



"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


UpperCAmelCase						:		Tuple			= logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   ["pixel_values"]

    def __init__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				Dict[str, int] = None ,  lowerCAmelCase_			:				PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				Union[int, float] = 1 / 2_5_5 ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				Dict[str, int] = None ,  lowerCAmelCase_			:				bool = True ,  **lowerCAmelCase_			:				Tuple ,  ):







      """simple docstring"""







      super().__init__(**lowerCAmelCase_)
      lowercase_								= size if size is not None else {"""shortest_edge""": 2_2_4}
      lowercase_								= get_size_dict(lowerCAmelCase_ ,  default_to_square=lowerCAmelCase_)
      lowercase_								= crop_size if crop_size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
      lowercase_								= get_size_dict(lowerCAmelCase_ ,  param_name="""crop_size""")

      lowercase_								= do_resize
      lowercase_								= size
      lowercase_								= resample
      lowercase_								= do_rescale
      lowercase_								= rescale_factor
      lowercase_								= do_center_crop
      lowercase_								= crop_size
      lowercase_								= do_flip_channel_order

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				np.ndarray ,  lowerCAmelCase_			:				Dict[str, int] ,  lowerCAmelCase_			:				PILImageResampling = PIL.Image.BILINEAR ,  lowerCAmelCase_			:				Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:				Optional[int] ,  ):







      """simple docstring"""







      lowercase_								= get_size_dict(lowerCAmelCase_ ,  default_to_square=lowerCAmelCase_)
      if "shortest_edge" not in size:
        raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''')
      lowercase_								= get_resize_output_image_size(lowerCAmelCase_ ,  size=size["""shortest_edge"""] ,  default_to_square=lowerCAmelCase_)
      return resize(lowerCAmelCase_ ,  size=lowerCAmelCase_ ,  resample=lowerCAmelCase_ ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				np.ndarray ,  lowerCAmelCase_			:				Dict[str, int] ,  lowerCAmelCase_			:				Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:				Union[str, Any] ,  ):







      """simple docstring"""







      lowercase_								= get_size_dict(lowerCAmelCase_)
      if "height" not in size or "width" not in size:
        raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''')
      return center_crop(lowerCAmelCase_ ,  size=(size["""height"""], size["""width"""]) ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[str] ,  lowerCAmelCase_			:				np.ndarray ,  lowerCAmelCase_			:				Union[int, float] ,  lowerCAmelCase_			:				Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:				List[str] ,  ):







      """simple docstring"""







      return rescale(lowerCAmelCase_ ,  scale=lowerCAmelCase_ ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[str] ,  lowerCAmelCase_			:				np.ndarray ,  lowerCAmelCase_			:				Optional[Union[str, ChannelDimension]] = None):







      """simple docstring"""







      return flip_channel_order(lowerCAmelCase_ ,  data_format=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				ImageInput ,  lowerCAmelCase_			:				bool = None ,  lowerCAmelCase_			:				Dict[str, int] = None ,  lowerCAmelCase_			:				PILImageResampling = None ,  lowerCAmelCase_			:				bool = None ,  lowerCAmelCase_			:				float = None ,  lowerCAmelCase_			:				bool = None ,  lowerCAmelCase_			:				Dict[str, int] = None ,  lowerCAmelCase_			:				bool = None ,  lowerCAmelCase_			:				Optional[Union[str, TensorType]] = None ,  lowerCAmelCase_			:				ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase_			:				Optional[int] ,  ):







      """simple docstring"""







      lowercase_								= do_resize if do_resize is not None else self.do_resize
      lowercase_								= resample if resample is not None else self.resample
      lowercase_								= do_rescale if do_rescale is not None else self.do_rescale
      lowercase_								= rescale_factor if rescale_factor is not None else self.rescale_factor
      lowercase_								= do_center_crop if do_center_crop is not None else self.do_center_crop
      lowercase_								= (
          do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
      )

      lowercase_								= size if size is not None else self.size
      lowercase_								= get_size_dict(lowerCAmelCase_ ,  default_to_square=lowerCAmelCase_)
      lowercase_								= crop_size if crop_size is not None else self.crop_size
      lowercase_								= get_size_dict(lowerCAmelCase_ ,  param_name="""crop_size""")

      lowercase_								= make_list_of_images(lowerCAmelCase_)

      if not valid_images(lowerCAmelCase_):
        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:
        raise ValueError("""Size must be specified if do_resize is True.""")

      if do_rescale and rescale_factor is None:
        raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

      if do_center_crop and crop_size is None:
        raise ValueError("""Crop size must be specified if do_center_crop is True.""")

