


"""simple docstring"""

import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
       import torch

if is_vision_available():
       import PIL


UpperCAmelCase__					=logging.get_logger(__name__)


def lowerCAmelCase_   (					UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			Union[int, Iterable[int]]					, UpperCamelCase__						:			bool					, UpperCamelCase__						:			int			):






 """simple docstring"""







 def constraint_to_multiple_of(UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			Union[str, Any]					, UpperCamelCase__						:			Optional[int]=0					, UpperCamelCase__						:			List[Any]=None			):
  __lowercase						=						round(val / multiple			) * multiple

  if max_val is not None and x > max_val:
   __lowercase						=						math.floor(val / multiple			) * multiple

  if x < min_val:
   __lowercase						=						math.ceil(val / multiple			) * multiple

  return x

 __lowercase						=						(output_size, output_size) if isinstance(UpperCamelCase__					, UpperCamelCase__			) else output_size

 __lowercase				,		__lowercase						=						get_image_size(UpperCamelCase__			)
 __lowercase				,		__lowercase						=						output_size

 # determine new height and width
 __lowercase						=						output_height / input_height
 __lowercase						=						output_width / input_width

 if keep_aspect_ratio:
  # scale as little as possible
  if abs(1 - scale_width			) < abs(1 - scale_height			):
   # fit width
   __lowercase						=						scale_width
  else:
   # fit height
   __lowercase						=						scale_height

 __lowercase						=						constraint_to_multiple_of(scale_height * input_height					, multiple=UpperCamelCase__			)
 __lowercase						=						constraint_to_multiple_of(scale_width * input_width					, multiple=UpperCamelCase__			)

 return (new_height, new_width)





class  lowerCamelCase__    (		_a     ):
      a			:  Tuple           =		["""pixel_values"""]
      def __init__(		self						:						str   ,							A_						:						bool = True   ,							A_						:						Dict[str, int] = None   ,							A_						:						PILImageResampling = PILImageResampling.BILINEAR   ,							A_						:						bool = False   ,							A_						:						int = 1   ,							A_						:						bool = True   ,							A_						:						Union[int, float] = 1 / 2_5_5   ,							A_						:						bool = True   ,							A_						:						Optional[Union[float, List[float]]] = None   ,							A_						:						Optional[Union[float, List[float]]] = None   ,							**A_						:						int   ,							):






       '''simple docstring'''


       super().__init__(**A_				)
       __lowercase						=						size if size is not None else {"""height""": 3_8_4, """width""": 3_8_4}
       __lowercase						=						get_size_dict(A_				)
       __lowercase						=						do_resize
       __lowercase						=						size
       __lowercase						=						keep_aspect_ratio
       __lowercase						=						ensure_multiple_of
       __lowercase						=						resample
       __lowercase						=						do_rescale
       __lowercase						=						rescale_factor
       __lowercase						=						do_normalize
       __lowercase						=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       __lowercase						=						image_std if image_std is not None else IMAGENET_STANDARD_STD
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						np.ndarray   ,							A_						:						Dict[str, int]   ,							A_						:						bool = False   ,							A_						:						int = 1   ,							A_						:						PILImageResampling = PILImageResampling.BICUBIC   ,							A_						:						Optional[Union[str, ChannelDimension]] = None   ,							**A_						:						int   ,							):






       '''simple docstring'''


       __lowercase						=						get_size_dict(A_				)
       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()}'''				)
       __lowercase						=						get_resize_output_image_size(
           A_   ,							output_size=(size["""height"""], size["""width"""])   ,							keep_aspect_ratio=A_   ,							multiple=A_   ,							)
       return resize(A_   ,							size=A_   ,							resample=A_   ,							data_format=A_   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						np.ndarray   ,							A_						:						Union[int, float]   ,							A_						:						Optional[Union[str, ChannelDimension]] = None   ,							**A_						:						Optional[Any]   ,							):






       '''simple docstring'''


       return rescale(A_   ,							scale=A_   ,							data_format=A_   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						np.ndarray   ,							A_						:						Union[float, List[float]]   ,							A_						:						Union[float, List[float]]   ,							A_						:						Optional[Union[str, ChannelDimension]] = None   ,							**A_						:						Union[str, Any]   ,							):






       '''simple docstring'''


       return normalize(A_   ,							mean=A_   ,							std=A_   ,							data_format=A_   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						ImageInput   ,							A_						:						bool = None   ,							A_						:						int = None   ,							A_						:						bool = None   ,							A_						:						int = None   ,							A_						:						PILImageResampling = None   ,							A_						:						bool = None   ,							A_						:						float = None   ,							A_						:						bool = None   ,							A_						:						Optional[Union[float, List[float]]] = None   ,							A_						:						Optional[Union[float, List[float]]] = None   ,							A_						:						Optional[Union[str, TensorType]] = None   ,							A_						:						ChannelDimension = ChannelDimension.FIRST   ,							**A_						:						Optional[Any]   ,							):






       '''simple docstring'''


       __lowercase						=						do_resize if do_resize is not None else self.do_resize
       __lowercase						=						size if size is not None else self.size
       __lowercase						=						get_size_dict(A_				)
       __lowercase						=						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
       __lowercase						=						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
       __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_normalize if do_normalize is not None else self.do_normalize
       __lowercase						=						image_mean if image_mean is not None else self.image_mean
       __lowercase						=						image_std if image_std is not None else self.image_std

       __lowercase						=						make_list_of_images(A_				)

       if not valid_images(A_				):
        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_rescale and rescale_factor is None:
        raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

       if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("""Image mean and std must be specified if do_normalize is True."""				)

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

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

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

       if do_normalize:
        __lowercase						=						[self.normalize(image=A_   ,							mean=A_   ,							std=A_				) for image in images]

       __lowercase						=						[to_channel_dimension_format(A_   ,							A_				) for image in images]

       __lowercase						=						{"""pixel_values""": images}
       return BatchFeature(data=A_   ,							tensor_type=A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Dict   ,							A_						:						List[Tuple] = None				):






       '''simple docstring'''


       __lowercase						=						outputs.logits

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

        if is_torch_tensor(A_				):
         __lowercase						=						target_sizes.numpy()

        __lowercase						=						[]

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

       return semantic_segmentation




"""simple docstring"""

from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			str					, UpperCamelCase__						:			Optional[str] = None			):






 """simple docstring"""







 if version.parse(hfh.__version__			).release < version.parse("""0.11.0"""			).release:
  # old versions of hfh don't url-encode the file path
  __lowercase						=						quote(UpperCamelCase__			)
 return hfh.hf_hub_url(UpperCamelCase__					, UpperCamelCase__					, repo_type="""dataset"""					, revision=UpperCamelCase__			)
