


'''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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
     import torch


a_  :				int						= logging.get_logger(__name__)





class      snake_case    (    _SCREAMING_SNAKE_CASE			):





  """simple docstring"""




  _lowerCamelCase		    =  ["pixel_values"]






  def __init__(    self			,  UpperCamelCase = True			,  UpperCamelCase = None			,  UpperCamelCase = PILImageResampling.BILINEAR			,  UpperCamelCase = True			,  UpperCamelCase = None			,  UpperCamelCase = True			,  UpperCamelCase = 1 / 255			,  UpperCamelCase = True			,  UpperCamelCase = None			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   super().__init__(**_lowerCAmelCase     )
   lowerCamelCase_      =  size if size is not None else {"shortest_edge": 256}
   lowerCamelCase_      =  get_size_dict(_lowerCAmelCase			,  default_to_square=_lowerCAmelCase     )
   lowerCamelCase_      =  crop_size if crop_size is not None else {"height": 224, "width": 224}
   lowerCamelCase_      =  get_size_dict(_lowerCAmelCase			,  param_name="crop_size"     )
   lowerCamelCase_      =  do_resize
   lowerCamelCase_      =  size
   lowerCamelCase_      =  resample
   lowerCamelCase_      =  do_center_crop
   lowerCamelCase_      =  crop_size
   lowerCamelCase_      =  do_rescale
   lowerCamelCase_      =  rescale_factor
   lowerCamelCase_      =  do_normalize
   lowerCamelCase_      =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   lowerCamelCase_      =  image_std if image_std is not None else IMAGENET_STANDARD_STD






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = PILImageResampling.BICUBIC			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  get_size_dict(_lowerCAmelCase			,  default_to_square=_lowerCAmelCase     )
   if "shortest_edge" not in size:
    raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''     )
   lowerCamelCase_      =  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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  get_size_dict(_lowerCAmelCase     )
   if "height" not in size or "width" not in size:
    raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''     )
   return center_crop(_lowerCAmelCase			,  size=(size["height"], size["width"])			,  data_format=_lowerCAmelCase			,  **_lowerCAmelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = None			,  **UpperCamelCase     ):







   """simple docstring"""
   return rescale(_lowerCAmelCase			,  scale=_lowerCAmelCase			,  data_format=_lowerCAmelCase			,  **_lowerCAmelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   return normalize(_lowerCAmelCase			,  mean=_lowerCAmelCase			,  std=_lowerCAmelCase			,  data_format=_lowerCAmelCase			,  **_lowerCAmelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = ChannelDimension.FIRST			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  do_resize if do_resize is not None else self.do_resize
   lowerCamelCase_      =  size if size is not None else self.size
   lowerCamelCase_      =  get_size_dict(_lowerCAmelCase			,  default_to_square=_lowerCAmelCase     )
   lowerCamelCase_      =  resample if resample is not None else self.resample
   lowerCamelCase_      =  do_center_crop if do_center_crop is not None else self.do_center_crop
   lowerCamelCase_      =  crop_size if crop_size is not None else self.crop_size
   lowerCamelCase_      =  get_size_dict(_lowerCAmelCase			,  param_name="crop_size"     )
   lowerCamelCase_      =  do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase_      =  rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCamelCase_      =  do_normalize if do_normalize is not None else self.do_normalize
   lowerCamelCase_      =  image_mean if image_mean is not None else self.image_mean
   lowerCamelCase_      =  image_std if image_std is not None else self.image_std

   lowerCamelCase_      =  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_center_crop and crop_size is None:
    raise ValueError("Crop size must be specified if do_center_crop 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.
   lowerCamelCase_      =  [to_numpy_array(_lowerCAmelCase     ) for image in images]

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

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

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

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

   lowerCamelCase_      =  [to_channel_dimension_format(_lowerCAmelCase			,  _lowerCAmelCase     ) for image in images]

   lowerCamelCase_      =  {"pixel_values": images}
   return BatchFeature(data=_lowerCAmelCase			,  tensor_type=_lowerCAmelCase     )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  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     ):
     lowerCamelCase_      =  target_sizes.numpy()

    lowerCamelCase_      =  []

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

   return semantic_segmentation









import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


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





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 def __init__(							self  :		str				,				*_lowerCAmelCase  :		Optional[Any]				,				**_lowerCAmelCase  :		Optional[Any]						):
       warnings.warn(
           'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use ImageGPTImageProcessor instead.'				,				_lowerCAmelCase				,				)
       super().__init__(*_lowerCAmelCase				,				**_lowerCAmelCase						)