





"""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  _UpperCAmelCase       :


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


					def      lowerCamelCase	( self      :List[Any]		):
												A		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

												A		=  self.get_config()

												return config, pixel_values, labels


					def      lowerCamelCase	( self      :Dict		):
												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      lowerCamelCase	( self      :str					,	__UpperCamelCase      :Dict					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Optional[int]		):
												A		=  DPTModel(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)		)


					def      lowerCamelCase	( self      :int					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[str]		):
												A		=  self.num_labels
												A		=  DPTForDepthEstimation(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.predicted_depth.shape					,	(self.batch_size, self.image_size, self.image_size)		)


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :int					,	__UpperCamelCase      :List[Any]					,	__UpperCamelCase      :Dict		):
												A		=  self.num_labels
												A		=  DPTForSemanticSegmentation(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase					,	labels=__UpperCamelCase		)
												self.parent.assertEqual(
												    result.logits.shape					,	(self.batch_size, self.num_labels, self.image_size, self.image_size)		)


					def      lowerCamelCase	( self      :List[str]		):
												A		=  self.prepare_config_and_inputs()
												A,     A,     A		=  config_and_inputs
												A		=  {"pixel_values": pixel_values}
												return config, inputs_dict







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

					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False


					def      lowerCamelCase	( self      :Union[str, Any]		):
												A		=  DPTModelTester(self		)
												A		=  ConfigTester(self					,	config_class=__UpperCamelCase					,	has_text_modality=__UpperCamelCase					,	hidden_size=37		)


					def      lowerCamelCase	( self      :Optional[Any]		):
												self.config_tester.run_common_tests()


					@unittest.skip(reason="DPT does not use inputs_embeds"		)
					def      lowerCamelCase	( self      :Tuple		):
												pass


					def      lowerCamelCase	( self      :Optional[int]		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A		=  model_class(__UpperCamelCase		)
																			self.assertIsInstance(model.get_input_embeddings()					,	(nn.Module)		)
																			A		=  model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__UpperCamelCase					,	nn.Linear		)		)


					def      lowerCamelCase	( self      :List[str]		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

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

																			A		=  ["pixel_values"]
																			self.assertListEqual(arg_names[:1]					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :Dict		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[Any]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[int]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase		)


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

																			A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
																			A		=  True

																			if model_class in get_values(__UpperCamelCase		):
																										continue

																			A		=  model_class(__UpperCamelCase		)
																			model.to(__UpperCamelCase		)
																			model.train()
																			A		=  self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase					,	return_labels=__UpperCamelCase		)
																			A		=  model(**__UpperCamelCase		).loss
																			loss.backward()


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

																			A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
																			A		=  False
																			A		=  True

																			if model_class in get_values(__UpperCamelCase		) or not model_class.supports_gradient_checkpointing:
																										continue
																			A		=  model_class(__UpperCamelCase		)
																			model.to(__UpperCamelCase		)
																			model.gradient_checkpointing_enable()
																			model.train()
																			A		=  self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase					,	return_labels=__UpperCamelCase		)
																			A		=  model(**__UpperCamelCase		).loss
																			loss.backward()


					def      lowerCamelCase	( self      :Optional[Any]		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

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


					@slow
					def      lowerCamelCase	( self      :Optional[int]		):
												for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A		=  DPTModel.from_pretrained(__UpperCamelCase		)
																			self.assertIsNotNone(__UpperCamelCase		)







def  A__    (      ):
							A		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
							return image






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


					def      lowerCamelCase	( self      :str		):
												A		=  DPTImageProcessor.from_pretrained("Intel/dpt-large"		)
												A		=  DPTForDepthEstimation.from_pretrained("Intel/dpt-large"		).to(__UpperCamelCase		)

												A		=  prepare_img()
												A		=  image_processor(images=__UpperCamelCase					,	return_tensors="pt"		).to(__UpperCamelCase		)

												# forward pass
												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)
																			A		=  outputs.predicted_depth

												# verify the predicted depth
												A		=  torch.Size((1, 3_84, 3_84)		)
												self.assertEqual(predicted_depth.shape					,	__UpperCamelCase		)

												A		=  torch.tensor(
												    [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]		).to(__UpperCamelCase		)

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


					def      lowerCamelCase	( self      :Dict		):
												A		=  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"		)
												A		=  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"		).to(__UpperCamelCase		)

												A		=  prepare_img()
												A		=  image_processor(images=__UpperCamelCase					,	return_tensors="pt"		).to(__UpperCamelCase		)

												# forward pass
												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)

												# verify the logits
												A		=  torch.Size((1, 1_50, 4_80, 4_80)		)
												self.assertEqual(outputs.logits.shape					,	__UpperCamelCase		)

												A		=  torch.tensor(
												    [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]		).to(__UpperCamelCase		)

