



"""simple docstring"""
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


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

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

def       _snake_case						(						UpperCamelCase						:		Any			,		UpperCamelCase						:		List[str]						):
 UpperCAmelCase	:					int	=	x.reshape(-1			,		3						)
 UpperCAmelCase	:					Union[str, Any]	=	squared_euclidean_distance(UpperCamelCase			,		UpperCamelCase						)
 return np.argmin(UpperCamelCase			,		axis=1						)



class       SCREAMING_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      ,     )  ->      None:





    '''simple docstring'''




    super().__init__(**_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Any	=	size if size is not None else {"""height""": 256, """width""": 256}
    UpperCAmelCase	:					Tuple	=	get_size_dict(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Any	=	np.array(_SCREAMING_SNAKE_CASE						) if clusters is not None else None
    UpperCAmelCase	:					Optional[Any]	=	do_resize
    UpperCAmelCase	:					str	=	size
    UpperCAmelCase	:					Optional[Any]	=	resample
    UpperCAmelCase	:					Optional[int]	=	do_normalize
    UpperCAmelCase	:					List[str]	=	do_color_quantize

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,     _SCREAMING_SNAKE_CASE = None      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      np.ndarray:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     )  ->      np.ndarray:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	rescale(image=_SCREAMING_SNAKE_CASE      ,     scale=1 / 127.5      ,     data_format=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[int]	=	image - 1
    return image




   def 	SCREAMING_SNAKE_CASE     (   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      ,     )  ->      PIL.Image.Image:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	do_resize if do_resize is not None else self.do_resize
    UpperCAmelCase	:					List[str]	=	size if size is not None else self.size
    UpperCAmelCase	:					Tuple	=	get_size_dict(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[Any]	=	resample if resample is not None else self.resample
    UpperCAmelCase	:					Optional[Any]	=	do_normalize if do_normalize is not None else self.do_normalize
    UpperCAmelCase	:					List[Any]	=	do_color_quantize if do_color_quantize is not None else self.do_color_quantize
    UpperCAmelCase	:					Optional[int]	=	clusters if clusters is not None else self.clusters
    UpperCAmelCase	:					Any	=	np.array(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					int	=	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.
    UpperCAmelCase	:					Optional[int]	=	[to_numpy_array(_SCREAMING_SNAKE_CASE						) for image in images]

    if do_resize:
     UpperCAmelCase	:					List[Any]	=	[self.resize(image=_SCREAMING_SNAKE_CASE      ,     size=_SCREAMING_SNAKE_CASE      ,     resample=_SCREAMING_SNAKE_CASE						) for image in images]

    if do_normalize:
     UpperCAmelCase	:					Union[str, Any]	=	[self.normalize(image=_SCREAMING_SNAKE_CASE						) for image in images]

    if do_color_quantize:
     UpperCAmelCase	:					List[Any]	=	[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)
     UpperCAmelCase	:					Dict	=	np.array(_SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					int	=	color_quantize(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						).reshape(images.shape[:-1]						)

     # flatten to (batch_size, height*width)
     UpperCAmelCase	:					int	=	images.shape[0]
     UpperCAmelCase	:					List[Any]	=	images.reshape(_SCREAMING_SNAKE_CASE      ,     -1						)

     # We need to convert back to a list of images to keep consistent behaviour across processors.
     UpperCAmelCase	:					List[str]	=	list(_SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					List[str]	=	[to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						) for image in images]

    UpperCAmelCase	:					Optional[Any]	=	{"""input_ids""": images}
    return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,     tensor_type=_SCREAMING_SNAKE_CASE						)





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:   List[str]								=		{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
          "CLIPSegModel",
          "CLIPSegPreTrainedModel",
          "CLIPSegTextModel",
          "CLIPSegVisionModel",
          "CLIPSegForImageSegmentation",
      ]

if TYPE_CHECKING:
      from .configuration_clipseg import (
          CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
          CLIPSegConfig,
          CLIPSegTextConfig,
          CLIPSegVisionConfig,
      )
      from .processing_clipseg import CLIPSegProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_clipseg import (
                CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                CLIPSegForImageSegmentation,
                CLIPSegModel,
                CLIPSegPreTrainedModel,
                CLIPSegTextModel,
                CLIPSegVisionModel,
            )

else:
      import sys

      A:   Tuple								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
