'''simple docstring'''
		lowercase__				:			Dict            =		load_dataset('hf-internal-testing/fixtures_ade20k'      ,						split='test' )

		lowercase__				:			Optional[Any]            =		Image.open(dataset[0]['file'] )
		lowercase__				:			Any            =		Image.open(dataset[1]['file'] )
		lowercase__				:			List[Any]            =		Image.open(dataset[2]['file'] )
		lowercase__				:			Tuple            =		Image.open(dataset[3]['file'] )

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





@require_torch
@require_vision
class 			__lowerCAmelCase				(					_UpperCamelCase      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							SegformerImageProcessor if is_vision_available() else None

	def       snake_case__(	self:	Optional[int] ):
			lowercase__				:			str            =		SegformerImageProcessingTester(self )

	@property
	def       snake_case__(	self:	Union[str, Any] ):
			return self.image_processor_tester.prepare_image_processor_dict()

	def       snake_case__(	self:	Tuple ):
			lowercase__				:			Optional[int]            =		self.image_processing_class(**self.image_processor_dict )
			self.assertTrue(hasattr(lowerCamelCase_,				'do_resize' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'size' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'do_normalize' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'image_mean' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'image_std' ) )
			self.assertTrue(hasattr(lowerCamelCase_,				'do_reduce_labels' ) )

	def       snake_case__(	self:	Any ):
			lowercase__				:			List[str]            =		self.image_processing_class.from_dict(self.image_processor_dict )
			self.assertEqual(image_processor.size,				{'height': 30, 'width': 30} )
			self.assertEqual(image_processor.do_reduce_labels,				lowerCamelCase_ )

			lowercase__				:			int            =		self.image_processing_class.from_dict(self.image_processor_dict,				size=42,				reduce_labels=lowerCamelCase_ )
			self.assertEqual(image_processor.size,				{'height': 42, 'width': 42} )
			self.assertEqual(image_processor.do_reduce_labels,				lowerCamelCase_ )

	def       snake_case__(	self:	List[str] ):
			pass

	def       snake_case__(	self:	Dict ):
			lowercase__				:			Any            =		self.image_processing_class(**self.image_processor_dict )
			# create random PIL images
			lowercase__				:			str            =		prepare_image_inputs(self.image_processor_tester,				equal_resolution=lowerCamelCase_ )
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,				Image.Image )

			# Test not batched input
			lowercase__				:			Optional[Any]            =		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.size['height'],
			        self.image_processor_tester.size['width'],
			    ),				)

			# Test batched
			lowercase__				:			Union[str, Any]            =		image_processing(lowerCamelCase_,				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.size['height'],
			        self.image_processor_tester.size['width'],
			    ),				)

	def       snake_case__(	self:	Optional[Any] ):
			lowercase__				:			Optional[Any]            =		self.image_processing_class(**self.image_processor_dict )
			# create random numpy tensors
			lowercase__				:			Tuple            =		prepare_image_inputs(self.image_processor_tester,				equal_resolution=lowerCamelCase_,				numpify=lowerCamelCase_ )
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,				np.ndarray )

			# Test not batched input
			lowercase__				:			List[Any]            =		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.size['height'],
			        self.image_processor_tester.size['width'],
			    ),				)

			# Test batched
			lowercase__				:			Dict            =		image_processing(lowerCamelCase_,				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.size['height'],
			        self.image_processor_tester.size['width'],
			    ),				)

	def       snake_case__(	self:	Tuple ):
			lowercase__				:			int            =		self.image_processing_class(**self.image_processor_dict )
			# create random PyTorch tensors
			lowercase__				:			int            =		prepare_image_inputs(self.image_processor_tester,				equal_resolution=lowerCamelCase_,				torchify=lowerCamelCase_ )
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,				torch.Tensor )

			# Test not batched input
			lowercase__				:			Any            =		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.size['height'],
			        self.image_processor_tester.size['width'],
			    ),				)

			# Test batched
			lowercase__				:			Dict            =		image_processing(lowerCamelCase_,				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.size['height'],
			        self.image_processor_tester.size['width'],
			    ),				)

	def       snake_case__(	self:	int ):
			lowercase__				:			List[str]            =		self.image_processing_class(**self.image_processor_dict )
			# create random PyTorch tensors
			lowercase__				:			Any            =		prepare_image_inputs(self.image_processor_tester,				equal_resolution=lowerCamelCase_,				torchify=lowerCamelCase_ )
			lowercase__				:			List[Any]            =		[]
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,				torch.Tensor )
					maps.append(torch.zeros(image.shape[-2:] ).long() )

