





'''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 		lowerCamelCase  :



	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, 384, 24, 24]		,      a_=True		,      a_=None		,      ):
			lowerCAmelCase     :    List[Any]						=   parent
			lowerCAmelCase     :    Tuple						=   batch_size
			lowerCAmelCase     :    List[str]						=   image_size
			lowerCAmelCase     :    Union[str, Any]						=   patch_size
			lowerCAmelCase     :    List[Any]						=   num_channels
			lowerCAmelCase     :    Union[str, Any]						=   is_training
			lowerCAmelCase     :    Optional[int]						=   use_labels
			lowerCAmelCase     :    List[Any]						=   hidden_size
			lowerCAmelCase     :    List[str]						=   num_hidden_layers
			lowerCAmelCase     :    str						=   backbone_out_indices
			lowerCAmelCase     :    Union[str, Any]						=   num_attention_heads
			lowerCAmelCase     :    Optional[int]						=   intermediate_size
			lowerCAmelCase     :    Optional[int]						=   hidden_act
			lowerCAmelCase     :    Any						=   hidden_dropout_prob
			lowerCAmelCase     :    Optional[int]						=   attention_probs_dropout_prob
			lowerCAmelCase     :    Optional[int]						=   initializer_range
			lowerCAmelCase     :    Dict						=   num_labels
			lowerCAmelCase     :    Any						=   backbone_featmap_shape
			lowerCAmelCase     :    List[str]						=   scope
			lowerCAmelCase     :    Union[str, Any]						=   is_hybrid
			# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
			lowerCAmelCase     :    Any						=   (image_size // patch_size) ** 2
			lowerCAmelCase     :    Tuple						=   num_patches + 1



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

			lowerCAmelCase     :    Union[str, Any]						=   None
			if self.use_labels:
					lowerCAmelCase     :    Union[str, Any]						=   ids_tensor([self.batch_size, self.image_size, self.image_size]		,      self.num_labels	)

			lowerCAmelCase     :    int						=   self.get_config()

			return config, pixel_values, labels



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   {
			    "global_padding": "same",
			    "layer_type": "bottleneck",
			    "depths": [3, 4, 9],
			    "out_features": ["stage1", "stage2", "stage3"],
			    "embedding_dynamic_padding": True,
			    "hidden_sizes": [96, 192, 384, 768],
			    "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   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Dict						=   DPTModel(config=a_	)
			model.to(a_	)
			model.eval()
			lowerCAmelCase     :    str						=   model(a_	)
			self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   self.num_labels
			lowerCAmelCase     :    Dict						=   DPTForDepthEstimation(a_	)
			model.to(a_	)
			model.eval()
			lowerCAmelCase     :    Dict						=   model(a_	)
			self.parent.assertEqual(result.predicted_depth.shape		,      (self.batch_size, self.image_size, self.image_size)	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Any						=   self.num_labels
			lowerCAmelCase     :    List[Any]						=   DPTForSemanticSegmentation(a_	)
			model.to(a_	)
			model.eval()
			lowerCAmelCase     :    Tuple						=   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	):
			lowerCAmelCase     :    List[str]						=   self.prepare_config_and_inputs()
			lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase     :    Union[str, Any]						=   config_and_inputs
			lowerCAmelCase     :    List[str]						=   {"pixel_values": pixel_values}
			return config, inputs_dict






@require_torch
class 		lowerCamelCase  (      _A      ,   _A      ,   unittest.TestCase       ):
	snake_case_      =   (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	snake_case_      =   (
	    {
	        "depth-estimation": DPTForDepthEstimation,
	        "feature-extraction": DPTModel,
	        "image-segmentation": DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	snake_case_      =   False
	snake_case_      =   False
	snake_case_      =   False



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   DPTModelTester(self	)
			lowerCAmelCase     :    Any						=   ConfigTester(self		,      config_class=a_		,      has_text_modality=a_		,      hidden_size=37	)



	def   _lowerCamelCase    (   self	):
			self.config_tester.run_common_tests()



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



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

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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase	,		lowerCAmelCase     :    Dict						=   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCAmelCase     :    Dict						=   model_class(a_	)
					lowerCAmelCase     :    Union[str, Any]						=   inspect.signature(model.forward	)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					lowerCAmelCase     :    Dict						=   [*signature.parameters.keys()]

					lowerCAmelCase     :    Tuple						=   ["pixel_values"]
					self.assertListEqual(arg_names[:1]		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_depth_estimation(*a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*a_	)



	def   _lowerCamelCase    (   self	):
			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     :    Tuple						=   True

					if model_class in get_values(a_	):
							continue

					lowerCAmelCase     :    Dict						=   model_class(a_	)
					model.to(a_	)
					model.train()
					lowerCAmelCase     :    str						=   self._prepare_for_class(a_		,      a_		,      return_labels=a_	)
					lowerCAmelCase     :    str						=   model(**a_	).loss
					loss.backward()



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

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

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



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

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



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





	def   _lowerCamelCase    (   self	):
			# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
			lowerCAmelCase	,		lowerCAmelCase     :    Union[str, Any]						=   self.model_tester.prepare_config_and_inputs_for_common()
			lowerCAmelCase     :    Optional[Any]						=   "add"
			with self.assertRaises(a_	):
					lowerCAmelCase     :    List[str]						=   DPTForDepthEstimation(a_	)






def      __A						(							):
		lowerCAmelCase     :    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	):
			lowerCAmelCase     :    Tuple						=   DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"	)
			lowerCAmelCase     :    Dict						=   DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"	).to(a_	)

			lowerCAmelCase     :    Any						=   prepare_img()
			lowerCAmelCase     :    Optional[int]						=   image_processor(images=a_		,      return_tensors="pt"	).to(a_	)

			# forward pass
			with torch.no_grad():
					lowerCAmelCase     :    Optional[Any]						=   model(**a_	)
					lowerCAmelCase     :    Tuple						=   outputs.predicted_depth

			# verify the predicted depth
			lowerCAmelCase     :    Any						=   torch.Size((1, 384, 384)	)
			self.assertEqual(predicted_depth.shape		,      a_	)

			lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(
			    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]	).to(a_	)

			self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100		,      a_		,      atol=1e-4	)	)










'''simple docstring'''






def      __A						(							a_						:  int	):
		assert (
		    isinstance(a_      ,a_	) and number_of_steps > 0
		), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
		if number_of_steps == 1:
				return 1
		lowerCAmelCase	,		lowerCAmelCase     :    int						=   1, 1
		for _ in range(number_of_steps - 1	):
				lowerCAmelCase	,		lowerCAmelCase     :    Union[str, Any]						=   current + previous, current
		return current


if __name__ == "__main__":
					import doctest

					doctest.testmod()



