


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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_SCREAMING_SNAKE_CASE      :			Tuple      				=   logging.get_logger(__name__)


if is_vision_available():
 import PIL

class   A__ (   A_     ):







    """simple docstring"""

    __magic_name__        =		['pixel_values']
    def __init__(	self      , __snake_case = True      , __snake_case = None      , __snake_case = PILImageResampling.BICUBIC      , __snake_case = True      , __snake_case = None      , __snake_case = True      , __snake_case = 1 / 2_5_5      , __snake_case = True      , __snake_case = None      , __snake_case = None      , __snake_case = True      , **__snake_case      , ):
         super().__init__(**_lowerCamelCase   )
         snake_case					=    size if size is not None else {'''shortest_edge''': 2_2_4}
         snake_case					=    get_size_dict(_lowerCamelCase      , default_to_square=_lowerCamelCase   )
         snake_case					=    crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         snake_case					=    get_size_dict(_lowerCamelCase      , default_to_square=_lowerCamelCase      , param_name='''crop_size'''   )

         snake_case					=    do_resize
         snake_case					=    size
         snake_case					=    resample
         snake_case					=    do_center_crop
         snake_case					=    crop_size
         snake_case					=    do_rescale
         snake_case					=    rescale_factor
         snake_case					=    do_normalize
         snake_case					=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         snake_case					=    image_std if image_std is not None else OPENAI_CLIP_STD
         snake_case					=    do_convert_rgb
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case = PILImageResampling.BICUBIC      , __snake_case = None      , **__snake_case      , ):
         snake_case					=    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()}'''   )
         snake_case					=    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 		a_      (	self      , __snake_case      , __snake_case      , __snake_case = None      , **__snake_case      , ):
         snake_case					=    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, width). Got {size.keys()}'''   )
         return center_crop(_lowerCamelCase      , size=(size['''height'''], size['''width'''])      , data_format=_lowerCamelCase      , **_lowerCamelCase   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case = None      , **__snake_case      , ):
         return rescale(_lowerCamelCase      , scale=_lowerCamelCase      , data_format=_lowerCamelCase      , **_lowerCamelCase   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case = None      , **__snake_case      , ):
         return normalize(_lowerCamelCase      , mean=_lowerCamelCase      , std=_lowerCamelCase      , data_format=_lowerCamelCase      , **_lowerCamelCase   )




    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 = 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					=    size if size is not None else self.size
         snake_case					=    get_size_dict(_lowerCamelCase      , param_name='''size'''      , default_to_square=_lowerCamelCase   )
         snake_case					=    resample if resample is not None else self.resample
         snake_case					=    do_center_crop if do_center_crop is not None else self.do_center_crop
         snake_case					=    crop_size if crop_size is not None else self.crop_size
         snake_case					=    get_size_dict(_lowerCamelCase      , param_name='''crop_size'''      , default_to_square=_lowerCamelCase   )
         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					=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         snake_case					=    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.'''   )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              snake_case					=    [convert_to_rgb(_lowerCamelCase   ) for image in images]

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

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

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

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

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

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

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








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   )




