'''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,
    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__     :	int				=			logging.get_logger(__name__)


if is_vision_available():
  import PIL


class 				_lowercase	(      UpperCamelCase_   ):
      '''simple docstring'''


      UpperCAmelCase_      :      str      							=   ['''pixel_values''']




      def __init__(				self					,lowerCamelCase_ = True					,lowerCamelCase_ = None					,lowerCamelCase_ = PILImageResampling.BICUBIC					,lowerCamelCase_ = True					,lowerCamelCase_ = None					,lowerCamelCase_ = True					,lowerCamelCase_ = 1 / 255					,lowerCamelCase_ = True					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = True					,**lowerCamelCase_					,)			->			None:
         super().__init__(**lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  size if size is not None else {'''shortest_edge''': 224}
         UpperCAmelCase__							:				List[Any]   				=  get_size_dict(lowerCamelCase_					,default_to_square=lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         UpperCAmelCase__							:				str   				=  get_size_dict(lowerCamelCase_					,default_to_square=lowerCamelCase_					,param_name='''crop_size'''							)

         UpperCAmelCase__							:				Tuple   				=  do_resize
         UpperCAmelCase__							:				Any   				=  size
         UpperCAmelCase__							:				Any   				=  resample
         UpperCAmelCase__							:				Optional[Any]   				=  do_center_crop
         UpperCAmelCase__							:				Optional[int]   				=  crop_size
         UpperCAmelCase__							:				List[Any]   				=  do_rescale
         UpperCAmelCase__							:				str   				=  rescale_factor
         UpperCAmelCase__							:				Union[str, Any]   				=  do_normalize
         UpperCAmelCase__							:				Optional[int]   				=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         UpperCAmelCase__							:				List[str]   				=  image_std if image_std is not None else OPENAI_CLIP_STD
         UpperCAmelCase__							:				List[Any]   				=  do_convert_rgb




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_ = PILImageResampling.BICUBIC					,lowerCamelCase_ = None					,**lowerCamelCase_					,)			->			np.ndarray:
         UpperCAmelCase__							:				List[str]   				=  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()}'''							)
         UpperCAmelCase__							:				str   				=  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					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_ = None					,**lowerCamelCase_					,)			->			np.ndarray:
         UpperCAmelCase__							:				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					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_ = None					,**lowerCamelCase_					,)			->			str:
         return rescale(lowerCamelCase_					,scale=lowerCamelCase_					,data_format=lowerCamelCase_					,**lowerCamelCase_							)




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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = ChannelDimension.FIRST					,**lowerCamelCase_					,)			->			PIL.Image.Image:
         UpperCAmelCase__							:				List[Any]   				=  do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase__							:				int   				=  size if size is not None else self.size
         UpperCAmelCase__							:				Any   				=  get_size_dict(lowerCamelCase_					,param_name='''size'''					,default_to_square=lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  resample if resample is not None else self.resample
         UpperCAmelCase__							:				int   				=  do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase__							:				int   				=  crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase__							:				int   				=  get_size_dict(lowerCamelCase_					,param_name='''crop_size'''					,default_to_square=lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase__							:				Union[str, Any]   				=  rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase__							:				Optional[Any]   				=  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__							:				Tuple   				=  image_std if image_std is not None else self.image_std
         UpperCAmelCase__							:				List[str]   				=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         UpperCAmelCase__							:				List[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:
            UpperCAmelCase__							:				Optional[int]   				=  [convert_to_rgb(lowerCamelCase_							) for image in images]

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

         if do_resize:
            UpperCAmelCase__							:				str   				=  [self.resize(image=lowerCamelCase_					,size=lowerCamelCase_					,resample=lowerCamelCase_							) for image in images]

         if do_center_crop:
            UpperCAmelCase__							:				int   				=  [self.center_crop(image=lowerCamelCase_					,size=lowerCamelCase_							) for image in images]

         if do_rescale:
            UpperCAmelCase__							:				Union[str, Any]   				=  [self.rescale(image=lowerCamelCase_					,scale=lowerCamelCase_							) for image in images]

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

         UpperCAmelCase__							:				int   				=  [to_channel_dimension_format(lowerCamelCase_					,lowerCamelCase_							) for image in images]

         UpperCAmelCase__							:				str   				=  {'''pixel_values''': images}
         return BatchFeature(data=lowerCamelCase_					,tensor_type=lowerCamelCase_							)





'''simple docstring'''

import torch

from transformers import AutoModel


class 				_lowercase	(      torch.nn.Module   ):
      '''simple docstring'''


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



         '''simple docstring'''



         super(lowerCamelCase_					,self							).__init__()

         UpperCAmelCase__							:				Union[str, Any]   				=  AutoModel.from_pretrained(lowerCamelCase_					,return_dict=lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  torch.nn.CosineSimilarity(3					,1e-08							)
         UpperCAmelCase__							:				int   				=  torch.nn.Softmax(dim=1							)




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         return self.bert(**lowerCamelCase_							).last_hidden_state




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return token_embeddings.sum(2					,keepdim=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=1							)			->			int:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



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

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

         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  self.BERT(**lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  None
         UpperCAmelCase__							:				Tuple   				=  None

         UpperCAmelCase__							:				Dict   				=  W_supports['''input_ids'''] == start_token_id
         UpperCAmelCase__							:				Any   				=  W_supports['''input_ids'''] == end_token_id

         for i, size in enumerate(lowerCamelCase_							):
            if i == 0:
               UpperCAmelCase__							:				int   				=  0
            else:
               UpperCAmelCase__							:				str   				=  support_sizes[i - 1]

            UpperCAmelCase__							:				Dict   				=  S[s : s + size][start_token_masks[s : s + size]]
            UpperCAmelCase__							:				Union[str, Any]   				=  S[s : s + size][end_token_masks[s : s + size]]

            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_start.T							).sum(1							).softmax(0							)
            UpperCAmelCase__							:				int   				=  torch.matmul(q[i]					,s_end.T							).sum(1							).softmax(0							)

            if p_starts is not None:
               UpperCAmelCase__							:				str   				=  torch.vstack((p_starts, p_start)							)
               UpperCAmelCase__							:				Optional[int]   				=  torch.vstack((p_ends, p_end)							)
            else:
               UpperCAmelCase__							:				Union[str, Any]   				=  p_start
               UpperCAmelCase__							:				int   				=  p_end

         return p_starts, p_ends




