


'''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 							__UpperCamelCase    (		unittest.TestCase ):




	def __init__(       self						,    __a						,    __a=7						,    __a=3						,    __a=18						,    __a=30						,    __a=400						,    __a=True						,    __a=None						,    __a=True						,    __a=None						,    __a=True						,    __a=[0.5, 0.5, 0.5]						,    __a=[0.5, 0.5, 0.5]						,    __a=False						,    ):

						'''simple docstring'''





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





	def 				__UpperCAmelCase							(       self							):

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

					__a      :	Dict    =  Image.open(dataset[0]['file']       )
					__a      :	int    =  Image.open(dataset[1]['file']       )

					return image, map






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

					__a      :	Union[str, Any]    =  Image.open(ds[0]['file']       )
					__a      :	str    =  Image.open(ds[1]['file']       )
					__a      :	List[str]    =  Image.open(ds[2]['file']       )
					__a      :	Any    =  Image.open(ds[3]['file']       )

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





@require_torch
@require_vision
class 							__UpperCamelCase    (		lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   BeitImageProcessor if is_vision_available() else None




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  BeitImageProcessingTester(self							)




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return self.image_processor_tester.prepare_image_processor_dict()




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  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							)

						__a      :	Optional[Any]    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						__a      :	Union[str, 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
						__a      :	Tuple    =  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      :	Dict    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						__a      :	str    =  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
						__a      :	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
						__a      :	Optional[int]    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						__a      :	Tuple    =  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
						__a      :	Dict    =  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      :	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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						# Test not batched input
						__a      :	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
						__a      :	int    =  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)
						__a   ,    __a      :	Union[str, Any]    =  prepare_semantic_single_inputs()

						__a      :	Any    =  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)
						__a   ,    __a      :	Tuple    =  prepare_semantic_batch_inputs()

						__a      :	Optional[int]    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	Union[str, Any]    =  True
						__a      :	Tuple    =  image_processing(__a						,    __a						,    return_tensors='pt'							)
						self.assertTrue(encoding['labels'].min().item() >= 0							)
						self.assertTrue(encoding['labels'].max().item() <= 255							)







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowercase     :						Union[str, Any]	=     {
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	pass

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						List[str]	=     [
	    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RoCBertForCausalLM',
	    'RoCBertForMaskedLM',
	    'RoCBertForMultipleChoice',
	    'RoCBertForPreTraining',
	    'RoCBertForQuestionAnswering',
	    'RoCBertForSequenceClassification',
	    'RoCBertForTokenClassification',
	    'RoCBertLayer',
	    'RoCBertModel',
	    'RoCBertPreTrainedModel',
	    'load_tf_weights_in_roc_bert',
	]

if TYPE_CHECKING:
	from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
	from .tokenization_roc_bert import RoCBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		raise OptionalDependencyNotAvailable()

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roc_bert import (
		    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoCBertForCausalLM,
		    RoCBertForMaskedLM,
		    RoCBertForMultipleChoice,
		    RoCBertForPreTraining,
		    RoCBertForQuestionAnswering,
		    RoCBertForSequenceClassification,
		    RoCBertForTokenClassification,
		    RoCBertLayer,
		    RoCBertModel,
		    RoCBertPreTrainedModel,
		    load_tf_weights_in_roc_bert,
		)


else:
	import sys

	__lowercase     :						Any	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



