

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     ):




				def __init__(   self ,  snake_case ,  snake_case=7 ,  snake_case=3 ,  snake_case=18 ,  snake_case=30 ,  snake_case=400 ,  snake_case=True ,  snake_case=None ,  snake_case=True ,  snake_case=None ,  snake_case=True ,  snake_case=[0.5, 0.5, 0.5] ,  snake_case=[0.5, 0.5, 0.5] ,  snake_case=False ,  ):
											snake_case_     							=       size if size is not None else {'height': 20, 'width': 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_reduce_labels




				def        a				(   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_reduce_labels": self.do_reduce_labels,
											}





def    __lowerCamelCase      (					):







							'''simple docstring'''






							snake_case_     							=       load_dataset('hf-internal-testing/fixtures_ade20k'  ,				split='test' )

							snake_case_     							=       Image.open(dataset[0]['file'] )
							snake_case_     							=       Image.open(dataset[1]['file'] )

							return image, map





def    __lowerCamelCase      (					):







							'''simple docstring'''






							snake_case_     							=       load_dataset('hf-internal-testing/fixtures_ade20k'  ,				split='test' )

							snake_case_     							=       Image.open(ds[0]['file'] )
							snake_case_     							=       Image.open(ds[1]['file'] )
							snake_case_     							=       Image.open(ds[2]['file'] )
							snake_case_     							=       Image.open(ds[3]['file'] )

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







@require_torch
@require_vision
class 					lowercase							(			lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					int			     =     BeitImageProcessor if is_vision_available() else None




				def        a				(   self			):
											snake_case_     							=       BeitImageProcessingTester(self			)




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




				def        a				(   self			):
											snake_case_     							=       self.image_processing_class(**self.image_processor_dict			)
											self.assertTrue(hasattr(snake_case ,  'do_resize'			)			)
											self.assertTrue(hasattr(snake_case ,  'size'			)			)
											self.assertTrue(hasattr(snake_case ,  'do_center_crop'			)			)
											self.assertTrue(hasattr(snake_case ,  'center_crop'			)			)
											self.assertTrue(hasattr(snake_case ,  'do_normalize'			)			)
											self.assertTrue(hasattr(snake_case ,  'image_mean'			)			)
											self.assertTrue(hasattr(snake_case ,  'image_std'			)			)




				def        a				(   self			):
											snake_case_     							=       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 ,  snake_case			)

											snake_case_     							=       self.image_processing_class.from_dict(
											    self.image_processor_dict ,  size=42 ,  crop_size=84 ,  reduce_labels=snake_case			)
											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 ,  snake_case			)




				def        a				(   self			):
											pass




				def        a				(   self			):
											# Initialize image_processing
											snake_case_     							=       self.image_processing_class(**self.image_processor_dict			)
											# create random PIL images
											snake_case_     							=       prepare_image_inputs(self.image_processor_tester ,  equal_resolution=snake_case			)
											for image in image_inputs:
																		self.assertIsInstance(snake_case ,  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(snake_case ,  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        a				(   self			):
											# Initialize image_processing
											snake_case_     							=       self.image_processing_class(**self.image_processor_dict			)
											# create random numpy tensors
											snake_case_     							=       prepare_image_inputs(self.image_processor_tester ,  equal_resolution=snake_case ,  numpify=snake_case			)
											for image in image_inputs:
																		self.assertIsInstance(snake_case ,  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(snake_case ,  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        a				(   self			):
											# Initialize image_processing
											snake_case_     							=       self.image_processing_class(**self.image_processor_dict			)
											# create random PyTorch tensors
											snake_case_     							=       prepare_image_inputs(self.image_processor_tester ,  equal_resolution=snake_case ,  torchify=snake_case			)
											for image in image_inputs:
																		self.assertIsInstance(snake_case ,  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(snake_case ,  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        a				(   self			):
											# Initialize image_processing
											snake_case_     							=       self.image_processing_class(**self.image_processor_dict			)
											# create random PyTorch tensors
											snake_case_     							=       prepare_image_inputs(self.image_processor_tester ,  equal_resolution=snake_case ,  torchify=snake_case			)
											snake_case_     							=       []
											for image in image_inputs:
																		self.assertIsInstance(snake_case ,  torch.Tensor			)
																		maps.append(torch.zeros(image.shape[-2:]			).long()			)

											# Test not batched input
											snake_case_     							=       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
											snake_case_     							=       image_processing(snake_case ,  snake_case ,  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)
											snake_case_							,       snake_case_     							=       prepare_semantic_single_inputs()

											snake_case_     							=       image_processing(snake_case ,  snake_case ,  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)
											snake_case_							,       snake_case_     							=       prepare_semantic_batch_inputs()

											snake_case_     							=       image_processing(snake_case ,  snake_case ,  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        a				(   self			):
											# Initialize image_processing
											snake_case_     							=       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
											snake_case_							,       snake_case_     							=       prepare_semantic_single_inputs()
											snake_case_     							=       image_processing(snake_case ,  snake_case ,  return_tensors='pt'			)
											self.assertTrue(encoding['labels'].min().item() >= 0			)
											self.assertTrue(encoding['labels'].max().item() <= 150			)

											snake_case_     							=       True
											snake_case_     							=       image_processing(snake_case ,  snake_case ,  return_tensors='pt'			)
											self.assertTrue(encoding['labels'].min().item() >= 0			)
											self.assertTrue(encoding['labels'].max().item() <= 255			)







_UpperCAmelCase						:  Any									=			6_5521





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       1
							snake_case_     							=       0
							for plain_chr in plain_text:
														snake_case_     							=       (a + ord(UpperCamelCase__ )) % MOD_ADLER
														snake_case_     							=       (b + a) % MOD_ADLER
							return (b << 16) | a




