
"""simple docstring"""




import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer




class      UpperCamelCase	(       tf.keras.layers.Layer      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = "longest"		,	lowercase__ = True		,	lowercase__ = 512		,	lowercase__ = None		,	lowercase__ = True		,	lowercase__ = True		,	lowercase__ = True		,	**lowercase__		,	)    ->    Optional[int]:
									"""simple docstring"""







									super().__init__()
									if use_fast_bert_tokenizer:
														SCREAMING_SNAKE_CASE				=	FastBertTokenizer(
														    lowercase__		,	token_out_type=tf.intaa		,	lower_case_nfd_strip_accents=lowercase__		,	**lowercase__			)
									else:
														SCREAMING_SNAKE_CASE				=	tf.lookup.StaticVocabularyTable(
														    tf.lookup.KeyValueTensorInitializer(
														        keys=lowercase__		,	key_dtype=tf.string		,	values=tf.range(tf.size(lowercase__		,	out_type=tf.intaa			)		,	dtype=tf.intaa			)		,	value_dtype=tf.intaa		,	)		,	num_oov_buckets=1		,	)
														SCREAMING_SNAKE_CASE				=	BertTokenizerLayer(
														    lowercase__		,	token_out_type=tf.intaa		,	lower_case=lowercase__		,	**lowercase__			)

									SCREAMING_SNAKE_CASE				=	vocab_list
									SCREAMING_SNAKE_CASE				=	do_lower_case
									SCREAMING_SNAKE_CASE				=	cls_token_id or vocab_list.index('[CLS]'			)
									SCREAMING_SNAKE_CASE				=	sep_token_id or vocab_list.index('[SEP]'			)
									SCREAMING_SNAKE_CASE				=	pad_token_id or vocab_list.index('[PAD]'			)
									SCREAMING_SNAKE_CASE				=	ShrinkLongestTrimmer(max_length - 3		,	axis=1			)  # Allow room for special tokens
									SCREAMING_SNAKE_CASE				=	max_length
									SCREAMING_SNAKE_CASE				=	padding
									SCREAMING_SNAKE_CASE				=	truncation
									SCREAMING_SNAKE_CASE				=	pad_to_multiple_of
									SCREAMING_SNAKE_CASE				=	return_token_type_ids
									SCREAMING_SNAKE_CASE				=	return_attention_mask


				@classmethod
				def 							A					(				cls		,	lowercase__		,	**lowercase__			)    ->    List[Any]:  # noqa: F821
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	kwargs.pop('do_lower_case'		,	lowercase__			)
									SCREAMING_SNAKE_CASE				=	tokenizer.do_lower_case if do_lower_case is None else do_lower_case
									SCREAMING_SNAKE_CASE				=	kwargs.pop('cls_token_id'		,	lowercase__			)
									SCREAMING_SNAKE_CASE				=	tokenizer.cls_token_id if cls_token_id is None else cls_token_id
									SCREAMING_SNAKE_CASE				=	kwargs.pop('sep_token_id'		,	lowercase__			)
									SCREAMING_SNAKE_CASE				=	tokenizer.sep_token_id if sep_token_id is None else sep_token_id
									SCREAMING_SNAKE_CASE				=	kwargs.pop('pad_token_id'		,	lowercase__			)
									SCREAMING_SNAKE_CASE				=	tokenizer.pad_token_id if pad_token_id is None else pad_token_id

									SCREAMING_SNAKE_CASE				=	tokenizer.get_vocab()
									SCREAMING_SNAKE_CASE				=	sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]		,	key=lambda lowercase__			: x[1]			)
									SCREAMING_SNAKE_CASE				=	[entry[0] for entry in vocab]
									return cls(
									    vocab_list=lowercase__		,	do_lower_case=lowercase__		,	cls_token_id=lowercase__		,	sep_token_id=lowercase__		,	pad_token_id=lowercase__		,	**lowercase__		,	)


