






"""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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_UpperCamelCase     			=  logging.get_logger(__name__)


if is_vision_available():
  import PIL







class 					lowerCamelCase__					(       _A ):
    SCREAMING_SNAKE_CASE		=			["""pixel_values"""]






    def __init__( self	,A = True	,A = None	,A = PILImageResampling.BILINEAR	,A = True	,A = False	,A = True	,A = True	,A = 1 / 255	,A = True	,A = None	,A = None	,**A	,):
          super().__init__(**snake_case_					)

          UpperCAmelCase							=       size if size is not None else {"""height""": 2_560, """width""": 1_920}
          if isinstance(snake_case_	,(tuple, list)					):
                # The previous feature extractor size parameter was in (width, height) format
                UpperCAmelCase							=       size[::-1]
          UpperCAmelCase							=       get_size_dict(snake_case_					)

          UpperCAmelCase							=       do_resize
          UpperCAmelCase							=       size
          UpperCAmelCase							=       resample
          UpperCAmelCase							=       do_thumbnail
          UpperCAmelCase							=       do_align_long_axis
          UpperCAmelCase							=       do_pad
          UpperCAmelCase							=       do_rescale
          UpperCAmelCase							=       rescale_factor
          UpperCAmelCase							=       do_normalize
          UpperCAmelCase							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase							=       image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 				_UpperCamelCase							( self	,A	,A	,A = None					):
          UpperCAmelCase							=       get_image_size(snake_case_					)
          UpperCAmelCase							=       size["""height"""], size["""width"""]

          if (output_width < output_height and input_width > input_height) or (
              output_width > output_height and input_width < input_height
          ):
                UpperCAmelCase							=       np.rotaa(snake_case_	,3					)

          if data_format is not None:
                UpperCAmelCase							=       to_channel_dimension_format(snake_case_	,snake_case_					)

          return image






    def 				_UpperCamelCase							( self	,A	,A	,A = False	,A = None	,):
          UpperCAmelCase							=       size["""height"""], size["""width"""]
          UpperCAmelCase							=       get_image_size(snake_case_					)

          UpperCAmelCase							=       output_width - input_width
          UpperCAmelCase							=       output_height - input_height

          if random_padding:
                UpperCAmelCase							=       np.random.randint(low=0	,high=delta_height + 1					)
                UpperCAmelCase							=       np.random.randint(low=0	,high=delta_width + 1					)
          else:
                UpperCAmelCase							=       delta_height // 2
                UpperCAmelCase							=       delta_width // 2

          UpperCAmelCase							=       delta_height - pad_top
          UpperCAmelCase							=       delta_width - pad_left

          UpperCAmelCase							=       ((pad_top, pad_bottom), (pad_left, pad_right))
          return pad(snake_case_	,snake_case_	,data_format=snake_case_					)






    def 				_UpperCamelCase							( self	,*A	,**A					):
          logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""					)
          return self.pad_image(*snake_case_	,**snake_case_					)






    def 				_UpperCamelCase							( self	,A	,A	,A = PILImageResampling.BICUBIC	,A = None	,**A	,):
          UpperCAmelCase							=       get_image_size(snake_case_					)
          UpperCAmelCase							=       size["""height"""], size["""width"""]

          # We always resize to the smallest of either the input or output size.
          UpperCAmelCase							=       min(snake_case_	,snake_case_					)
          UpperCAmelCase							=       min(snake_case_	,snake_case_					)

          if height == input_height and width == input_width:
                return image

          if input_height > input_width:
                UpperCAmelCase							=       int(input_width * height / input_height					)
          elif input_width > input_height:
                UpperCAmelCase							=       int(input_height * width / input_width					)

          return resize(
              snake_case_	,size=(height, width)	,resample=snake_case_	,reducing_gap=2.0	,data_format=snake_case_	,**snake_case_					)






    def 				_UpperCamelCase							( self	,A	,A	,A = PILImageResampling.BICUBIC	,A = None	,**A	,):
          UpperCAmelCase							=       get_size_dict(snake_case_					)
          UpperCAmelCase							=       min(size["""height"""]	,size["""width"""]					)
          UpperCAmelCase							=       get_resize_output_image_size(snake_case_	,size=snake_case_	,default_to_square=snake_case_					)
          UpperCAmelCase							=       resize(snake_case_	,size=snake_case_	,resample=snake_case_	,data_format=snake_case_	,**snake_case_					)
          return resized_image






    def 				_UpperCamelCase							( self	,A	,A	,A = None	,**A	,):
          return rescale(snake_case_	,scale=snake_case_	,data_format=snake_case_	,**snake_case_					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A = None	,**A	,):
          return normalize(snake_case_	,mean=snake_case_	,std=snake_case_	,data_format=snake_case_	,**snake_case_					)






    def 				_UpperCamelCase							( self	,A	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = False	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = ChannelDimension.FIRST	,**A	,):
          UpperCAmelCase							=       do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase							=       size if size is not None else self.size
          if isinstance(snake_case_	,(tuple, list)					):
                # Previous feature extractor had size in (width, height) format
                UpperCAmelCase							=       size[::-1]
          UpperCAmelCase							=       get_size_dict(snake_case_					)
          UpperCAmelCase							=       resample if resample is not None else self.resample
          UpperCAmelCase							=       do_thumbnail if do_thumbnail is not None else self.do_thumbnail
          UpperCAmelCase							=       do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
          UpperCAmelCase							=       do_pad if do_pad is not None else self.do_pad
          UpperCAmelCase							=       do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase							=       rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase							=       do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase							=       image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase							=       image_std if image_std is not None else self.image_std

          UpperCAmelCase							=       make_list_of_images(snake_case_					)

          if not valid_images(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:
                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_pad and size is None:
                raise ValueError("""Size must be specified if do_pad 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.
          UpperCAmelCase							=       [to_numpy_array(snake_case_					) for image in images]

          if do_align_long_axis:
                UpperCAmelCase							=       [self.align_long_axis(snake_case_	,size=snake_case_					) for image in images]

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

          if do_thumbnail:
                UpperCAmelCase							=       [self.thumbnail(image=snake_case_	,size=snake_case_					) for image in images]

          if do_pad:
                UpperCAmelCase							=       [self.pad_image(image=snake_case_	,size=snake_case_	,random_padding=snake_case_					) for image in images]

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

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

          UpperCAmelCase							=       [to_channel_dimension_format(snake_case_	,snake_case_					) for image in images]

          UpperCAmelCase							=       {"""pixel_values""": images}
          return BatchFeature(data=snake_case_	,tensor_type=snake_case_					)





import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


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

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				Dict  ,		*snake_case_       :				Optional[int]  ,		**snake_case_       :				List[Any]					):
							warnings.warn(
							    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use MobileNetV2ImageProcessor instead."""  ,		snake_case_  ,		)
							super().__init__(*snake_case_  ,		**snake_case_					)






