'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


A_	   = logging.get_logger(__name__)

A_	   = {
    "google/bigbird-pegasus-large-arxiv": (
        "https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-pubmed": (
        "https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"
    ),
    "google/bigbird-pegasus-large-bigpatent": (
        "https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}




class 		SCREAMING_SNAKE_CASE_					(_a      ):



			"""simple docstring"""






			__magic_name__ :				List[Any]   							=       """bigbird_pegasus"""
			__magic_name__ :				List[str]   							=       ["""past_key_values"""]
			__magic_name__ :				Any   							=       {
			    """num_attention_heads""": """encoder_attention_heads""",
			    """hidden_size""": """d_model""",
			    """attention_probs_dropout_prob""": """attention_dropout""",
			}



			def __init__(							self				,			snake_case_=96_103				,			snake_case_=4_096				,			snake_case_=16				,			snake_case_=4_096				,			snake_case_=16				,			snake_case_=16				,			snake_case_=4_096				,			snake_case_=16				,			snake_case_=0.0				,			snake_case_=0.0				,			snake_case_=True				,			snake_case_=True				,			snake_case_="gelu_new"				,			snake_case_=1_024				,			snake_case_=0.1				,			snake_case_=0.0				,			snake_case_=0.0				,			snake_case_=0.02				,			snake_case_=2				,			snake_case_=0.0				,			snake_case_=True				,			snake_case_=0				,			snake_case_=2				,			snake_case_=1				,			snake_case_="block_sparse"				,			snake_case_=64				,			snake_case_=3				,			snake_case_=False				,			**snake_case_				,			):
									snake_case :       Any				=    vocab_size
									snake_case :       str				=    max_position_embeddings
									snake_case :       str				=    d_model
									snake_case :       int				=    encoder_ffn_dim
									snake_case :       Optional[Any]				=    encoder_layers
									snake_case :       Any				=    encoder_attention_heads
									snake_case :       int				=    decoder_ffn_dim
									snake_case :       Dict				=    decoder_layers
									snake_case :       Dict				=    decoder_attention_heads
									snake_case :       Union[str, Any]				=    dropout
									snake_case :       List[Any]				=    attention_dropout
									snake_case :       int				=    activation_dropout
									snake_case :       Union[str, Any]				=    activation_function
									snake_case :       Optional[int]				=    init_std
									snake_case :       List[Any]				=    encoder_layerdrop
									snake_case :       Dict				=    decoder_layerdrop
									snake_case :       int				=    classifier_dropout
									snake_case :       List[Any]				=    use_cache
									snake_case :       int				=    encoder_layers
									snake_case :       List[Any]				=    scale_embedding  # scale factor will be sqrt(d_model) if True

									# extra config
									snake_case :       Any				=    attention_type
									snake_case :       Any				=    block_size
									snake_case :       int				=    num_random_blocks
									snake_case :       List[str]				=    use_bias

									super().__init__(
									    pad_token_id=_a				,			bos_token_id=_a				,			eos_token_id=_a				,			is_encoder_decoder=_a				,			decoder_start_token_id=_a				,			**_a				,			)







class 		SCREAMING_SNAKE_CASE_					(_a      ):



			"""simple docstring"""






			@property
			def   lowerCamelCase (							self					):
									if self.task in ["default", "seq2seq-lm"]:
															snake_case :       Optional[Any]				=    OrderedDict(
															    [
															        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
															        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
															    ]					)

															if self.use_past:
																					snake_case :       List[Any]				=    {0: "batch"}
																					snake_case :       int				=    {0: "batch", 1: "past_decoder_sequence + sequence"}
															else:
																					snake_case :       List[str]				=    {0: "batch", 1: "decoder_sequence"}
																					snake_case :       Optional[int]				=    {0: "batch", 1: "decoder_sequence"}

															if self.use_past:
																					self.fill_with_past_key_values_(_a				,			direction="inputs"					)
									elif self.task == "causal-lm":
															# TODO: figure this case out.
															snake_case :       List[str]				=    OrderedDict(
															    [
															        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
															        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
															    ]					)
															if self.use_past:
																					snake_case :       str				=    self.num_layers
																					for i in range(_a					):
																											snake_case :       Any				=    {0: "batch", 2: "past_sequence + sequence"}
																											snake_case :       Any				=    {0: "batch", 2: "past_sequence + sequence"}
									else:
															snake_case :       int				=    OrderedDict(
															    [
															        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
															        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
															        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
															        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
															    ]					)

									return common_inputs



			@property
			def   lowerCamelCase (							self					):
									if self.task in ["default", "seq2seq-lm"]:
															snake_case :       str				=    super().outputs
									else:
															snake_case :       Dict				=    super(_a				,			self					).outputs
															if self.use_past:
																					snake_case :       Tuple				=    self.num_layers
																					for i in range(_a					):
																											snake_case :       str				=    {0: "batch", 2: "past_sequence + sequence"}
																											snake_case :       List[str]				=    {0: "batch", 2: "past_sequence + sequence"}
									return common_outputs



