






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_      :



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=2       ,			a=32       ,			a=16       ,			a=3       ,			a=True       ,			a=True       ,			a=32       ,			a=4       ,			a=[0, 1, 2, 3]       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=0.02       ,			a=3       ,			a=[1, 3_84, 24, 24]       ,			a=True       ,			a=None       ,			)       ->    Union[str, Any]:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							image_size
								snake_case_				=							patch_size
								snake_case_				=							num_channels
								snake_case_				=							is_training
								snake_case_				=							use_labels
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							backbone_out_indices
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							initializer_range
								snake_case_				=							num_labels
								snake_case_				=							backbone_featmap_shape
								snake_case_				=							scope
								snake_case_				=							is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								snake_case_				=							(image_size // patch_size) ** 2
								snake_case_				=							num_patches + 1




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

								snake_case_				=							self.get_config()

								return config, pixel_values, labels




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							{
								    'global_padding': 'same',
								    'layer_type': 'bottleneck',
								    'depths': [3, 4, 9],
								    'out_features': ['stage1', 'stage2', 'stage3'],
								    'embedding_dynamic_padding': True,
								    'hidden_sizes': [96, 1_92, 3_84, 7_68],
								    'num_groups': 2,
								}

								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       ,			backbone_config=a       ,			backbone_featmap_shape=self.backbone_featmap_shape       ,			)




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




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    Union[str, Any]:
								snake_case_				=							self.num_labels
								snake_case_				=							DPTForDepthEstimation(a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a					)
								self.parent.assertEqual(result.predicted_depth.shape       ,			(self.batch_size, self.image_size, self.image_size)					)




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






	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class  UpperCamelCase_      (			snake_case_       ,					snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	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 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_				=							DPTModelTester(self					)
								snake_case_				=							ConfigTester(self       ,			config_class=a       ,			has_text_modality=a       ,			hidden_size=37					)




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								self.config_tester.run_common_tests()




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




	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_common()

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




	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case_				=							['pixel_values']
															self.assertListEqual(arg_names[:1]       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a					)




	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*a					)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*a					)




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

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

															if model_class in get_values(a					):
																						continue

															snake_case_				=							model_class(a					)
															model.to(a					)
															model.train()
															snake_case_				=							self._prepare_for_class(a       ,			a       ,			return_labels=a					)
															snake_case_				=							model(**a					).loss
															loss.backward()




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

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

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




	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_common()

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




	@slow
	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
															snake_case_				=							DPTModel.from_pretrained(a					)
															self.assertIsNotNone(a					)






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_				=							'add'
								with self.assertRaises(a					):
															snake_case_				=							DPTForDepthEstimation(a					)






def 						__UpperCAmelCase			(     ):
							snake_case_				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
							return image




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



	'''simple docstring'''




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_				=							DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'					)
								snake_case_				=							DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'					).to(a					)

								snake_case_				=							prepare_img()
								snake_case_				=							image_processor(images=a       ,			return_tensors='pt'					).to(a					)

								# forward pass
								with torch.no_grad():
															snake_case_				=							model(**a					)
															snake_case_				=							outputs.predicted_depth

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

								snake_case_				=							torch.tensor(
								    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]					).to(a					)

								self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00       ,			a       ,			atol=1E-4					)					)













from typing import List, Optional, Union

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




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ['''image_processor''', '''tokenizer''']
	lowerCAmelCase													= '''BlipImageProcessor'''
	lowerCAmelCase													= ('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(       self       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							False
								super().__init__(a       ,			a					)
								snake_case_				=							self.image_processor




	def __call__(       self       ,			a = None       ,			a = None       ,			a = True       ,			a = False       ,			a = None       ,			a = None       ,			a = 0       ,			a = None       ,			a = None       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = True       ,			a = None       ,			**a       ,			)       ->    BatchEncoding:
								if images is None and text is None:
															raise ValueError('You have to specify either images or text.'					)

								# Get only text
								if images is None:
															snake_case_				=							self.tokenizer
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
															return text_encoding

								# add pixel_values
								snake_case_				=							self.image_processor(a       ,			return_tensors=a					)

								if text is not None:
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
								else:
															snake_case_				=							None

								if text_encoding is not None:
															encoding_image_processor.update(a					)

								return encoding_image_processor




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    int:
								return self.tokenizer.batch_decode(*a       ,			**a					)




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    Any:
								return self.tokenizer.decode(*a       ,			**a					)






	@property
	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.tokenizer.model_input_names
								snake_case_				=							self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)





