




'''simple docstring'''




import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DPTImageProcessor

class     lowerCAmelCase_ (			unittest.TestCase ):







  '''simple docstring'''







  def __init__(    self     :						Optional[int]    ,		_UpperCAmelCase     :						Dict    ,		_UpperCAmelCase     :						Any=7    ,		_UpperCAmelCase     :						List[str]=3    ,		_UpperCAmelCase     :						Dict=18    ,		_UpperCAmelCase     :						List[str]=30    ,		_UpperCAmelCase     :						Optional[Any]=4_00    ,		_UpperCAmelCase     :						List[Any]=True    ,		_UpperCAmelCase     :						Optional[Any]=None    ,		_UpperCAmelCase     :						int=True    ,		_UpperCAmelCase     :						Tuple=[0.5, 0.5, 0.5]    ,		_UpperCAmelCase     :						Dict=[0.5, 0.5, 0.5]    ,		):






   """simple docstring"""






   UpperCAmelCase__      				=     size if size is not None else {"""height""": 18, """width""": 18}
   UpperCAmelCase__      				=     parent
   UpperCAmelCase__      				=     batch_size
   UpperCAmelCase__      				=     num_channels
   UpperCAmelCase__      				=     image_size
   UpperCAmelCase__      				=     min_resolution
   UpperCAmelCase__      				=     max_resolution
   UpperCAmelCase__      				=     do_resize
   UpperCAmelCase__      				=     size
   UpperCAmelCase__      				=     do_normalize
   UpperCAmelCase__      				=     image_mean
   UpperCAmelCase__      				=     image_std



  def 				SCREAMING_SNAKE_CASE__      (    self     :						Union[str, Any]			):






   """simple docstring"""






   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
   }

@require_torch
@require_vision
class     lowerCAmelCase_ (			__A      ,	unittest.TestCase ):







  '''simple docstring'''







  lowerCAmelCase_					:       List[str]					  =    DPTImageProcessor if is_vision_available() else None


  def 				SCREAMING_SNAKE_CASE__      (    self     :						List[Any]			):






   """simple docstring"""






   UpperCAmelCase__      				=     DPTImageProcessingTester(self			)


  @property
  def 				SCREAMING_SNAKE_CASE__      (    self     :						Any			):






   """simple docstring"""






   return self.image_processor_tester.prepare_image_processor_dict()


  def 				SCREAMING_SNAKE_CASE__      (    self     :						str			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.image_processing_class(**self.image_processor_dict			)
   self.assertTrue(hasattr(snake_case_    ,		"""image_mean"""			)			)
   self.assertTrue(hasattr(snake_case_    ,		"""image_std"""			)			)
   self.assertTrue(hasattr(snake_case_    ,		"""do_normalize"""			)			)
   self.assertTrue(hasattr(snake_case_    ,		"""do_resize"""			)			)
   self.assertTrue(hasattr(snake_case_    ,		"""size"""			)			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Union[str, Any]			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.image_processing_class.from_dict(self.image_processor_dict			)
   self.assertEqual(image_processor.size    ,		{"""height""": 18, """width""": 18}			)

   UpperCAmelCase__      				=     self.image_processing_class.from_dict(self.image_processor_dict    ,		size=42			)
   self.assertEqual(image_processor.size    ,		{"""height""": 42, """width""": 42}			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Optional[int]			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.image_processing_class(**self.image_processor_dict			)
   # create random PIL images
   UpperCAmelCase__      				=     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=snake_case_			)
   for image in image_inputs:
    self.assertIsInstance(snake_case_    ,		Image.Image			)

   # Test not batched input
   UpperCAmelCase__      				=     image_processing(image_inputs[0]    ,		return_tensors="""pt"""			).pixel_values
   self.assertEqual(
       encoded_images.shape    ,		(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )    ,		)

   # Test batched
   UpperCAmelCase__      				=     image_processing(snake_case_    ,		return_tensors="""pt"""			).pixel_values
   self.assertEqual(
       encoded_images.shape    ,		(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )    ,		)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Any			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.image_processing_class(**self.image_processor_dict			)
   # create random numpy tensors
   UpperCAmelCase__      				=     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=snake_case_    ,		numpify=snake_case_			)
   for image in image_inputs:
    self.assertIsInstance(snake_case_    ,		np.ndarray			)

   # Test not batched input
   UpperCAmelCase__      				=     image_processing(image_inputs[0]    ,		return_tensors="""pt"""			).pixel_values
   self.assertEqual(
       encoded_images.shape    ,		(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )    ,		)

   # Test batched
   UpperCAmelCase__      				=     image_processing(snake_case_    ,		return_tensors="""pt"""			).pixel_values
   self.assertEqual(
       encoded_images.shape    ,		(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )    ,		)



  def 				SCREAMING_SNAKE_CASE__      (    self     :						int			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.image_processing_class(**self.image_processor_dict			)
   # create random PyTorch tensors
   UpperCAmelCase__      				=     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=snake_case_    ,		torchify=snake_case_			)
   for image in image_inputs:
    self.assertIsInstance(snake_case_    ,		torch.Tensor			)

   # Test not batched input
   UpperCAmelCase__      				=     image_processing(image_inputs[0]    ,		return_tensors="""pt"""			).pixel_values
   self.assertEqual(
       encoded_images.shape    ,		(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )    ,		)

   # Test batched
   UpperCAmelCase__      				=     image_processing(snake_case_    ,		return_tensors="""pt"""			).pixel_values
   self.assertEqual(
       encoded_images.shape    ,		(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.size["""height"""],
           self.image_processor_tester.size["""width"""],
       )    ,		)

"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'glpn'





			def __init__(			self   ,      snake_case_=3   ,      snake_case_=4   ,      snake_case_=[2, 2, 2, 2]   ,      snake_case_=[8, 4, 2, 1]   ,      snake_case_=[32, 64, 160, 256]   ,      snake_case_=[7, 3, 3, 3]   ,      snake_case_=[4, 2, 2, 2]   ,      snake_case_=[1, 2, 5, 8]   ,      snake_case_=[4, 4, 4, 4]   ,      snake_case_="gelu"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.02   ,      snake_case_=0.1   ,      snake_case_=1E-6   ,      snake_case_=64   ,      snake_case_=10   ,      snake_case_=-1   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)

										A__       :     str								=			num_channels
										A__       :     List[Any]								=			num_encoder_blocks
										A__       :     List[str]								=			depths
										A__       :     int								=			sr_ratios
										A__       :     Optional[Any]								=			hidden_sizes
										A__       :     int								=			patch_sizes
										A__       :     str								=			strides
										A__       :     int								=			mlp_ratios
										A__       :     Optional[int]								=			num_attention_heads
										A__       :     Optional[Any]								=			hidden_act
										A__       :     Optional[Any]								=			hidden_dropout_prob
										A__       :     Optional[Any]								=			attention_probs_dropout_prob
										A__       :     List[str]								=			initializer_range
										A__       :     Dict								=			drop_path_rate
										A__       :     Any								=			layer_norm_eps
										A__       :     Any								=			decoder_hidden_size
										A__       :     Optional[Any]								=			max_depth
										A__       :     List[Any]								=			head_in_index





