





'''simple docstring'''
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


a__							:							List[str] =logging.get_logger(__name__)
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  List[Any]     ) ->      Dict:



       """simple docstring"""






       __UpperCamelCase       =  b.T
       __UpperCamelCase       =  np.sum(np.square(__lowercase     )				,  axis=1     )
       __UpperCamelCase       =  np.sum(np.square(__lowercase     )				,  axis=0     )
       __UpperCamelCase       =  np.matmul(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  aa[:, None] - 2 * ab + ba[None, :]
       return d
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Dict     ) ->      List[Any]:



       """simple docstring"""






       __UpperCamelCase       =  x.reshape(-1				,  3     )
       __UpperCamelCase       =  squared_euclidean_distance(__lowercase				,  __lowercase     )
       return np.argmin(__lowercase				,  axis=1     )


class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[Any]					=["pixel_values"]






 def __init__(       self       :							Union[str, Any]							,	__A       :							Optional[Union[List[List[int]], np.ndarray]] = None							,	__A       :							bool = True							,	__A       :							Dict[str, int] = None							,	__A       :							PILImageResampling = PILImageResampling.BILINEAR							,	__A       :							bool = True							,	__A       :							bool = True							,	**__A       :							List[str]							,	):
        super().__init__(**__A	)
        __UpperCamelCase       =  size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
        __UpperCamelCase       =  get_size_dict(__A	)
        __UpperCamelCase       =  np.array(__A	) if clusters is not None else None
        __UpperCamelCase       =  do_resize
        __UpperCamelCase       =  size
        __UpperCamelCase       =  resample
        __UpperCamelCase       =  do_normalize
        __UpperCamelCase       =  do_color_quantize






 def        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							np.ndarray							,	__A       :							Dict[str, int]							,	__A       :							PILImageResampling = PILImageResampling.BILINEAR							,	__A       :							Optional[Union[str, ChannelDimension]] = None							,	**__A       :							Union[str, Any]							,	):
        __UpperCamelCase       =  get_size_dict(__A	)
        if "height" not in size or "width" not in size:
               raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''	)
        return resize(
            __A							,	size=(size['height'], size['width'])							,	resample=__A							,	data_format=__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							np.ndarray							,	__A       :							Optional[Union[str, ChannelDimension]] = None							,	):
        __UpperCamelCase       =  rescale(image=__A							,	scale=1 / 127.5							,	data_format=__A	)
        __UpperCamelCase       =  image - 1
        return image






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							ImageInput							,	__A       :							bool = None							,	__A       :							Dict[str, int] = None							,	__A       :							PILImageResampling = None							,	__A       :							bool = None							,	__A       :							Optional[bool] = None							,	__A       :							Optional[Union[List[List[int]], np.ndarray]] = None							,	__A       :							Optional[Union[str, TensorType]] = None							,	__A       :							Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST							,	**__A       :							Optional[int]							,	):
        __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	)
        __UpperCamelCase       =  resample if resample is not None else self.resample
        __UpperCamelCase       =  do_normalize if do_normalize is not None else self.do_normalize
        __UpperCamelCase       =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        __UpperCamelCase       =  clusters if clusters is not None else self.clusters
        __UpperCamelCase       =  np.array(__A	)

        __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 or resample is None:
               raise ValueError('Size and resample must be specified if do_resize is True.'	)

        if do_color_quantize and clusters is None:
               raise ValueError('Clusters must be specified if do_color_quantize is True.'	)

        # 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_normalize:
               __UpperCamelCase       =  [self.normalize(image=__A	) for image in images]

        if do_color_quantize:
               __UpperCamelCase       =  [to_channel_dimension_format(__A							,	ChannelDimension.LAST	) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               __UpperCamelCase       =  np.array(__A	)
               __UpperCamelCase       =  color_quantize(__A							,	__A	).reshape(images.shape[:-1]	)

               # flatten to (batch_size, height*width)
               __UpperCamelCase       =  images.shape[0]
               __UpperCamelCase       =  images.reshape(__A							,	-1	)

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               __UpperCamelCase       =  list(__A	)
        else:
               __UpperCamelCase       =  [to_channel_dimension_format(__A							,	__A	) for image in images]

        __UpperCamelCase       =  {'input_ids': images}
        return BatchFeature(data=__A							,	tensor_type=__A	)




'''simple docstring'''



import torch

from transformers import AutoModel





class 							__UpperCamelCase				(		torch.nn.Module					):




	def __init__(   self						,				__a="sayef/fsner-bert-base-uncased"							):

						'''simple docstring'''





						super(__a						,				self							).__init__()

						__a			:       Tuple					    =  AutoModel.from_pretrained(__a						,				return_dict=__a							)
						__a			:       int					    =  torch.nn.CosineSimilarity(3						,				1E-0_8							)
						__a			:       Union[str, Any]					    =  torch.nn.Softmax(dim=1							)




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						return self.bert(**__a							).last_hidden_state




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return token_embeddings.sum(2						,				keepdim=__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=1							):

						'''simple docstring'''





						return self.softmax(T * self.cos(__a						,				__a							)							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  W_supports['sizes'].tolist()
						__a			:       Union[str, Any]					    =  W_supports['start_token_id'].item()
						__a			:       Any					    =  W_supports['end_token_id'].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						__a			:       Tuple					    =  self.BERT(**__a							)
						__a			:       str					    =  self.BERT(**__a							)

						__a			:       Any					    =  None
						__a			:       Dict					    =  None

						__a			:       Dict					    =  W_supports['input_ids'] == start_token_id
						__a			:       Union[str, Any]					    =  W_supports['input_ids'] == end_token_id

						for i, size in enumerate(__a							):
											if i == 0:
																__a			:       Optional[int]					    =  0
											else:
																__a			:       Union[str, Any]					    =  support_sizes[i - 1]

											__a			:       int					    =  S[s : s + size][start_token_masks[s : s + size]]
											__a			:       Union[str, Any]					    =  S[s : s + size][end_token_masks[s : s + size]]

											__a			:       Tuple					    =  torch.matmul(q[i]						,				s_start.T							).sum(1							).softmax(0							)
											__a			:       Dict					    =  torch.matmul(q[i]						,				s_end.T							).sum(1							).softmax(0							)

											if p_starts is not None:
																__a			:       str					    =  torch.vstack((p_starts, p_start)							)
																__a			:       str					    =  torch.vstack((p_ends, p_end)							)
											else:
																__a			:       List[str]					    =  p_start
																__a			:       int					    =  p_end

						return p_starts, p_ends