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


					def      lowerCamelCase	( self      :List[Any]		):
												A		=  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"		)
												A		=  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"		).to(__UpperCamelCase		)

												A		=  prepare_img()
												A		=  image_processor(images=__UpperCamelCase					,	return_tensors="pt"		).to(__UpperCamelCase		)

												# forward pass
												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)

												A		=  outputs.logits.detach().cpu()

												A		=  image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase					,	target_sizes=[(5_00, 3_00)]		)
												A		=  torch.Size((5_00, 3_00)		)
												self.assertEqual(segmentation[0].shape					,	__UpperCamelCase		)

												A		=  image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase		)
												A		=  torch.Size((4_80, 4_80)		)
												self.assertEqual(segmentation[0].shape					,	__UpperCamelCase		)









"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							['''image_processor''', '''tokenizer''']
					UpperCamelCase								=							'''LayoutLMv2ImageProcessor'''
					UpperCamelCase								=							('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')


					def __init__( self      :List[Any]					,	__UpperCamelCase      :List[Any]=None					,	__UpperCamelCase      :Tuple=None					,	**__UpperCamelCase      :int		):
												A		=  None
												if "feature_extractor" in kwargs:
																			warnings.warn(
																			    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																			    " instead."					,	__UpperCamelCase					,	)
																			A		=  kwargs.pop("feature_extractor"		)

												A		=  image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																			raise ValueError("You need to specify an `image_processor`."		)
												if tokenizer is None:
																			raise ValueError("You need to specify a `tokenizer`."		)

												super().__init__(__UpperCamelCase					,	__UpperCamelCase		)


					def __call__( self      :Optional[Any]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,	__UpperCamelCase      :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,	__UpperCamelCase      :Union[List[List[int]], List[List[List[int]]]] = None					,	__UpperCamelCase      :Optional[Union[List[int], List[List[int]]]] = None					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Union[bool, str, PaddingStrategy] = False					,	__UpperCamelCase      :Union[bool, str, TruncationStrategy] = False					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :int = 0					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Optional[Union[str, TensorType]] = None					,	**__UpperCamelCase      :int					,	):
												# verify input
												if self.image_processor.apply_ocr and (boxes is not None):
																			raise ValueError(
																			    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."		)

												if self.image_processor.apply_ocr and (word_labels is not None):
																			raise ValueError(
																			    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)

												if return_overflowing_tokens is True and return_offsets_mapping is False:
																			raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)

												# first, apply the image processor
												A		=  self.image_processor(images=__UpperCamelCase					,	return_tensors=__UpperCamelCase		)

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																			if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																										A		=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
																			A		=  features["words"]

												A		=  self.tokenizer(
												    text=text if text is not None else features["words"]					,	text_pair=text_pair if text_pair is not None else None					,	boxes=boxes if boxes is not None else features["boxes"]					,	word_labels=__UpperCamelCase					,	add_special_tokens=__UpperCamelCase					,	padding=__UpperCamelCase					,	truncation=__UpperCamelCase					,	max_length=__UpperCamelCase					,	stride=__UpperCamelCase					,	pad_to_multiple_of=__UpperCamelCase					,	return_token_type_ids=__UpperCamelCase					,	return_attention_mask=__UpperCamelCase					,	return_overflowing_tokens=__UpperCamelCase					,	return_special_tokens_mask=__UpperCamelCase					,	return_offsets_mapping=__UpperCamelCase					,	return_length=__UpperCamelCase					,	verbose=__UpperCamelCase					,	return_tensors=__UpperCamelCase					,	**__UpperCamelCase					,	)

												# add pixel values
												A		=  features.pop("pixel_values"		)
												if return_overflowing_tokens is True:
																			A		=  self.get_overflowing_images(__UpperCamelCase					,	encoded_inputs["overflow_to_sample_mapping"]		)
												A		=  images

												return encoded_inputs


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[str]		):
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												A		=  []
												for sample_idx in overflow_to_sample_mapping:
																			images_with_overflow.append(images[sample_idx]		)

												if len(__UpperCamelCase		) != len(__UpperCamelCase		):
																			raise ValueError(
																			    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
																			    f" {len(__UpperCamelCase		)} and {len(__UpperCamelCase		)}"		)

												return images_with_overflow


					def      lowerCamelCase	( self      :Tuple					,	*__UpperCamelCase      :Union[str, Any]					,	**__UpperCamelCase      :Tuple		):
												return self.tokenizer.batch_decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	*__UpperCamelCase      :str					,	**__UpperCamelCase      :List[str]		):
												return self.tokenizer.decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					@property
					def      lowerCamelCase	( self      :str		):
												return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


					@property
					def      lowerCamelCase	( self      :str		):
												warnings.warn(
												    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,	__UpperCamelCase					,	)
												return self.image_processor_class


					@property
					def      lowerCamelCase	( self      :int		):
												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,	__UpperCamelCase					,	)
												return self.image_processor


