

"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCamelCase__ :     List[str]							  =     logging.get_logger(__name__)

lowerCamelCase__ :     Union[str, Any]							  =     {
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}






class 			_UpperCAmelCase	(						lowerCAmelCase_):
			__a       :				List[str]     =							'dpt'
			def __init__(						self					,						_A=7_68					,						_A=12					,						_A=12					,						_A=30_72					,						_A="gelu"					,						_A=0.0					,						_A=0.0					,						_A=0.02					,						_A=1e-12					,						_A=3_84					,						_A=16					,						_A=3					,						_A=False					,						_A=True					,						_A=[2, 5, 8, 11]					,						_A="project"					,						_A=[4, 2, 1, 0.5]					,						_A=[96, 1_92, 3_84, 7_68]					,						_A=2_56					,						_A=-1					,						_A=False					,						_A=True					,						_A=0.4					,						_A=2_55					,						_A=0.1					,						_A=[1, 10_24, 24, 24]					,						_A=[0, 1]					,						_A=None					,						**_A					,						)			->			List[str]:






				'''simple docstring'''







				super().__init__(**__lowerCAmelCase   )

				_UpperCAmelCase			:							str	  =    hidden_size
				_UpperCAmelCase			:							Union[str, Any]	  =    is_hybrid

				if self.is_hybrid:
					if backbone_config is None:
						logger.info("""Initializing the config with a `BiT` backbone."""   )
						_UpperCAmelCase			:							str	  =    {
						    """global_padding""": """same""",
						    """layer_type""": """bottleneck""",
						    """depths""": [3, 4, 9],
						    """out_features""": ["""stage1""", """stage2""", """stage3"""],
						    """embedding_dynamic_padding""": True,
						}
						_UpperCAmelCase			:							Dict	  =    BitConfig(**__lowerCAmelCase   )
					elif isinstance(__lowerCAmelCase					,						__lowerCAmelCase   ):
						logger.info("""Initializing the config with a `BiT` backbone."""   )
						_UpperCAmelCase			:							Any	  =    BitConfig(**__lowerCAmelCase   )
					elif isinstance(__lowerCAmelCase					,						__lowerCAmelCase   ):
						_UpperCAmelCase			:							Tuple	  =    backbone_config
					else:
						raise ValueError(
						    f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''   )

					_UpperCAmelCase			:							Tuple	  =    backbone_featmap_shape
					_UpperCAmelCase			:							List[Any]	  =    neck_ignore_stages

					if readout_type != "project":
						raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""   )
				else:
					_UpperCAmelCase			:							List[Any]	  =    None
					_UpperCAmelCase			:							List[Any]	  =    None
					_UpperCAmelCase			:							Union[str, Any]	  =    []

				_UpperCAmelCase			:							int	  =    num_hidden_layers
				_UpperCAmelCase			:							int	  =    num_attention_heads
				_UpperCAmelCase			:							Any	  =    intermediate_size
				_UpperCAmelCase			:							List[str]	  =    hidden_act
				_UpperCAmelCase			:							Tuple	  =    hidden_dropout_prob
				_UpperCAmelCase			:							Optional[int]	  =    attention_probs_dropout_prob
				_UpperCAmelCase			:							int	  =    initializer_range
				_UpperCAmelCase			:							Optional[int]	  =    layer_norm_eps
				_UpperCAmelCase			:							List[Any]	  =    image_size
				_UpperCAmelCase			:							Any	  =    patch_size
				_UpperCAmelCase			:							Tuple	  =    num_channels
				_UpperCAmelCase			:							Union[str, Any]	  =    qkv_bias
				_UpperCAmelCase			:							Union[str, Any]	  =    backbone_out_indices
				if readout_type not in ["ignore", "add", "project"]:
					raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""   )
				_UpperCAmelCase			:							Any	  =    readout_type
				_UpperCAmelCase			:							Dict	  =    reassemble_factors
				_UpperCAmelCase			:							Dict	  =    neck_hidden_sizes
				_UpperCAmelCase			:							str	  =    fusion_hidden_size
				_UpperCAmelCase			:							Dict	  =    head_in_index
				_UpperCAmelCase			:							Any	  =    use_batch_norm_in_fusion_residual
				# auxiliary head attributes (semantic segmentation)
				_UpperCAmelCase			:							Optional[int]	  =    use_auxiliary_head
				_UpperCAmelCase			:							Tuple	  =    auxiliary_loss_weight
				_UpperCAmelCase			:							str	  =    semantic_loss_ignore_index
				_UpperCAmelCase			:							Dict	  =    semantic_classifier_dropout
			def 							__snake_case       (						self   )			->			int:






				'''simple docstring'''







				_UpperCAmelCase			:							Tuple	  =    copy.deepcopy(self.__dict__   )

				if output["backbone_config"] is not None:
					_UpperCAmelCase			:							str	  =    self.backbone_config.to_dict()

				_UpperCAmelCase			:							Dict	  =    self.__class__.model_type
				return output






"""simple docstring"""


import unittest

import numpy as np

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

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 MobileViTImageProcessor


class 				a (						unittest.TestCase      ):


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Optional[Any]=7      , __lowerCAmelCase		:     Optional[Any]=3      , __lowerCAmelCase		:     Optional[Any]=18      , __lowerCAmelCase		:     str=30      , __lowerCAmelCase		:     List[str]=400      , __lowerCAmelCase		:     Union[str, Any]=True      , __lowerCAmelCase		:     str=None      , __lowerCAmelCase		:     Optional[int]=True      , __lowerCAmelCase		:     int=None      , __lowerCAmelCase		:     List[str]=True      , ):
      _UpperCAmelCase         =					size if size is not None else {"""shortest_edge""": 20}
      _UpperCAmelCase         =					crop_size if crop_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_center_crop
      _UpperCAmelCase         =					crop_size
      _UpperCAmelCase         =					do_flip_channel_order


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_flip_channel_order": self.do_flip_channel_order,
      }



@require_torch
@require_vision
class 				a (						lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[int]   							=							MobileViTImageProcessor if is_vision_available() else None


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					MobileViTImageProcessingTester(self    )


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return self.image_processor_tester.prepare_image_processor_dict()


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_resize"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """size"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_flip_channel_order"""    )    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 20}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 18, """width""": 18}    )

      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 42}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 84, """width""": 84}    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      pass


    def    lowerCAmelCase_     (    self		:     Dict    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PIL images
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     str    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random numpy tensors
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase      , numpify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PyTorch tensors
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase      , torchify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )
