
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   ):







	'''simple docstring'''





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





	def   __snake_case				(			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 				SCREAMING_SNAKE_CASE     (      ) ->       Any:

				"""simple docstring"""







				A__  :	int         =							load_dataset('''hf-internal-testing/fixtures_ade20k'''							,						split='''test'''       )

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

				return image, map

def 				SCREAMING_SNAKE_CASE     (      ) ->       Tuple:

				"""simple docstring"""







				A__  :	Tuple         =							load_dataset('''hf-internal-testing/fixtures_ade20k'''							,						split='''test'''       )

				A__  :	List[str]         =							Image.open(ds[0]['''file''']       )
				A__  :	int         =							Image.open(ds[1]['''file''']       )
				A__  :	Any         =							Image.open(ds[2]['''file''']       )
				A__  :	Dict         =							Image.open(ds[3]['''file''']       )

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







@require_torch
@require_vision
class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_,			unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				BeitImageProcessor if is_vision_available() else None





	def   __snake_case				(			self		):
					A__  :	str         =							BeitImageProcessingTester(self		)





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





	def   __snake_case				(			self		):
					A__  :	str         =							self.image_processing_class(**self.image_processor_dict		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_resize'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''size'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_center_crop'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''center_crop'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_normalize'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''image_mean'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''image_std'''		)		)





	def   __snake_case				(			self		):
					A__  :	Union[str, 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				,       UpperCamelCase__		)

					A__  :	Optional[Any]         =							self.image_processing_class.from_dict(
					    self.image_processor_dict				,       size=42				,       crop_size=84				,       reduce_labels=UpperCamelCase__		)
					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				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					pass





	def   __snake_case				(			self		):
					# Initialize image_processing
					A__  :	Union[str, Any]         =							self.image_processing_class(**self.image_processor_dict		)
					# create random PIL images
					A__  :	Any         =							prepare_image_inputs(self.image_processor_tester				,       equal_resolution=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__				,       Image.Image		)

					# 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__  :	List[Any]         =							image_processing(UpperCamelCase__				,       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   __snake_case				(			self		):
					# Initialize image_processing
					A__  :	str         =							self.image_processing_class(**self.image_processor_dict		)
					# create random numpy tensors
					A__  :	Dict         =							prepare_image_inputs(self.image_processor_tester				,       equal_resolution=UpperCamelCase__				,       numpify=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__				,       np.ndarray		)

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

					# Test batched
					A__  :	Dict         =							image_processing(UpperCamelCase__				,       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   __snake_case				(			self		):
					# Initialize image_processing
					A__  :	Optional[int]         =							self.image_processing_class(**self.image_processor_dict		)
					# create random PyTorch tensors
					A__  :	Union[str, Any]         =							prepare_image_inputs(self.image_processor_tester				,       equal_resolution=UpperCamelCase__				,       torchify=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__				,       torch.Tensor		)

					# 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__  :	Tuple         =							image_processing(UpperCamelCase__				,       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   __snake_case				(			self		):
					# Initialize image_processing
					A__  :	List[str]         =							self.image_processing_class(**self.image_processor_dict		)
					# create random PyTorch tensors
					A__  :	List[Any]         =							prepare_image_inputs(self.image_processor_tester				,       equal_resolution=UpperCamelCase__				,       torchify=UpperCamelCase__		)
					A__  :	int         =							[]
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__				,       torch.Tensor		)
									maps.append(torch.zeros(image.shape[-2:]		).long()		)

					# Test not batched input
					A__  :	Tuple         =							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__  :	Any         =							image_processing(UpperCamelCase__				,       UpperCamelCase__				,       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__  :	Optional[Any]         =							prepare_semantic_single_inputs()

					A__  :	Dict         =							image_processing(UpperCamelCase__				,       UpperCamelCase__				,       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__  :	List[str]         =							prepare_semantic_batch_inputs()

