


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


a_ :str  							=			logging.get_logger(__name__)




def    a   (							A__      ,    A__      ,    A__      ,    A__							)	-> Tuple[int, int]:




      '''simple docstring'''

      def constraint_to_multiple_of(A__      ,    A__      ,    A__=0      ,    A__=None							):
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		round(val / multiple							) * multiple

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

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

            return x

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		(output_size, output_size) if isinstance(A__      ,    A__							) else output_size

      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Tuple						     =		get_image_size(A__							)
      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  List[str]						     =		output_size

      # determine new height and width
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		output_height / input_height
      SCREAMING_SNAKE_CASE__       :  Dict						     =		output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width							) < abs(1 - scale_height							):
                  # fit width
                  SCREAMING_SNAKE_CASE__       :  List[str]						     =		scale_width
            else:
                  # fit height
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		scale_height

      SCREAMING_SNAKE_CASE__       :  int						     =		constraint_to_multiple_of(scale_height * input_height      ,    multiple=A__							)
      SCREAMING_SNAKE_CASE__       :  int						     =		constraint_to_multiple_of(scale_width * input_width      ,    multiple=A__							)

      return (new_height, new_width)





class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			List[str]      = ['''pixel_values''']







     def __init__(						self :						List[Any]   ,   _lowercase :						bool = True   ,   _lowercase :						Dict[str, int] = None   ,   _lowercase :						PILImageResampling = PILImageResampling.BILINEAR   ,   _lowercase :						bool = False   ,   _lowercase :						int = 1   ,   _lowercase :						bool = True   ,   _lowercase :						Union[int, float] = 1 / 2_55   ,   _lowercase :						bool = True   ,   _lowercase :						Optional[Union[float, List[float]]] = None   ,   _lowercase :						Optional[Union[float, List[float]]] = None   ,   **_lowercase :						List[Any]   ,   ):
           super().__init__(**_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		size if size is not None else {'''height''': 3_84, '''width''': 3_84}
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		get_size_dict(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		do_resize
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		size
           SCREAMING_SNAKE_CASE__       :  int						     =		keep_aspect_ratio
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		ensure_multiple_of
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		resample
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		do_rescale
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		rescale_factor
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		do_normalize
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           SCREAMING_SNAKE_CASE__       :  Any						     =		image_std if image_std is not None else IMAGENET_STANDARD_STD







     def 							lowercase__       (						self :						Optional[int]   ,   _lowercase :						np.ndarray   ,   _lowercase :						Dict[str, int]   ,   _lowercase :						bool = False   ,   _lowercase :						int = 1   ,   _lowercase :						PILImageResampling = PILImageResampling.BICUBIC   ,   _lowercase :						Optional[Union[str, ChannelDimension]] = None   ,   **_lowercase :						Optional[int]   ,   ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		get_size_dict(_lowercase      )
           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()}"""      )
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		get_resize_output_image_size(
               _lowercase   ,   output_size=(size['''height'''], size['''width'''])   ,   keep_aspect_ratio=_lowercase   ,   multiple=_lowercase   ,   )
           return resize(_lowercase   ,   size=_lowercase   ,   resample=_lowercase   ,   data_format=_lowercase   ,   **_lowercase      )







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







     def 							lowercase__       (						self :						List[str]   ,   _lowercase :						np.ndarray   ,   _lowercase :						Union[float, List[float]]   ,   _lowercase :						Union[float, List[float]]   ,   _lowercase :						Optional[Union[str, ChannelDimension]] = None   ,   **_lowercase :						Optional[Any]   ,   ):
           return normalize(_lowercase   ,   mean=_lowercase   ,   std=_lowercase   ,   data_format=_lowercase   ,   **_lowercase      )







     def 							lowercase__       (						self :						Optional[Any]   ,   _lowercase :						ImageInput   ,   _lowercase :						bool = None   ,   _lowercase :						int = None   ,   _lowercase :						bool = None   ,   _lowercase :						int = None   ,   _lowercase :						PILImageResampling = None   ,   _lowercase :						bool = None   ,   _lowercase :						float = None   ,   _lowercase :						bool = None   ,   _lowercase :						Optional[Union[float, List[float]]] = None   ,   _lowercase :						Optional[Union[float, List[float]]] = None   ,   _lowercase :						Optional[Union[str, TensorType]] = None   ,   _lowercase :						ChannelDimension = ChannelDimension.FIRST   ,   **_lowercase :						Tuple   ,   ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		do_resize if do_resize is not None else self.do_resize
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		size if size is not None else self.size
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		get_size_dict(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		resample if resample is not None else self.resample
           SCREAMING_SNAKE_CASE__       :  str						     =		do_rescale if do_rescale is not None else self.do_rescale
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		rescale_factor if rescale_factor is not None else self.rescale_factor
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		do_normalize if do_normalize is not None else self.do_normalize
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		image_mean if image_mean is not None else self.image_mean
           SCREAMING_SNAKE_CASE__       :  str						     =		image_std if image_std is not None else self.image_std

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		make_list_of_images(_lowercase      )

           if not valid_images(_lowercase      ):
                 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.
           SCREAMING_SNAKE_CASE__       :  str						     =		[to_numpy_array(_lowercase      ) for image in images]

           if do_resize:
                 SCREAMING_SNAKE_CASE__       :  Any						     =		[self.resize(image=_lowercase   ,   size=_lowercase   ,   resample=_lowercase      ) for image in images]

           if do_rescale:
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		[self.rescale(image=_lowercase   ,   scale=_lowercase      ) for image in images]

           if do_normalize:
                 SCREAMING_SNAKE_CASE__       :  Any						     =		[self.normalize(image=_lowercase   ,   mean=_lowercase   ,   std=_lowercase      ) for image in images]

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[to_channel_dimension_format(_lowercase   ,   _lowercase      ) for image in images]

           SCREAMING_SNAKE_CASE__       :  str						     =		{'''pixel_values''': images}
           return BatchFeature(data=_lowercase   ,   tensor_type=_lowercase      )







     def 							lowercase__       (						self :						Tuple   ,   _lowercase :						Optional[Any]   ,   _lowercase :						List[Tuple] = None      ):
           SCREAMING_SNAKE_CASE__       :  str						     =		outputs.logits

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

                 if is_torch_tensor(_lowercase      ):
                       SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		target_sizes.numpy()

                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		[]

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

           return semantic_segmentation










"""simple docstring"""




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format




class 			__snake_case   :
							__a					 =       None




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      self.feature_extraction_class(**self.feat_extract_dict   )
										__lowerCamelCase          =      json.loads(feat_extract.to_json_string()   )
										for key, value in self.feat_extract_dict.items():
													self.assertEqual(obj[key]     ,							A_   )




							def   __a   (     self:    List[str]   ):
										__lowerCamelCase          =      self.feature_extraction_class(**self.feat_extract_dict   )

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCamelCase          =      os.path.join(A_     ,							"""feat_extract.json"""   )
													feat_extract_first.to_json_file(A_   )
													__lowerCamelCase          =      self.feature_extraction_class.from_json_file(A_   )

										self.assertEqual(feat_extract_second.to_dict()     ,							feat_extract_first.to_dict()   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      self.feature_extraction_class(**self.feat_extract_dict   )

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCamelCase          =      feat_extract_first.save_pretrained(A_   )[0]
													check_json_file_has_correct_format(A_   )
													__lowerCamelCase          =      self.feature_extraction_class.from_pretrained(A_   )

										self.assertEqual(feat_extract_second.to_dict()     ,							feat_extract_first.to_dict()   )




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      self.feature_extraction_class()
										self.assertIsNotNone(A_   )