				@classmethod
				def 							A					(				cls		,	lowercase__		,	*lowercase__		,	**lowercase__			)    ->    List[Any]:
									"""simple docstring"""







									try:
														SCREAMING_SNAKE_CASE				=	BertTokenizer.from_pretrained(lowercase__		,	*lowercase__		,	**lowercase__			)
									except:  # noqa: E722
														from .tokenization_bert_fast import BertTokenizerFast

														SCREAMING_SNAKE_CASE				=	BertTokenizerFast.from_pretrained(lowercase__		,	*lowercase__		,	**lowercase__			)
									return cls.from_tokenizer(lowercase__		,	**lowercase__			)


				def 							A					(				self		,	lowercase__			)    ->    Tuple:
									"""simple docstring"""







									if self.do_lower_case:
														SCREAMING_SNAKE_CASE				=	case_fold_utfa(lowercase__			)
									SCREAMING_SNAKE_CASE				=	self.tf_tokenizer.tokenize(lowercase__			)
									return tokens.merge_dims(1		,	-1			)


				def 							A					(				self		,	lowercase__		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	)    ->    List[Any]:
									"""simple docstring"""







									if padding is None:
														SCREAMING_SNAKE_CASE				=	self.padding
									if padding not in ("longest", "max_length"):
														raise ValueError('Padding must be either \'longest\' or \'max_length\'!'			)
									if max_length is not None and text_pair is not None:
														# Because we have to instantiate a Trimmer to do it properly
														raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'			)
									if max_length is None:
														SCREAMING_SNAKE_CASE				=	self.max_length
									if truncation is None:
														SCREAMING_SNAKE_CASE				=	self.truncation
									if pad_to_multiple_of is None:
														SCREAMING_SNAKE_CASE				=	self.pad_to_multiple_of
									if return_token_type_ids is None:
														SCREAMING_SNAKE_CASE				=	self.return_token_type_ids
									if return_attention_mask is None:
														SCREAMING_SNAKE_CASE				=	self.return_attention_mask
									if not isinstance(lowercase__		,	tf.Tensor			):
														SCREAMING_SNAKE_CASE				=	tf.convert_to_tensor(lowercase__			)
									if text_pair is not None and not isinstance(lowercase__		,	tf.Tensor			):
														SCREAMING_SNAKE_CASE				=	tf.convert_to_tensor(lowercase__			)
									if text_pair is not None:
														if text.shape.rank > 1:
																			raise ValueError('text argument should not be multidimensional when a text pair is supplied!'			)
														if text_pair.shape.rank > 1:
																			raise ValueError('text_pair should not be multidimensional!'			)
									if text.shape.rank == 2:
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	text[:, 0], text[:, 1]
									SCREAMING_SNAKE_CASE				=	self.unpaired_tokenize(lowercase__			)
									if text_pair is None:  # Unpaired text
														if truncation:
																			SCREAMING_SNAKE_CASE				=	text[:, : max_length - 2]  # Allow room for special tokens
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	combine_segments(
														    (text,)		,	start_of_sequence_id=self.cls_token_id		,	end_of_segment_id=self.sep_token_id			)
									else:  # Paired text
														SCREAMING_SNAKE_CASE				=	self.unpaired_tokenize(lowercase__			)
														if truncation:
																			SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self.paired_trimmer.trim([text, text_pair]			)
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	combine_segments(
														    (text, text_pair)		,	start_of_sequence_id=self.cls_token_id		,	end_of_segment_id=self.sep_token_id			)
									if padding == "longest":
														SCREAMING_SNAKE_CASE				=	input_ids.bounding_shape(axis=1			)
														if pad_to_multiple_of is not None:
																			# No ceiling division in tensorflow, so we negate floordiv instead
																			SCREAMING_SNAKE_CASE				=	pad_to_multiple_of * (-tf.math.floordiv(-pad_length		,	lowercase__			))
									else:
														SCREAMING_SNAKE_CASE				=	max_length

