




'''simple docstring'''

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


a       :						str								=		logging.get_logger(__name__)

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



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			"""lilt"""





			def __init__(      self						:    List[Any]      ,		a_						:    List[Any]=30_522      ,		a_						:    Optional[int]=768      ,		a_						:    Optional[Any]=12      ,		a_						:    Union[str, Any]=12      ,		a_						:    Optional[int]=3_072      ,		a_						:    Dict="gelu"      ,		a_						:    Union[str, Any]=0.1      ,		a_						:    str=0.1      ,		a_						:    Optional[int]=512      ,		a_						:    Tuple=2      ,		a_						:    Dict=0.02      ,		a_						:    Tuple=1e-12      ,		a_						:    str=0      ,		a_						:    Union[str, Any]="absolute"      ,		a_						:    Dict=None      ,		a_						:    List[str]=4      ,		a_						:    Optional[Any]=1_024      ,		**a_						:    str      ,		):



						"""simple docstring"""




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

						__snake_case			=		vocab_size
						__snake_case			=		hidden_size
						__snake_case			=		num_hidden_layers
						__snake_case			=		num_attention_heads
						__snake_case			=		hidden_act
						__snake_case			=		intermediate_size
						__snake_case			=		hidden_dropout_prob
						__snake_case			=		attention_probs_dropout_prob
						__snake_case			=		max_position_embeddings
						__snake_case			=		type_vocab_size
						__snake_case			=		initializer_range
						__snake_case			=		layer_norm_eps
						__snake_case			=		position_embedding_type
						__snake_case			=		classifier_dropout
						__snake_case			=		channel_shrink_ratio
						__snake_case			=		max_ad_position_embeddings









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


_lowerCAmelCase:						List[Any]   				=       logging.get_logger(__name__)




class       lowercase_					(lowercase__   ):
				snake_case	    =['pixel_values']





				def __init__(				self       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = PILImageResampling.BILINEAR       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = True       ,  lowercase_ = 1 / 255       ,  lowercase_ = True       ,  lowercase_ = None       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> None:
											super().__init__(**lowercase_)
											a__      =size if size is not None else {'shortest_edge': 256}
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											a__      =crop_size if crop_size is not None else {'height': 224, 'width': 224}
											a__      =get_size_dict(lowercase_       ,  param_name='crop_size')
											a__      =do_resize
											a__      =size
											a__      =resample
											a__      =do_center_crop
											a__      =crop_size
											a__      =do_rescale
											a__      =rescale_factor
											a__      =do_normalize
											a__      =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											a__      =image_std if image_std is not None else IMAGENET_STANDARD_STD





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





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> np.ndarray:
											a__      =get_size_dict(lowercase_)
											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(lowercase_       ,  size=(size['height'], size['width'])       ,  data_format=lowercase_       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = None       ,  **lowercase_)  -> np.ndarray:
											return rescale(lowercase_       ,  scale=lowercase_       ,  data_format=lowercase_       ,  **lowercase_)





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





				def 						__UpperCamelCase    (				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_       ,  )  -> Tuple:
											a__      =do_resize if do_resize is not None else self.do_resize
											a__      =size if size is not None else self.size
											a__      =get_size_dict(lowercase_       ,  default_to_square=lowercase_)
											a__      =resample if resample is not None else self.resample
											a__      =do_center_crop if do_center_crop is not None else self.do_center_crop
											a__      =crop_size if crop_size is not None else self.crop_size
											a__      =get_size_dict(lowercase_       ,  param_name='crop_size')
											a__      =do_rescale if do_rescale is not None else self.do_rescale
											a__      =rescale_factor if rescale_factor is not None else self.rescale_factor
											a__      =do_normalize if do_normalize is not None else self.do_normalize
											a__      =image_mean if image_mean is not None else self.image_mean
											a__      =image_std if image_std is not None else self.image_std

											a__      =make_list_of_images(lowercase_)

											if not valid_images(lowercase_):
																		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__      =[to_numpy_array(lowercase_) for image in images]

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

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

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

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

											a__      =[to_channel_dimension_format(lowercase_       ,  lowercase_) for image in images]

											a__      ={'pixel_values': images}
											return BatchFeature(data=lowercase_       ,  tensor_type=lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None)  -> str:
											a__      =outputs.logits

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

																		if is_torch_tensor(lowercase_):
																									a__      =target_sizes.numpy()

																		a__      =[]

																		for idx in range(len(lowercase_)):
																									a__      =torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0)       ,  size=target_sizes[idx]       ,  mode='bilinear'       ,  align_corners=lowercase_)
																									a__      =resized_logits[0].argmax(dim=0)
																									semantic_segmentation.append(lowercase_)
											else:
																		a__      =logits.argmax(dim=1)
																		a__      =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

											return semantic_segmentation