					A__  :	List[str]         =							image_processing(UpperCamelCase__				,       UpperCamelCase__				,       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   __snake_case				(			self		):
					# Initialize image_processing
					A__  :	Optional[Any]         =							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__  :	List[Any]         =							prepare_semantic_single_inputs()
					A__  :	int         =							image_processing(UpperCamelCase__				,       UpperCamelCase__				,       return_tensors='''pt'''		)
					self.assertTrue(encoding['''labels'''].min().item() >= 0		)
					self.assertTrue(encoding['''labels'''].max().item() <= 150		)

					A__  :	Optional[int]         =							True
					A__  :	str         =							image_processing(UpperCamelCase__				,       UpperCamelCase__				,       return_tensors='''pt'''		)
					self.assertTrue(encoding['''labels'''].min().item() >= 0		)
					self.assertTrue(encoding['''labels'''].max().item() <= 255		)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE    :      int          =			{
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			['BertTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			[
					    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'BertForMaskedLM',
					    'BertForMultipleChoice',
					    'BertForNextSentencePrediction',
					    'BertForPreTraining',
					    'BertForQuestionAnswering',
					    'BertForSequenceClassification',
					    'BertForTokenClassification',
					    'BertLayer',
					    'BertLMHeadModel',
					    'BertModel',
					    'BertPreTrainedModel',
					    'load_tf_weights_in_bert',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Tuple          =			[
					    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFBertEmbeddings',
					    'TFBertForMaskedLM',
					    'TFBertForMultipleChoice',
					    'TFBertForNextSentencePrediction',
					    'TFBertForPreTraining',
					    'TFBertForQuestionAnswering',
					    'TFBertForSequenceClassification',
					    'TFBertForTokenClassification',
					    'TFBertLMHeadModel',
					    'TFBertMainLayer',
					    'TFBertModel',
					    'TFBertPreTrainedModel',
					]
try:
					if not is_tensorflow_text_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Dict          =			['TFBertTokenizer']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE    :      Optional[int]          =			[
					    'FlaxBertForCausalLM',
					    'FlaxBertForMaskedLM',
					    'FlaxBertForMultipleChoice',
					    'FlaxBertForNextSentencePrediction',
					    'FlaxBertForPreTraining',
					    'FlaxBertForQuestionAnswering',
					    'FlaxBertForSequenceClassification',
					    'FlaxBertForTokenClassification',
					    'FlaxBertModel',
					    'FlaxBertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
					from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bert_fast import BertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bert import (
										    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BertForMaskedLM,
										    BertForMultipleChoice,
										    BertForNextSentencePrediction,
										    BertForPreTraining,
										    BertForQuestionAnswering,
										    BertForSequenceClassification,
										    BertForTokenClassification,
										    BertLayer,
										    BertLMHeadModel,
										    BertModel,
										    BertPreTrainedModel,
										    load_tf_weights_in_bert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_bert import (
										    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBertEmbeddings,
										    TFBertForMaskedLM,
										    TFBertForMultipleChoice,
										    TFBertForNextSentencePrediction,
										    TFBertForPreTraining,
										    TFBertForQuestionAnswering,
										    TFBertForSequenceClassification,
										    TFBertForTokenClassification,
										    TFBertLMHeadModel,
										    TFBertMainLayer,
										    TFBertModel,
										    TFBertPreTrainedModel,
										)

					try:
										if not is_tensorflow_text_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bert_tf import TFBertTokenizer

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_bert import (
										    FlaxBertForCausalLM,
										    FlaxBertForMaskedLM,
										    FlaxBertForMultipleChoice,
										    FlaxBertForNextSentencePrediction,
										    FlaxBertForPreTraining,
										    FlaxBertForQuestionAnswering,
										    FlaxBertForSequenceClassification,
										    FlaxBertForTokenClassification,
										    FlaxBertModel,
										    FlaxBertPreTrainedModel,
										)

else:
					import sys

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