									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	pad_model_inputs(lowercase__		,	max_seq_length=lowercase__		,	pad_value=self.pad_token_id			)
									SCREAMING_SNAKE_CASE				=	{'input_ids': input_ids}
									if return_attention_mask:
														SCREAMING_SNAKE_CASE				=	attention_mask
									if return_token_type_ids:
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	pad_model_inputs(
														    lowercase__		,	max_seq_length=lowercase__		,	pad_value=self.pad_token_id			)
														SCREAMING_SNAKE_CASE				=	token_type_ids
									return output




				def 							A					(				self			)    ->    List[str]:
									"""simple docstring"""







									return {
									    "vocab_list": self.vocab_list,
									    "do_lower_case": self.do_lower_case,
									    "cls_token_id": self.cls_token_id,
									    "sep_token_id": self.sep_token_id,
									    "pad_token_id": self.pad_token_id,
									}


"""simple docstring"""

import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
					from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DPTImageProcessor






class        _lowercase   :




	def __init__( self		, UpperCAmelCase_		, UpperCAmelCase_=2		, UpperCAmelCase_=32		, UpperCAmelCase_=16		, UpperCAmelCase_=3		, UpperCAmelCase_=True		, UpperCAmelCase_=True		, UpperCAmelCase_=32		, UpperCAmelCase_=4		, UpperCAmelCase_=[0, 1, 2, 3]		, UpperCAmelCase_=4		, UpperCAmelCase_=37		, UpperCAmelCase_="gelu"		, UpperCAmelCase_=0.1		, UpperCAmelCase_=0.1		, UpperCAmelCase_=0.02		, UpperCAmelCase_=3		, UpperCAmelCase_=False		, UpperCAmelCase_=None		, )		->							List[str]:
			lowerCamelCase     :			List[str]		=    parent
			lowerCamelCase     :			Optional[int]		=    batch_size
			lowerCamelCase     :			Union[str, Any]		=    image_size
			lowerCamelCase     :			str		=    patch_size
			lowerCamelCase     :			Optional[Any]		=    num_channels
			lowerCamelCase     :			Tuple		=    is_training
			lowerCamelCase     :			int		=    use_labels
			lowerCamelCase     :			Union[str, Any]		=    hidden_size
			lowerCamelCase     :			List[Any]		=    num_hidden_layers
			lowerCamelCase     :			List[str]		=    backbone_out_indices
			lowerCamelCase     :			str		=    num_attention_heads
			lowerCamelCase     :			str		=    intermediate_size
			lowerCamelCase     :			str		=    hidden_act
			lowerCamelCase     :			Union[str, Any]		=    hidden_dropout_prob
			lowerCamelCase     :			Tuple		=    attention_probs_dropout_prob
			lowerCamelCase     :			List[Any]		=    initializer_range
			lowerCamelCase     :			Any		=    num_labels
			lowerCamelCase     :			Optional[Any]		=    scope
			lowerCamelCase     :			List[str]		=    is_hybrid
			# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
			lowerCamelCase     :			Optional[int]		=    (image_size // patch_size) ** 2
			lowerCamelCase     :			Tuple		=    num_patches + 1




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Optional[Any]		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			lowerCamelCase     :			Any		=    None
			if self.use_labels:
					lowerCamelCase     :			List[Any]		=    ids_tensor([self.batch_size, self.image_size, self.image_size]		, self.num_labels					)

			lowerCamelCase     :			Union[str, Any]		=    self.get_config()

			return config, pixel_values, labels




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			return DPTConfig(
			    image_size=self.image_size		, patch_size=self.patch_size		, num_channels=self.num_channels		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, backbone_out_indices=self.backbone_out_indices		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, is_decoder=UpperCAmelCase_		, initializer_range=self.initializer_range		, is_hybrid=self.is_hybrid		, )




