from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
	import torch


_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						( lowerCamelCase_  ):


				'''simple docstring'''






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



						"""simple docstring"""





						super().__init__(**__snake_case )
						A_		:    Tuple         =				size if size is not None else {'shortest_edge': 2_5_6}
						A_		:    Dict         =				get_size_dict(__snake_case		,     default_to_square=__snake_case )
						A_		:    Optional[Any]         =				crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
						A_		:    Union[str, Any]         =				get_size_dict(__snake_case		,     param_name='crop_size' )
						A_		:    Union[str, Any]         =				do_resize
						A_		:    Tuple         =				size
						A_		:    Tuple         =				resample
						A_		:    Union[str, Any]         =				do_center_crop
						A_		:    Dict         =				crop_size
						A_		:    Optional[int]         =				do_rescale
						A_		:    Union[str, Any]         =				rescale_factor
						A_		:    Tuple         =				do_normalize
						A_		:    Union[str, Any]         =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A_		:    str         =				image_std if image_std is not None else IMAGENET_STANDARD_STD
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = PILImageResampling.BICUBIC		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Any         =				get_size_dict(__snake_case		,     default_to_square=__snake_case )
						if "shortest_edge" not in size:
								raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
						A_		:    Optional[Any]         =				get_resize_output_image_size(__snake_case		,     size=size['shortest_edge']		,     default_to_square=__snake_case )
						return resize(__snake_case		,     size=__snake_case		,     resample=__snake_case		,     data_format=__snake_case		,     **__snake_case )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Dict         =				get_size_dict(__snake_case )
						if "height" not in size or "width" not in size:
								raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}''' )
						return center_crop(__snake_case		,     size=(size['height'], size['width'])		,     data_format=__snake_case		,     **__snake_case )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase ):



						"""simple docstring"""





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



						"""simple docstring"""





						return normalize(__snake_case		,     mean=__snake_case		,     std=__snake_case		,     data_format=__snake_case		,     **__snake_case )
				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 = ChannelDimension.FIRST		,     **lowercase		,     ):



						"""simple docstring"""





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

						A_		:    Tuple         =				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_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.' )

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

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

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

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

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

						A_		:    int         =				[to_channel_dimension_format(__snake_case		,     __snake_case ) for image in images]

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







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    str         =				outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
								if len(__snake_case ) != len(__snake_case ):
										raise ValueError(
										    'Make sure that you pass in as many target sizes as the batch dimension of the logits' )

								if is_torch_tensor(__snake_case ):
										A_		:    Tuple         =				target_sizes.numpy()

								A_		:    Dict         =				[]

								for idx in range(len(__snake_case ) ):
										A_		:    int         =				torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0 )		,     size=target_sizes[idx]		,     mode='bilinear'		,     align_corners=__snake_case )
										A_		:    Union[str, Any]         =				resized_logits[0].argmax(dim=0 )
										semantic_segmentation.append(__snake_case )
						else:
								A_		:    List[Any]         =				logits.argmax(dim=1 )
								A_		:    Dict         =				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

						return semantic_segmentation

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






