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 UpperCAmelCase						( _a  ):


				'''simple docstring'''






				lowerCamelCase_    = ["""pixel_values"""]
				def __init__(						self		,     lowercase = True		,     lowercase = None		,     lowercase = PILImageResampling.BICUBIC		,     lowercase = True		,     lowercase = None		,     lowercase = True		,     lowercase = 1 / 2_5_5		,     lowercase = True		,     lowercase = None		,     lowercase = None		,     lowercase = True		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(**__lowerCAmelCase )
						A_		:    Any         =				size if size is not None else {'shortest_edge': 2_2_4}
						A_		:    Any         =				get_size_dict(__lowerCAmelCase		,     default_to_square=__lowerCAmelCase )
						A_		:    Tuple         =				crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
						A_		:    Optional[int]         =				get_size_dict(__lowerCAmelCase		,     default_to_square=__lowerCAmelCase		,     param_name='crop_size' )

						A_		:    str         =				do_resize
						A_		:    Any         =				size
						A_		:    Any         =				resample
						A_		:    Any         =				do_center_crop
						A_		:    Optional[Any]         =				crop_size
						A_		:    Optional[int]         =				do_rescale
						A_		:    Any         =				rescale_factor
						A_		:    List[str]         =				do_normalize
						A_		:    Optional[Any]         =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						A_		:    Optional[int]         =				image_std if image_std is not None else OPENAI_CLIP_STD
						A_		:    Optional[int]         =				do_convert_rgb
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = PILImageResampling.BICUBIC		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Tuple         =				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()}''' )
						A_		:    int         =				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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						return rescale(__lowerCAmelCase		,     scale=__lowerCAmelCase		,     data_format=__lowerCAmelCase		,     **__lowerCAmelCase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						return normalize(__lowerCAmelCase		,     mean=__lowerCAmelCase		,     std=__lowerCAmelCase		,     data_format=__lowerCAmelCase		,     **__lowerCAmelCase )







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = ChannelDimension.FIRST		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				do_resize if do_resize is not None else self.do_resize
						A_		:    Union[str, Any]         =				size if size is not None else self.size
						A_		:    Tuple         =				get_size_dict(__lowerCAmelCase		,     param_name='size'		,     default_to_square=__lowerCAmelCase )
						A_		:    List[str]         =				resample if resample is not None else self.resample
						A_		:    str         =				do_center_crop if do_center_crop is not None else self.do_center_crop
						A_		:    str         =				crop_size if crop_size is not None else self.crop_size
						A_		:    Any         =				get_size_dict(__lowerCAmelCase		,     param_name='crop_size'		,     default_to_square=__lowerCAmelCase )
						A_		:    int         =				do_rescale if do_rescale is not None else self.do_rescale
						A_		:    Optional[int]         =				rescale_factor if rescale_factor is not None else self.rescale_factor
						A_		:    int         =				do_normalize if do_normalize is not None else self.do_normalize
						A_		:    int         =				image_mean if image_mean is not None else self.image_mean
						A_		:    Union[str, Any]         =				image_std if image_std is not None else self.image_std
						A_		:    str         =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						A_		:    Union[str, Any]         =				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:
								A_		:    int         =				[convert_to_rgb(__lowerCAmelCase ) for image in images]

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

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

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

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

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

						A_		:    Tuple         =				[to_channel_dimension_format(__lowerCAmelCase		,     __lowerCAmelCase ) for image in images]

						A_		:    Union[str, Any]         =				{'pixel_values': images}
						return BatchFeature(data=__lowerCAmelCase		,     tensor_type=__lowerCAmelCase )

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


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''lilt'''
				def __init__(						self		,     lowercase=3_0_5_2_2		,     lowercase=7_6_8		,     lowercase=1_2		,     lowercase=1_2		,     lowercase=3_0_7_2		,     lowercase="gelu"		,     lowercase=0.1		,     lowercase=0.1		,     lowercase=5_1_2		,     lowercase=2		,     lowercase=0.02		,     lowercase=1E-12		,     lowercase=0		,     lowercase="absolute"		,     lowercase=None		,     lowercase=4		,     lowercase=1_0_2_4		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(pad_token_id=lowercase		,     **lowercase )

						A_		:    str         =				vocab_size
						A_		:    Tuple         =				hidden_size
						A_		:    List[Any]         =				num_hidden_layers
						A_		:    Any         =				num_attention_heads
						A_		:    Union[str, Any]         =				hidden_act
						A_		:    int         =				intermediate_size
						A_		:    int         =				hidden_dropout_prob
						A_		:    Union[str, Any]         =				attention_probs_dropout_prob
						A_		:    List[str]         =				max_position_embeddings
						A_		:    List[str]         =				type_vocab_size
						A_		:    Any         =				initializer_range
						A_		:    str         =				layer_norm_eps
						A_		:    Optional[int]         =				position_embedding_type
						A_		:    Union[str, Any]         =				classifier_dropout
						A_		:    Optional[Any]         =				channel_shrink_ratio
						A_		:    int         =				max_ad_position_embeddings