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Union[str, Any]:
			lowerCamelCase     :			Dict		=    DPTModel(config=UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			List[Any]		=    model(UpperCAmelCase_					)
			self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							List[Any]:
			lowerCamelCase     :			str		=    self.num_labels
			lowerCamelCase     :			Optional[int]		=    DPTForDepthEstimation(UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			str		=    model(UpperCAmelCase_					)
			self.parent.assertEqual(result.predicted_depth.shape		, (self.batch_size, self.image_size, self.image_size)					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Tuple:
			lowerCamelCase     :			Optional[Any]		=    self.num_labels
			lowerCamelCase     :			str		=    DPTForSemanticSegmentation(UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			List[Any]		=    model(UpperCAmelCase_		, labels=UpperCAmelCase_					)
			self.parent.assertEqual(
			    result.logits.shape		, (self.batch_size, self.num_labels, self.image_size, self.image_size)					)




	def 			_UpperCamelCase    ( self					)		->							Dict:
			lowerCamelCase     :			int		=    self.prepare_config_and_inputs()
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			List[str]		=    config_and_inputs
			lowerCamelCase     :			int		=    {'pixel_values': pixel_values}
			return config, inputs_dict






@require_torch
class        _lowercase   (				__UpperCAmelCase				,       __UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	lowercase_        =	(
	    {
	        'depth-estimation': DPTForDepthEstimation,
	        'feature-extraction': DPTModel,
	        'image-segmentation': DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	lowercase_        =	False
	lowercase_        =	False
	lowercase_        =	False




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Optional[Any]		=    DPTModelTester(self					)
			lowerCamelCase     :			Optional[int]		=    ConfigTester(self		, config_class=UpperCAmelCase_		, has_text_modality=UpperCAmelCase_		, hidden_size=37					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			self.config_tester.run_common_tests()




	@unittest.skip(reason='DPT does not use inputs_embeds'					)
	def 			_UpperCamelCase    ( self					)		->							int:
			pass




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCamelCase     :			Union[str, Any]		=    model_class(UpperCAmelCase_					)
					self.assertIsInstance(model.get_input_embeddings()		, (nn.Module)					)
					lowerCamelCase     :			Optional[int]		=    model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(UpperCAmelCase_		, nn.Linear					)					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase	,    lowerCamelCase     :			str		=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCamelCase     :			List[Any]		=    model_class(UpperCAmelCase_					)
					lowerCamelCase     :			List[str]		=    inspect.signature(model.forward					)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					lowerCamelCase     :			Tuple		=    [*signature.parameters.keys()]

					lowerCamelCase     :			Any		=    ['pixel_values']
					self.assertListEqual(arg_names[:1]		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			str		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Tuple		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_depth_estimation(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			int		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			for model_class in self.all_model_classes:
					if model_class.__name__ == "DPTForDepthEstimation":
							continue

					lowerCamelCase	,    lowerCamelCase     :			str		=    self.model_tester.prepare_config_and_inputs_for_common()
					lowerCamelCase     :			Optional[int]		=    True

					if model_class in get_values(UpperCAmelCase_					):
							continue

					lowerCamelCase     :			Tuple		=    model_class(UpperCAmelCase_					)
					model.to(UpperCAmelCase_					)
					model.train()
					lowerCamelCase     :			Tuple		=    self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_		, return_labels=UpperCAmelCase_					)
					lowerCamelCase     :			int		=    model(**UpperCAmelCase_					).loss
					loss.backward()




	def 			_UpperCamelCase    ( self					)		->							str:
			for model_class in self.all_model_classes:
					if model_class.__name__ == "DPTForDepthEstimation":
							continue

					lowerCamelCase	,    lowerCamelCase     :			List[Any]		=    self.model_tester.prepare_config_and_inputs_for_common()
					lowerCamelCase     :			List[str]		=    False
					lowerCamelCase     :			int		=    True

					if model_class in get_values(UpperCAmelCase_					) or not model_class.supports_gradient_checkpointing:
							continue
					lowerCamelCase     :			Optional[int]		=    model_class(UpperCAmelCase_					)
					model.to(UpperCAmelCase_					)
					model.gradient_checkpointing_enable()
					model.train()
					lowerCamelCase     :			Optional[Any]		=    self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_		, return_labels=UpperCAmelCase_					)
					lowerCamelCase     :			str		=    model(**UpperCAmelCase_					).loss
					loss.backward()




	def 			_UpperCamelCase    ( self					)		->							Dict:
			lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    self.model_tester.prepare_config_and_inputs_for_common()

