from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


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

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Tuple:


  '''simple docstring'''






  lowercase__  :   str    		=      b.T
  lowercase__  :   Tuple    		=      np.sum(np.square(lowercase_   ) , axis=1   )
  lowercase__  :   int    		=      np.sum(np.square(lowercase_   ) , axis=0   )
  lowercase__  :   Optional[Any]    		=      np.matmul(lowercase_ , lowercase_   )
  lowercase__  :   Dict    		=      aa[:, None] - 2 * ab + ba[None, :]
  return d

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	List[str]:


  '''simple docstring'''






  lowercase__  :   int    		=      x.reshape(-1 , 3   )
  lowercase__  :   Dict    		=      squared_euclidean_distance(lowercase_ , lowercase_   )
  return np.argmin(lowercase_ , axis=1   )



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			['pixel_values']


 def __init__(					self					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = True					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR					,  SCREAMING_SNAKE_CASE_ = True					,  SCREAMING_SNAKE_CASE_ = True					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[str]    		=      size if size is not None else {"""height""": 2_56, """width""": 2_56}
   lowercase__  :   Tuple    		=      get_size_dict(SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[str]    		=      np.array(SCREAMING_SNAKE_CASE_) if clusters is not None else None
   lowercase__  :   Union[str, Any]    		=      do_resize
   lowercase__  :   Tuple    		=      size
   lowercase__  :   Optional[Any]    		=      resample
   lowercase__  :   int    		=      do_normalize
   lowercase__  :   List[str]    		=      do_color_quantize


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR					,  SCREAMING_SNAKE_CASE_ = None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      get_size_dict(SCREAMING_SNAKE_CASE_)
   if "height" not in size or "width" not in size:
     raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}')
   return resize(
       SCREAMING_SNAKE_CASE_					,  size=(size["""height"""], size["""width"""])					,  resample=SCREAMING_SNAKE_CASE_					,  data_format=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None					,  ):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      rescale(image=SCREAMING_SNAKE_CASE_					,  scale=1 / 1_27.5					,  data_format=SCREAMING_SNAKE_CASE_)
   lowercase__  :   Union[str, Any]    		=      image - 1
   return image



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      do_resize if do_resize is not None else self.do_resize
   lowercase__  :   List[str]    		=      size if size is not None else self.size
   lowercase__  :   Optional[int]    		=      get_size_dict(SCREAMING_SNAKE_CASE_)
   lowercase__  :   int    		=      resample if resample is not None else self.resample
   lowercase__  :   Dict    		=      do_normalize if do_normalize is not None else self.do_normalize
   lowercase__  :   List[str]    		=      do_color_quantize if do_color_quantize is not None else self.do_color_quantize
   lowercase__  :   Union[str, Any]    		=      clusters if clusters is not None else self.clusters
   lowercase__  :   int    		=      np.array(SCREAMING_SNAKE_CASE_)

   lowercase__  :   Any    		=      make_list_of_images(SCREAMING_SNAKE_CASE_)

   if not valid_images(SCREAMING_SNAKE_CASE_):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray.""")

   if do_resize and size is None or resample is None:
     raise ValueError("""Size and resample must be specified if do_resize is True.""")

   if do_color_quantize and clusters is None:
     raise ValueError("""Clusters must be specified if do_color_quantize is True.""")

   # All transformations expect numpy arrays.
   lowercase__  :   Dict    		=      [to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

   if do_resize:
     lowercase__  :   Tuple    		=      [self.resize(image=SCREAMING_SNAKE_CASE_					,  size=SCREAMING_SNAKE_CASE_					,  resample=SCREAMING_SNAKE_CASE_) for image in images]

   if do_normalize:
     lowercase__  :   List[Any]    		=      [self.normalize(image=SCREAMING_SNAKE_CASE_) for image in images]

   if do_color_quantize:
     lowercase__  :   Dict    		=      [to_channel_dimension_format(SCREAMING_SNAKE_CASE_					,  ChannelDimension.LAST) for image in images]
     # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
     lowercase__  :   int    		=      np.array(SCREAMING_SNAKE_CASE_)
     lowercase__  :   Optional[int]    		=      color_quantize(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_).reshape(images.shape[:-1])

     # flatten to (batch_size, height*width)
     lowercase__  :   Any    		=      images.shape[0]
     lowercase__  :   Dict    		=      images.reshape(SCREAMING_SNAKE_CASE_					,  -1)

     # We need to convert back to a list of images to keep consistent behaviour across processors.
     lowercase__  :   Any    		=      list(SCREAMING_SNAKE_CASE_)
   else:
     lowercase__  :   Tuple    		=      [to_channel_dimension_format(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__  :   List[Any]    		=      {"""input_ids""": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE_					,  tensor_type=SCREAMING_SNAKE_CASE_)








import copy

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


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

lowerCamelCase__    :		str          =   {
    """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       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Optional[int]        =			'dpt'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=7_68					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=30_72					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0_2					,  SCREAMING_SNAKE_CASE_=1E-12					,  SCREAMING_SNAKE_CASE_=3_84					,  SCREAMING_SNAKE_CASE_=16					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=[2, 5, 8, 11]					,  SCREAMING_SNAKE_CASE_="project"					,  SCREAMING_SNAKE_CASE_=[4, 2, 1, 0.5]					,  SCREAMING_SNAKE_CASE_=[96, 1_92, 3_84, 7_68]					,  SCREAMING_SNAKE_CASE_=2_56					,  SCREAMING_SNAKE_CASE_=-1					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=0.4					,  SCREAMING_SNAKE_CASE_=2_55					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=[1, 10_24, 24, 24]					,  SCREAMING_SNAKE_CASE_=[0, 1]					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[str]    		=      hidden_size
   lowercase__  :   str    		=      is_hybrid

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

     lowercase__  :   Optional[Any]    		=      backbone_featmap_shape
     lowercase__  :   List[str]    		=      neck_ignore_stages

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

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



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      copy.deepcopy(self.__dict__)

   if output["backbone_config"] is not None:
     lowercase__  :   Optional[Any]    		=      self.backbone_config.to_dict()

   lowercase__  :   Dict    		=      self.__class__.model_type
   return output