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = -1				,			snake_case_ = -1				,			snake_case_ = False				,			snake_case_ = None				,			):
									snake_case :       Union[str, Any]				=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _a				,			_a				,			_a				,			_a				,			_a					)

									# Generate decoder inputs
									snake_case :       str				=    seq_length if not self.use_past else 1
									snake_case :       Dict				=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _a				,			_a				,			_a				,			_a				,			_a					)
									snake_case :       List[str]				=    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									snake_case :       str				=    dict(**_a				,			**_a					)

									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
															else:
																					import torch
															snake_case :       str				=    common_inputs["input_ids"].shape
															snake_case :       List[str]				=    common_inputs["decoder_input_ids"].shape[1]
															snake_case :       Any				=    self.num_attention_heads
															snake_case :       int				=    (
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															snake_case :       str				=    decoder_seq_length + 3
															snake_case :       List[str]				=    (
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															snake_case :       List[str]				=    torch.cat(
															    [common_inputs["decoder_attention_mask"], torch.ones(_a				,			_a					)]				,			dim=1					)

															snake_case :       List[str]				=    []
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															snake_case :       int				=    self.num_layers
															snake_case :       List[Any]				=    min(_a				,			_a					)
															snake_case :       str				=    max(_a				,			_a					) - min_num_layers
															snake_case :       Optional[Any]				=    "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

															for _ in range(_a					):
																					common_inputs["past_key_values"].append(
																					    (
																					        torch.zeros(_a					),
																					        torch.zeros(_a					),
																					        torch.zeros(_a					),
																					        torch.zeros(_a					),
																					    )					)
															# TODO: test this.
															snake_case :       Any				=    encoder_shape if remaining_side_name == "encoder" else decoder_shape
															for _ in range(_a				,			_a					):
																					common_inputs["past_key_values"].append((torch.zeros(_a					), torch.zeros(_a					))					)
									return common_inputs



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = -1				,			snake_case_ = -1				,			snake_case_ = False				,			snake_case_ = None				,			):
									snake_case :       Tuple				=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _a				,			_a				,			_a				,			_a				,			_a					)

									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
															else:
																					import torch
															snake_case :       Tuple				=    common_inputs["input_ids"].shape
															# Not using the same length for past_key_values
															snake_case :       str				=    seqlen + 2
															snake_case :       List[str]				=    self.num_layers
															snake_case :       Any				=    self.num_attention_heads
															snake_case :       Optional[int]				=    (
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)

															snake_case :       int				=    common_inputs["attention_mask"].dtype
															snake_case :       str				=    torch.cat(
															    [common_inputs["attention_mask"], torch.ones(_a				,			_a				,			dtype=_a					)]				,			dim=1					)
															snake_case :       int				=    [
															    (torch.zeros(_a					), torch.zeros(_a					)) for _ in range(_a					)
															]
									return common_inputs



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = -1				,			snake_case_ = -1				,			snake_case_ = False				,			snake_case_ = None				,			):
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									snake_case :       List[str]				=    compute_effective_axis_dimension(
									    _a				,			fixed_dimension=OnnxConfig.default_fixed_batch				,			num_token_to_add=0					)

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									snake_case :       Optional[int]				=    tokenizer.num_special_tokens_to_add(_a					)
									snake_case :       Any				=    compute_effective_axis_dimension(
									    _a				,			fixed_dimension=OnnxConfig.default_fixed_sequence				,			num_token_to_add=_a					)

									# Generate dummy inputs according to compute batch and sequence
									snake_case :       Any				=    [" ".join([tokenizer.unk_token]					) * seq_length] * batch_size
									snake_case :       Optional[Any]				=    dict(tokenizer(_a				,			return_tensors=_a					)					)
									return common_inputs