			lowerCamelCase     :			Union[str, Any]		=    _config_zero_init(UpperCAmelCase_					)
			for model_class in self.all_model_classes:
					lowerCamelCase     :			List[str]		=    model_class(config=UpperCAmelCase_					)
					# Skip the check for the backbone
					lowerCamelCase     :			Optional[int]		=    []
					for name, module in model.named_modules():
							if module.__class__.__name__ == "DPTViTHybridEmbeddings":
									lowerCamelCase     :			Optional[int]		=    [F"""{name}.{key}""" for key in module.state_dict().keys()]
									break

					for name, param in model.named_parameters():
							if param.requires_grad:
									if name in backbone_params:
											continue
									self.assertIn(
									    ((param.data.mean() * 1E9).round() / 1E9).item()		, [0.0, 1.0]		, msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""		, )




	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'					)
	def 			_UpperCamelCase    ( self					)		->							Dict:
			pass




	@slow
	def 			_UpperCamelCase    ( self					)		->							str:
			for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase     :			List[str]		=    DPTModel.from_pretrained(UpperCAmelCase_					)
					self.assertIsNotNone(UpperCAmelCase_					)


def 		UpperCAmelCase					(		):





		'''simple docstring'''


		lowerCamelCase     :			int		=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image






@require_torch
@require_vision
@slow
class        _lowercase   (				unittest.TestCase		):




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			int		=    DPTImageProcessor.from_pretrained('Intel/dpt-large'					)
			lowerCamelCase     :			List[str]		=    DPTForDepthEstimation.from_pretrained('Intel/dpt-large'					).to(UpperCAmelCase_					)

			lowerCamelCase     :			str		=    prepare_img()
			lowerCamelCase     :			Tuple		=    image_processor(images=UpperCAmelCase_		, return_tensors='pt'					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					lowerCamelCase     :			Dict		=    model(**UpperCAmelCase_					)
					lowerCamelCase     :			str		=    outputs.predicted_depth

			# verify the predicted depth
			lowerCamelCase     :			List[str]		=    torch.Size((1, 384, 384)					)
			self.assertEqual(predicted_depth.shape		, UpperCAmelCase_					)

			lowerCamelCase     :			Dict		=    torch.tensor(
			    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]					).to(UpperCAmelCase_					)

			self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]		, UpperCAmelCase_		, atol=1E-4					)					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			int		=    DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'					)
			lowerCamelCase     :			Optional[Any]		=    DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'					).to(UpperCAmelCase_					)

			lowerCamelCase     :			List[Any]		=    prepare_img()
			lowerCamelCase     :			Tuple		=    image_processor(images=UpperCAmelCase_		, return_tensors='pt'					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					lowerCamelCase     :			List[str]		=    model(**UpperCAmelCase_					)

			# verify the logits
			lowerCamelCase     :			int		=    torch.Size((1, 150, 480, 480)					)
			self.assertEqual(outputs.logits.shape		, UpperCAmelCase_					)

			lowerCamelCase     :			Tuple		=    torch.tensor(
			    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]					).to(UpperCAmelCase_					)

			self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]		, UpperCAmelCase_		, atol=1E-4					)					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			Dict		=    DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'					)
			lowerCamelCase     :			str		=    DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'					).to(UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    prepare_img()
			lowerCamelCase     :			Any		=    image_processor(images=UpperCAmelCase_		, return_tensors='pt'					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					lowerCamelCase     :			Dict		=    model(**UpperCAmelCase_					)

			lowerCamelCase     :			int		=    outputs.logits.detach().cpu()

			lowerCamelCase     :			int		=    image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_		, target_sizes=[(500, 300)]					)
			lowerCamelCase     :			Optional[Any]		=    torch.Size((500, 300)					)
			self.assertEqual(segmentation[0].shape		, UpperCAmelCase_					)

			lowerCamelCase     :			List[Any]		=    image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    torch.Size((480, 480)					)
			self.assertEqual(segmentation[0].shape		, UpperCAmelCase_					)