      # All transformations expect numpy arrays.
      lowercase_								= [to_numpy_array(lowerCAmelCase_) for image in images]

      if do_resize:
        lowercase_								= [self.resize(image=lowerCAmelCase_ ,  size=lowerCAmelCase_ ,  resample=lowerCAmelCase_) for image in images]

      if do_center_crop:
        lowercase_								= [self.center_crop(image=lowerCAmelCase_ ,  size=lowerCAmelCase_) for image in images]

      if do_rescale:
        lowercase_								= [self.rescale(image=lowerCAmelCase_ ,  scale=lowerCAmelCase_) for image in images]

      # the pretrained checkpoints assume images are BGR, not RGB
      if do_flip_channel_order:
        lowercase_								= [self.flip_channel_order(image=lowerCAmelCase_) for image in images]

      lowercase_								= [to_channel_dimension_format(lowerCAmelCase_ ,  lowerCAmelCase_) for image in images]

      lowercase_								= {"""pixel_values""": images}
      return BatchFeature(data=lowerCAmelCase_ ,  tensor_type=lowerCAmelCase_)




    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[Tuple] = None):







      """simple docstring"""







      lowercase_								= outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
        if len(lowerCAmelCase_) != len(lowerCAmelCase_):
          raise ValueError(
              """Make sure that you pass in as many target sizes as the batch dimension of the logits""")

        if is_torch_tensor(lowerCAmelCase_):
          lowercase_								= target_sizes.numpy()

        lowercase_								= []

        for idx in range(len(lowerCAmelCase_)):
          lowercase_								= torch.nn.functional.interpolate(
              logits[idx].unsqueeze(dim=0) ,  size=target_sizes[idx] ,  mode="""bilinear""" ,  align_corners=lowerCAmelCase_)
          lowercase_								= resized_logits[0].argmax(dim=0)
          semantic_segmentation.append(lowerCAmelCase_)
      else:
        lowercase_								= logits.argmax(dim=1)
        lowercase_								= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

      return semantic_segmentation






"""simple docstring"""
import os
from collections.abc import Iterator


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase = "."							) ->						Iterator[str]:







  '''simple docstring'''


  for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase							):
    lowercase_								= [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
    for filename in filenames:
      if filename == "__init__.py":
        continue
      if os.path.splitext(__lowerCAmelCase							)[1] in (".py", ".ipynb"):
        yield os.path.join(__lowerCAmelCase ,  __lowerCAmelCase							).lstrip("""./"""							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  return F'''{i * "  "}*''' if i else "\n##"


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						str:







  '''simple docstring'''


  lowercase_								= old_path.split(os.sep							)
  for i, new_part in enumerate(new_path.split(os.sep							)							):
    if (i + 1 > len(__lowerCAmelCase							) or old_parts[i] != new_part) and new_part:
      print(F'''{md_prefix(__lowerCAmelCase							)} {new_part.replace("_" ,  " "							).title()}'''							)
  return new_path


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase = "."							) ->						None:







  '''simple docstring'''


  lowercase_								= """"""
  for filepath in sorted(good_file_paths(__lowerCAmelCase							)							):
    lowercase_					,    lowercase_								= os.path.split(__lowerCAmelCase							)
    if filepath != old_path:
      lowercase_								= print_path(__lowerCAmelCase ,  __lowerCAmelCase							)
    lowercase_								= (filepath.count(os.sep							) + 1) if filepath else 0
    lowercase_								= F'''{filepath}/{filename}'''.replace(""" """ ,  """%20"""							)
    lowercase_								= os.path.splitext(filename.replace("""_""" ,  """ """							).title()							)[0]
    print(F'''{md_prefix(__lowerCAmelCase							)} [{filename}]({url})'''							)


if __name__ == "__main__":
       print_directory_md(".")

