


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 (
    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_vision_available, logging


if is_vision_available():
 import PIL


_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   logging.get_logger(__name__)

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		['pixel_values']
    def __init__(	self      , __snake_case = True      , __snake_case = None      , __snake_case = None      , __snake_case = PILImageResampling.BILINEAR      , __snake_case = True      , __snake_case = 1 / 2_5_5      , __snake_case = True      , __snake_case = None      , __snake_case = None      , **__snake_case      , ):
         super().__init__(**__snake_case   )
         snake_case					=    size if size is not None else {'''shortest_edge''': 3_8_4}
         snake_case					=    get_size_dict(__snake_case      , default_to_square=__snake_case   )

         snake_case					=    do_resize
         snake_case					=    size
         # Default value set here for backwards compatibility where the value in config is None
         snake_case					=    crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
         snake_case					=    resample
         snake_case					=    do_rescale
         snake_case					=    rescale_factor
         snake_case					=    do_normalize
         snake_case					=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         snake_case					=    image_std if image_std is not None else IMAGENET_STANDARD_STD
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case = PILImageResampling.BICUBIC      , __snake_case = None      , **__snake_case      , ):
         snake_case					=    get_size_dict(__snake_case      , default_to_square=__snake_case   )
         if "shortest_edge" not in size:
              raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''   )
         snake_case					=    size['''shortest_edge''']

         if shortest_edge < 3_8_4:
              # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
              snake_case					=    int(shortest_edge / crop_pct   )
              snake_case					=    get_resize_output_image_size(__snake_case      , size=__snake_case      , default_to_square=__snake_case   )
              snake_case					=    resize(image=__snake_case      , size=__snake_case      , resample=__snake_case      , data_format=__snake_case      , **__snake_case   )
              # then crop to (shortest_edge, shortest_edge)
              return center_crop(image=__snake_case      , size=(shortest_edge, shortest_edge)      , data_format=__snake_case      , **__snake_case   )
         else:
              # warping (no cropping) when evaluated at 384 or larger
              return resize(
                  __snake_case      , size=(shortest_edge, shortest_edge)      , resample=__snake_case      , data_format=__snake_case      , **__snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case = None      , **__snake_case      , ):
         return rescale(__snake_case      , scale=__snake_case      , data_format=__snake_case      , **__snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case = None      , **__snake_case      , ):
         return normalize(__snake_case      , mean=__snake_case      , std=__snake_case      , data_format=__snake_case      , **__snake_case   )




    def 		a_      (	self      , __snake_case      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = ChannelDimension.FIRST      , **__snake_case      , ):
         snake_case					=    do_resize if do_resize is not None else self.do_resize
         snake_case					=    crop_pct if crop_pct is not None else self.crop_pct
         snake_case					=    resample if resample is not None else self.resample
         snake_case					=    do_rescale if do_rescale is not None else self.do_rescale
         snake_case					=    rescale_factor if rescale_factor is not None else self.rescale_factor
         snake_case					=    do_normalize if do_normalize is not None else self.do_normalize
         snake_case					=    image_mean if image_mean is not None else self.image_mean
         snake_case					=    image_std if image_std is not None else self.image_std

         snake_case					=    size if size is not None else self.size
         snake_case					=    get_size_dict(__snake_case      , default_to_square=__snake_case   )

         snake_case					=    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 or resample is None:
              raise ValueError('''Size and resample must be specified if do_resize is True.'''   )

         if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
              raise ValueError('''crop_pct must be specified if size < 384.'''   )

         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.
         snake_case					=    [to_numpy_array(__snake_case   ) for image in images]

         if do_resize:
              snake_case					=    [self.resize(image=__snake_case      , size=__snake_case      , crop_pct=__snake_case      , resample=__snake_case   ) for image in images]

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

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

         snake_case					=    [to_channel_dimension_format(__snake_case      , __snake_case   ) for image in images]

         snake_case					=    {'''pixel_values''': images}
         return BatchFeature(data=__snake_case      , tensor_type=__snake_case   )








import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


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

class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , *__snake_case      , **__snake_case   ):
         warnings.warn(
             '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use BeitImageProcessor instead.'''      , __snake_case      , )
         super().__init__(*__snake_case      , **__snake_case   )




