
"""simple docstring"""






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    :Tuple		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Tuple=7		, lowerCamelCase_    :Union[str, Any]=3		, lowerCamelCase_    :Optional[int]=18		, lowerCamelCase_    :List[Any]=30		, lowerCamelCase_    :Tuple=4_00		, lowerCamelCase_    :Union[str, Any]=True		, lowerCamelCase_    :Dict=None		, lowerCamelCase_    :int=True		, lowerCamelCase_    :Optional[int]=None		, lowerCamelCase_    :Optional[Any]=True		, lowerCamelCase_    :Dict=[0.5, 0.5, 0.5]		, lowerCamelCase_    :str=[0.5, 0.5, 0.5]		, lowerCamelCase_    :Dict=False		, )							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= size if size is not None else {'''height''': 20, '''width''': 20}
			SCREAMING_SNAKE_CASE       :     Any				= crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
			SCREAMING_SNAKE_CASE       :     str				= parent
			SCREAMING_SNAKE_CASE       :     Any				= batch_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_channels
			SCREAMING_SNAKE_CASE       :     List[str]				= image_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= min_resolution
			SCREAMING_SNAKE_CASE       :     str				= max_resolution
			SCREAMING_SNAKE_CASE       :     Dict				= do_resize
			SCREAMING_SNAKE_CASE       :     Optional[int]				= size
			SCREAMING_SNAKE_CASE       :     Any				= do_center_crop
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= crop_size
			SCREAMING_SNAKE_CASE       :     Dict				= do_normalize
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= image_mean
			SCREAMING_SNAKE_CASE       :     int				= image_std
			SCREAMING_SNAKE_CASE       :     Optional[int]				= do_reduce_labels







	def   __lowerCAmelCase (						self    :int					)							->			Tuple:







			'''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 __A						(   )->							Union[str, Any]:




		'''simple docstring'''






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

		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= Image.open(dataset[0]['''file''']     )
		SCREAMING_SNAKE_CASE       :     Dict				= Image.open(dataset[1]['''file''']     )

		return image, map





def __A						(   )->							Dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= load_dataset('''hf-internal-testing/fixtures_ade20k'''		,		split='''test'''     )

		SCREAMING_SNAKE_CASE       :     Tuple				= Image.open(ds[0]['''file''']     )
		SCREAMING_SNAKE_CASE       :     int				= Image.open(ds[1]['''file''']     )
		SCREAMING_SNAKE_CASE       :     Any				= 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__(				_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  BeitImageProcessor if is_vision_available() else None





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= BeitImageProcessingTester(self					)





	@property
	def   __lowerCAmelCase (						self    :int					)							->			Optional[int]:







			'''simple docstring'''




			return self.image_processor_tester.prepare_image_processor_dict()





	def   __lowerCAmelCase (						self    :Any					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.image_processing_class(**self.image_processor_dict					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''do_resize'''					)					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''size'''					)					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''do_center_crop'''					)					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''center_crop'''					)					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''do_normalize'''					)					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''image_mean'''					)					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''image_std'''					)					)





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= 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		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     int				= self.image_processing_class.from_dict(
			    self.image_processor_dict		, size=42		, crop_size=84		, reduce_labels=lowerCamelCase_					)
			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		, lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :int					)							->			Tuple:







			'''simple docstring'''




			pass





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= self.image_processing_class(**self.image_processor_dict					)
			# create random PIL images
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowerCamelCase_					)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_		, Image.Image					)

			# Test not batched input
			SCREAMING_SNAKE_CASE       :     Union[str, 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.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )		, )

			# Test batched
			SCREAMING_SNAKE_CASE       :     Tuple				= 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.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )		, )





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= self.image_processing_class(**self.image_processor_dict					)
			# create random numpy tensors
			SCREAMING_SNAKE_CASE       :     List[Any]				= 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
			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       :     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.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )		, )





	def   __lowerCAmelCase (						self    :Any					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= 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=lowerCamelCase_		, torchify=lowerCamelCase_					)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_		, 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       :     Tuple				= 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.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )		, )





	def   __lowerCAmelCase (						self    :Tuple					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= 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=lowerCamelCase_		, torchify=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= []
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_		, torch.Tensor					)
					maps.append(torch.zeros(image.shape[-2:]					).long()					)

			# Test not batched input
			SCREAMING_SNAKE_CASE       :     List[str]				= 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() <= 2_55					)

			# Test batched
			SCREAMING_SNAKE_CASE       :     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.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() <= 2_55					)

			# Test not batched input (PIL images)
			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= prepare_semantic_single_inputs()

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= image_processing(lowerCamelCase_		, lowerCamelCase_		, 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() <= 2_55					)

			# Test batched input (PIL images)
			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= prepare_semantic_batch_inputs()

			SCREAMING_SNAKE_CASE       :     List[str]				= image_processing(lowerCamelCase_		, lowerCamelCase_		, 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() <= 2_55					)







	def   __lowerCAmelCase (						self    :List[str]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= 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,					SCREAMING_SNAKE_CASE       :     str				= prepare_semantic_single_inputs()
			SCREAMING_SNAKE_CASE       :     Any				= image_processing(lowerCamelCase_		, lowerCamelCase_		, return_tensors='''pt'''					)
			self.assertTrue(encoding['''labels'''].min().item() >= 0					)
			self.assertTrue(encoding['''labels'''].max().item() <= 1_50					)

			SCREAMING_SNAKE_CASE       :     int				= True
			SCREAMING_SNAKE_CASE       :     List[Any]				= image_processing(lowerCamelCase_		, lowerCamelCase_		, return_tensors='''pt'''					)
			self.assertTrue(encoding['''labels'''].min().item() >= 0					)
			self.assertTrue(encoding['''labels'''].max().item() <= 2_55					)







"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       str						     =			{
    "studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
    "studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """luke"""





	def __init__(						self    :Optional[int]		, lowerCamelCase_    :Union[str, Any]=5_02_67		, lowerCamelCase_    :int=50_00_00		, lowerCamelCase_    :Tuple=7_68		, lowerCamelCase_    :List[str]=2_56		, lowerCamelCase_    :Dict=12		, lowerCamelCase_    :Optional[int]=12		, lowerCamelCase_    :Optional[Any]=30_72		, lowerCamelCase_    :List[Any]="gelu"		, lowerCamelCase_    :Any=0.1		, lowerCamelCase_    :Union[str, Any]=0.1		, lowerCamelCase_    :List[str]=5_12		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Tuple=0.0_2		, lowerCamelCase_    :Optional[int]=1E-12		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :List[str]=None		, lowerCamelCase_    :str=1		, lowerCamelCase_    :Any=0		, lowerCamelCase_    :str=2		, **lowerCamelCase_    :List[Any]		, )							->			Optional[int]:







			'''simple docstring'''




			super().__init__(pad_token_id=lowerCamelCase_		, bos_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, **lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= vocab_size
			SCREAMING_SNAKE_CASE       :     List[str]				= entity_vocab_size
			SCREAMING_SNAKE_CASE       :     str				= hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= entity_emb_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= hidden_act
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= intermediate_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     Dict				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     Dict				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     Tuple				= type_vocab_size
			SCREAMING_SNAKE_CASE       :     str				= initializer_range
			SCREAMING_SNAKE_CASE       :     Dict				= layer_norm_eps
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_entity_aware_attention
			SCREAMING_SNAKE_CASE       :     List[str]				= classifier_dropout





