





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


UpperCamelCase_												=	logging.get_logger(__name__)


if is_vision_available():
    import PIL

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      ['pixel_values']

 def __init__(  self,		A_ = True,		A_ = None,		A_ = PILImageResampling.BICUBIC,		A_ = True,		A_ = None,		A_ = True,		A_ = 1 / 255,		A_ = True,		A_ = None,		A_ = None,		A_ = True,		**A_,		)	->       None:
    super().__init__(**A_		)
    UpperCAmelCase__						=size if size is not None else {"shortest_edge": 224}
    UpperCAmelCase__						=get_size_dict(A_,		default_to_square=A_		)
    UpperCAmelCase__						=crop_size if crop_size is not None else {"height": 224, "width": 224}
    UpperCAmelCase__						=get_size_dict(A_,		default_to_square=A_,		param_name="crop_size"		)

    UpperCAmelCase__						=do_resize
    UpperCAmelCase__						=size
    UpperCAmelCase__						=resample
    UpperCAmelCase__						=do_center_crop
    UpperCAmelCase__						=crop_size
    UpperCAmelCase__						=do_rescale
    UpperCAmelCase__						=rescale_factor
    UpperCAmelCase__						=do_normalize
    UpperCAmelCase__						=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
    UpperCAmelCase__						=image_std if image_std is not None else OPENAI_CLIP_STD
    UpperCAmelCase__						=do_convert_rgb

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_ = PILImageResampling.BICUBIC,		A_ = None,		**A_,		)	->       np.ndarray:
    UpperCAmelCase__						=get_size_dict(A_,		default_to_square=A_		)
    if "shortest_edge" not in size:
       raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
    UpperCAmelCase__						=get_resize_output_image_size(A_,		size=size["shortest_edge"],		default_to_square=A_		)
    return resize(A_,		size=A_,		resample=A_,		data_format=A_,		**A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_ = None,		**A_,		)	->       np.ndarray:
    UpperCAmelCase__						=get_size_dict(A_		)
    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(A_,		size=(size["height"], size["width"]),		data_format=A_,		**A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_ = None,		**A_,		)	->       int:
    return rescale(A_,		scale=A_,		data_format=A_,		**A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_ = None,		**A_,		)	->       np.ndarray:
    return normalize(A_,		mean=A_,		std=A_,		data_format=A_,		**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_ = None,		A_ = None,		A_ = None,		A_ = None,		A_ = None,		A_ = None,		A_ = None,		A_ = None,		A_ = None,		A_ = None,		A_ = None,		A_ = None,		A_ = ChannelDimension.FIRST,		**A_,		)	->       PIL.Image.Image:
    UpperCAmelCase__						=do_resize if do_resize is not None else self.do_resize
    UpperCAmelCase__						=size if size is not None else self.size
    UpperCAmelCase__						=get_size_dict(A_,		param_name="size",		default_to_square=A_		)
    UpperCAmelCase__						=resample if resample is not None else self.resample
    UpperCAmelCase__						=do_center_crop if do_center_crop is not None else self.do_center_crop
    UpperCAmelCase__						=crop_size if crop_size is not None else self.crop_size
    UpperCAmelCase__						=get_size_dict(A_,		param_name="crop_size",		default_to_square=A_		)
    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__						=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    UpperCAmelCase__						=make_list_of_images(A_		)

    if not valid_images(A_		):
       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:
       UpperCAmelCase__						=[convert_to_rgb(A_		) for image in images]

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

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

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

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

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

    UpperCAmelCase__						=[to_channel_dimension_format(A_,		A_		) for image in images]

    UpperCAmelCase__						={"pixel_values": images}
    return BatchFeature(data=A_,		tensor_type=A_		)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'donut-swin'

 __UpperCamelCase       =      {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }

 def __init__(  self,		A_=224,		A_=4,		A_=3,		A_=96,		A_=[2, 2, 6, 2],		A_=[3, 6, 12, 24],		A_=7,		A_=4.0,		A_=True,		A_=0.0,		A_=0.0,		A_=0.1,		A_="gelu",		A_=False,		A_=0.02,		A_=1E-5,		**A_,		)	->       List[str]:
    super().__init__(**A_		)

    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=patch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=embed_dim
    UpperCAmelCase__						=depths
    UpperCAmelCase__						=len(A_		)
    UpperCAmelCase__						=num_heads
    UpperCAmelCase__						=window_size
    UpperCAmelCase__						=mlp_ratio
    UpperCAmelCase__						=qkv_bias
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=drop_path_rate
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=use_absolute_embeddings
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    UpperCAmelCase__						=int(embed_dim * 2 ** (len(A_		) - 1)		)