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = -1				,			snake_case_ = -1				,			snake_case_ = False				,			snake_case_ = None				,			):
									if self.task in ["default", "seq2seq-lm"]:
															snake_case :       Any				=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
															    _a				,			batch_size=_a				,			seq_length=_a				,			is_pair=_a				,			framework=_a					)

									elif self.task == "causal-lm":
															snake_case :       Dict				=    self._generate_dummy_inputs_for_causal_lm(
															    _a				,			batch_size=_a				,			seq_length=_a				,			is_pair=_a				,			framework=_a					)
									else:
															snake_case :       int				=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
															    _a				,			batch_size=_a				,			seq_length=_a				,			is_pair=_a				,			framework=_a					)

									return common_inputs






			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									if self.task in ["default", "seq2seq-lm"]:
															snake_case :       Tuple				=    super()._flatten_past_key_values_(_a				,			_a				,			_a				,			_a					)
									else:
															snake_case :       Tuple				=    super(_a				,			self					)._flatten_past_key_values_(
															    _a				,			_a				,			_a				,			_a					)










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 						lowerCamelCase__			:


						def __init__(			self    :	List[str]   ,							_a    :	Any   ,							_a    :	str=2   ,							_a    :	List[str]=3_2   ,							_a    :	str=1_6   ,							_a    :	Tuple=3   ,							_a    :	Any=True   ,							_a    :	Optional[Any]=True   ,							_a    :	Optional[Any]=3_2   ,							_a    :	Optional[Any]=4   ,							_a    :	str=[0, 1, 2, 3]   ,							_a    :	Any=4   ,							_a    :	List[Any]=3_7   ,							_a    :	Optional[Any]="gelu"   ,							_a    :	str=0.1   ,							_a    :	Optional[Any]=0.1   ,							_a    :	Optional[Any]=0.0_2   ,							_a    :	Optional[int]=3   ,							_a    :	List[str]=False   ,							_a    :	Dict=None   ,							):
													a__:							List[Any]									=parent
													a__:							List[str]									=batch_size
													a__:							Optional[Any]									=image_size
													a__:							Optional[Any]									=patch_size
													a__:							Dict									=num_channels
													a__:							Optional[int]									=is_training
													a__:							List[Any]									=use_labels
													a__:							List[str]									=hidden_size
													a__:							int									=num_hidden_layers
													a__:							List[Any]									=backbone_out_indices
													a__:							int									=num_attention_heads
													a__:							List[Any]									=intermediate_size
													a__:							int									=hidden_act
													a__:							int									=hidden_dropout_prob
													a__:							Union[str, Any]									=attention_probs_dropout_prob
													a__:							Tuple									=initializer_range
													a__:							Dict									=num_labels
													a__:							Tuple									=scope
													a__:							List[Any]									=is_hybrid
													# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
													a__:							Any									=(image_size // patch_size) ** 2
													a__:							Tuple									=num_patches + 1


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							str									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

													a__:							Dict									=None
													if self.use_labels:
																				a__:							Tuple									=ids_tensor([self.batch_size, self.image_size, self.image_size]   ,							self.num_labels      )

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

													return config, pixel_values, labels


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													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=_a   ,							initializer_range=self.initializer_range   ,							is_hybrid=self.is_hybrid   ,							)


						def 	_lowerCamelCase      (			self    :	Optional[int]   ,							_a    :	Dict   ,							_a    :	List[str]   ,							_a    :	int      ):
													a__:							Union[str, Any]									=DPTModel(config=_a      )
													model.to(_a      )
													model.eval()
													a__:							List[Any]									=model(_a      )
													self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)      )


						def 	_lowerCamelCase      (			self    :	int   ,							_a    :	Tuple   ,							_a    :	Union[str, Any]   ,							_a    :	List[Any]      ):
													a__:							Union[str, Any]									=self.num_labels
													a__:							List[str]									=DPTForDepthEstimation(_a      )
													model.to(_a      )
													model.eval()
													a__:							Optional[int]									=model(_a      )
													self.parent.assertEqual(result.predicted_depth.shape   ,							(self.batch_size, self.image_size, self.image_size)      )


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	str   ,							_a    :	Optional[int]   ,							_a    :	int      ):
													a__:							Any									=self.num_labels
													a__:							List[str]									=DPTForSemanticSegmentation(_a      )
													model.to(_a      )
													model.eval()
													a__:							Union[str, Any]									=model(_a   ,							labels=_a      )
													self.parent.assertEqual(
													    result.logits.shape   ,							(self.batch_size, self.num_labels, self.image_size, self.image_size)      )







						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							List[str]									=self.prepare_config_and_inputs()
													a__				,      a__				,      a__:							Union[str, Any]									=config_and_inputs
													a__:							Tuple									={"pixel_values": pixel_values}
													return config, inputs_dict


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

						_lowerCAmelCase             =			False
						_lowerCAmelCase             =			False
						_lowerCAmelCase             =			False


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Dict									=DPTModelTester(self      )
													a__:							int									=ConfigTester(self   ,							config_class=_a   ,							has_text_modality=_a   ,							hidden_size=3_7      )