			# Test not batched input
			lowercase__				:			Dict            =		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.size['height'],
			        self.image_processor_tester.size['width'],
			    ),				)
			self.assertEqual(
			    encoding['labels'].shape,				(
			        1,
			        self.image_processor_tester.size['height'],
			        self.image_processor_tester.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
			lowercase__				:			List[str]            =		image_processing(lowerCamelCase_,				lowerCamelCase_,				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.size['height'],
			        self.image_processor_tester.size['width'],
			    ),				)
			self.assertEqual(
			    encoding['labels'].shape,				(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.size['height'],
			        self.image_processor_tester.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)
			lowercase__      ,							lowercase__				:			List[str]            =		prepare_semantic_single_inputs()

			lowercase__				:			Tuple            =		image_processing(lowerCamelCase_,				lowerCamelCase_,				return_tensors='pt' )
			self.assertEqual(
			    encoding['pixel_values'].shape,				(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['height'],
			        self.image_processor_tester.size['width'],
			    ),				)
			self.assertEqual(
			    encoding['labels'].shape,				(
			        1,
			        self.image_processor_tester.size['height'],
			        self.image_processor_tester.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)
			lowercase__      ,							lowercase__				:			Tuple            =		prepare_semantic_batch_inputs()

			lowercase__				:			Union[str, Any]            =		image_processing(lowerCamelCase_,				lowerCamelCase_,				return_tensors='pt' )
			self.assertEqual(
			    encoding['pixel_values'].shape,				(
			        2,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['height'],
			        self.image_processor_tester.size['width'],
			    ),				)
			self.assertEqual(
			    encoding['labels'].shape,				(
			        2,
			        self.image_processor_tester.size['height'],
			        self.image_processor_tester.size['width'],
			    ),				)
			self.assertEqual(encoding['labels'].dtype,				torch.long )
			self.assertTrue(encoding['labels'].min().item() >= 0 )
			self.assertTrue(encoding['labels'].max().item() <= 255 )





	def       snake_case__(	self:	int ):
			lowercase__				:			List[str]            =		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
			lowercase__      ,							lowercase__				:			Tuple            =		prepare_semantic_single_inputs()
			lowercase__				:			int            =		image_processing(lowerCamelCase_,				lowerCamelCase_,				return_tensors='pt' )
			self.assertTrue(encoding['labels'].min().item() >= 0 )
			self.assertTrue(encoding['labels'].max().item() <= 150 )

			lowercase__				:			List[str]            =		True
			lowercase__				:			Optional[Any]            =		image_processing(lowerCamelCase_,				lowerCamelCase_,				return_tensors='pt' )
			self.assertTrue(encoding['labels'].min().item() >= 0 )
			self.assertTrue(encoding['labels'].max().item() <= 255 )









from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase			):


	'''simple docstring'''





	def __init__(	self:	Union[str, Any] ):
			super().__init__()

			self.register_buffer('watermark_image',				torch.zeros((62, 62, 4) ) )
			lowercase__				:			int            =		None





	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	List[PIL.Image.Image],				lowerCamelCase_:	Dict=None ):
			# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

			lowercase__				:			str            =		images[0].height
			lowercase__				:			Optional[Any]            =		images[0].width

			lowercase__				:			List[Any]            =		sample_size or h

			lowercase__				:			Any            =		min(h / sample_size,				w / sample_size )
			lowercase__      ,							lowercase__				:			Optional[int]            =		(int(h / coef ), int(w / coef )) if coef < 1 else (h, w)

			lowercase__      ,							lowercase__				:			Any            =		1024**2, img_w * img_h
			lowercase__				:			Union[str, Any]            =		(Sa / Sa) ** 0.5
			lowercase__      ,							lowercase__      ,							lowercase__				:			Optional[int]            =		int(K * 62 ), img_w - int(14 * K ), img_h - int(14 * K )

			if self.watermark_image_as_pil is None:
					lowercase__				:			Tuple            =		self.watermark_image.to(torch.uinta ).cpu().numpy()
					lowercase__				:			str            =		Image.fromarray(lowerCamelCase_,				mode='RGBA' )
					lowercase__				:			Tuple            =		watermark_image

			lowercase__				:			Optional[int]            =		self.watermark_image_as_pil.resize(
			    (wm_size, wm_size),				PIL_INTERPOLATION['bicubic'],				reducing_gap=lowerCamelCase_ )

			for pil_img in images:
					pil_img.paste(lowerCamelCase_,				box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y),				mask=wm_img.split()[-1] )

			return images


