'''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 (
    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_vision_available, logging


if is_vision_available():
  import PIL


__SCREAMING_SNAKE_CASE       :     List[str]     =  logging.get_logger(__name__)

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						["pixel_values"]




      def __init__(       self						:				Optional[int]						,					A						:				bool = True						,					A						:				Dict[str, int] = None						,					A						:				float = None						,					A						:				PILImageResampling = PILImageResampling.BILINEAR						,					A						:				bool = True						,					A						:				Union[int, float] = 1 / 255						,					A						:				bool = True						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[float, List[float]]] = None						,					**A						:				int						,					):
             super().__init__(**A       )
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 384}
             _UpperCAmelCase     :  Union[str, Any]							      =    get_size_dict(A						,					default_to_square=A       )

             _UpperCAmelCase     :  int							      =    do_resize
             _UpperCAmelCase     :  Optional[int]							      =    size
             # Default value set here for backwards compatibility where the value in config is None
             _UpperCAmelCase     :  str							      =    crop_pct if crop_pct is not None else 224 / 256
             _UpperCAmelCase     :  Tuple							      =    resample
             _UpperCAmelCase     :  Union[str, Any]							      =    do_rescale
             _UpperCAmelCase     :  List[str]							      =    rescale_factor
             _UpperCAmelCase     :  Tuple							      =    do_normalize
             _UpperCAmelCase     :  Optional[Any]							      =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             _UpperCAmelCase     :  Optional[Any]							      =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def      _A	(       self						:				List[Any]						,					A						:				np.ndarray						,					A						:				Dict[str, int]						,					A						:				float						,					A						:				PILImageResampling = PILImageResampling.BICUBIC						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Any						,					):
             _UpperCAmelCase     :  Optional[int]							      =    get_size_dict(A						,					default_to_square=A       )
             if "shortest_edge" not in size:
                    raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""       )
             _UpperCAmelCase     :  Optional[Any]							      =    size["shortest_edge"]

             if shortest_edge < 384:
                    # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                    _UpperCAmelCase     :  Dict							      =    int(shortest_edge / crop_pct       )
                    _UpperCAmelCase     :  Optional[Any]							      =    get_resize_output_image_size(A						,					size=A						,					default_to_square=A       )
                    _UpperCAmelCase     :  int							      =    resize(image=A						,					size=A						,					resample=A						,					data_format=A						,					**A       )
                    # then crop to (shortest_edge, shortest_edge)
                    return center_crop(image=A						,					size=(shortest_edge, shortest_edge)						,					data_format=A						,					**A       )
             else:
                    # warping (no cropping) when evaluated at 384 or larger
                    return resize(
                        A						,					size=(shortest_edge, shortest_edge)						,					resample=A						,					data_format=A						,					**A       )




      def      _A	(       self						:				Dict						,					A						:				np.ndarray						,					A						:				Union[int, float]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				int						,					):
             return rescale(A						,					scale=A						,					data_format=A						,					**A       )




      def      _A	(       self						:				List[Any]						,					A						:				np.ndarray						,					A						:				Union[float, List[float]]						,					A						:				Union[float, List[float]]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Optional[int]						,					):
             return normalize(A						,					mean=A						,					std=A						,					data_format=A						,					**A       )





      def      _A	(       self						:				str						,					A						:				ImageInput						,					A						:				bool = None						,					A						:				Dict[str, int] = None						,					A						:				float = None						,					A						:				PILImageResampling = None						,					A						:				bool = None						,					A						:				float = None						,					A						:				bool = None						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[str, TensorType]] = None						,					A						:				ChannelDimension = ChannelDimension.FIRST						,					**A						:				List[Any]						,					):
             _UpperCAmelCase     :  Optional[int]							      =    do_resize if do_resize is not None else self.do_resize
             _UpperCAmelCase     :  str							      =    crop_pct if crop_pct is not None else self.crop_pct
             _UpperCAmelCase     :  Dict							      =    resample if resample is not None else self.resample
             _UpperCAmelCase     :  List[Any]							      =    do_rescale if do_rescale is not None else self.do_rescale
             _UpperCAmelCase     :  Optional[Any]							      =    rescale_factor if rescale_factor is not None else self.rescale_factor
             _UpperCAmelCase     :  str							      =    do_normalize if do_normalize is not None else self.do_normalize
             _UpperCAmelCase     :  Optional[int]							      =    image_mean if image_mean is not None else self.image_mean
             _UpperCAmelCase     :  Dict							      =    image_std if image_std is not None else self.image_std

             _UpperCAmelCase     :  Tuple							      =    size if size is not None else self.size
             _UpperCAmelCase     :  List[str]							      =    get_size_dict(A						,					default_to_square=A       )

             _UpperCAmelCase     :  Any							      =    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_resize and size["shortest_edge"] < 384 and crop_pct is None:
                    raise ValueError("crop_pct must be specified if size < 384."       )

             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.
             _UpperCAmelCase     :  List[Any]							      =    [to_numpy_array(A       ) for image in images]

             if do_resize:
                    _UpperCAmelCase     :  List[Any]							      =    [self.resize(image=A						,					size=A						,					crop_pct=A						,					resample=A       ) for image in images]

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

             if do_normalize:
                    _UpperCAmelCase     :  Any							      =    [self.normalize(image=A						,					mean=A						,					std=A       ) for image in images]

             _UpperCAmelCase     :  Any							      =    [to_channel_dimension_format(A						,					A       ) for image in images]

             _UpperCAmelCase     :  Optional[Any]							      =    {"pixel_values": 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