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													self.config_tester.run_common_tests()


						@unittest.skip(reason="DPT does not use inputs_embeds"      )
						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													pass


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__				,      a__:							Optional[Any]									=self.model_tester.prepare_config_and_inputs_for_common()

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


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__				,      a__:							int									=self.model_tester.prepare_config_and_inputs_for_common()

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

																				a__:							List[str]									=["pixel_values"]
																				self.assertListEqual(arg_names[:1]   ,							_a      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													a__:							List[str]									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_a      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Any									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_depth_estimation(*_a      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Any									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*_a      )


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													for model_class in self.all_model_classes:
																				if model_class.__name__ == "DPTForDepthEstimation":
																											continue

																				a__				,      a__:							List[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
																				a__:							Optional[int]									=True

																				if model_class in get_values(_a      ):
																											continue

																				a__:							Union[str, Any]									=model_class(_a      )
																				model.to(_a      )
																				model.train()
																				a__:							Optional[int]									=self._prepare_for_class(_a   ,							_a   ,							return_labels=_a      )
																				a__:							List[Any]									=model(**_a      ).loss
																				loss.backward()


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													for model_class in self.all_model_classes:
																				if model_class.__name__ == "DPTForDepthEstimation":
																											continue

																				a__				,      a__:							Optional[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
																				a__:							Union[str, Any]									=False
																				a__:							Any									=True

																				if model_class in get_values(_a      ) or not model_class.supports_gradient_checkpointing:
																											continue
																				a__:							Tuple									=model_class(_a      )
																				model.to(_a      )
																				model.gradient_checkpointing_enable()
																				model.train()
																				a__:							Dict									=self._prepare_for_class(_a   ,							_a   ,							return_labels=_a      )
																				a__:							Optional[Any]									=model(**_a      ).loss
																				loss.backward()


						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__				,      a__:							str									=self.model_tester.prepare_config_and_inputs_for_common()

													a__:							Tuple									=_config_zero_init(_a      )
													for model_class in self.all_model_classes:
																				a__:							List[Any]									=model_class(config=_a      )
																				# Skip the check for the backbone
																				a__:							List[Any]									=[]
																				for name, module in model.named_modules():
																											if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																																		a__:							Optional[Any]									=[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 	_lowerCamelCase      (			self    :	Dict      ):
													pass







						@slow
						def 	_lowerCamelCase      (			self    :	str      ):
													for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				a__:							List[Any]									=DPTModel.from_pretrained(_a      )
																				self.assertIsNotNone(_a      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
							return image







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


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__:							Any									=DPTImageProcessor.from_pretrained("Intel/dpt-large"      )
													a__:							str									=DPTForDepthEstimation.from_pretrained("Intel/dpt-large"      ).to(_a      )

													a__:							Optional[int]									=prepare_img()
													a__:							List[Any]									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							int									=model(**_a      )
																				a__:							List[str]									=outputs.predicted_depth

													# verify the predicted depth
													a__:							Optional[int]									=torch.Size((1, 3_8_4, 3_8_4)      )
													self.assertEqual(predicted_depth.shape   ,							_a      )

													a__:							Union[str, Any]									=torch.tensor(
													    [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]      ).to(_a      )

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


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Optional[Any]									=DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
													a__:							Union[str, Any]									=DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(_a      )

													a__:							Dict									=prepare_img()
													a__:							Optional[int]									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							int									=model(**_a      )

													# verify the logits
													a__:							int									=torch.Size((1, 1_5_0, 4_8_0, 4_8_0)      )
													self.assertEqual(outputs.logits.shape   ,							_a      )

													a__:							Any									=torch.tensor(
													    [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]      ).to(_a      )

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







						def 	_lowerCamelCase      (			self    :	int      ):
													a__:							Dict									=DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
													a__:							List[Any]									=DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(_a      )

													a__:							int									=prepare_img()
													a__:							Any									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							Optional[int]									=model(**_a      )

													a__:							Union[str, Any]									=outputs.logits.detach().cpu()

													a__:							List[Any]									=image_processor.post_process_semantic_segmentation(outputs=_a   ,							target_sizes=[(5_0_0, 3_0_0)]      )
													a__:							Dict									=torch.Size((5_0_0, 3_0_0)      )
													self.assertEqual(segmentation[0].shape   ,							_a      )

													a__:							Optional[Any]									=image_processor.post_process_semantic_segmentation(outputs=_a      )
													a__:							List[str]									=torch.Size((4_8_0, 4_8_0)      )
													self.assertEqual(segmentation[0].shape   ,							_a      )





