





import torch

from transformers import AutoTokenizer
class 	UpperCamelCase_						(     __lowerCAmelCase    ):




					def __init__(						self ,  lowerCAmelCase_     )						->							List[Any]:
										_snake_case													= AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE     )




					def 		lowerCAmelCase	(						self ,  lowerCAmelCase_     )						->							Tuple:

										if isinstance(__SCREAMING_SNAKE_CASE ,  __SCREAMING_SNAKE_CASE     ) and all(isinstance(_x ,  __SCREAMING_SNAKE_CASE     ) for _x in x     ):
															_snake_case													= None
															for l in x:
																				_snake_case													= self.tokenizer(
																				    __SCREAMING_SNAKE_CASE ,  padding='max_length' ,  max_length=384 ,  truncation=__SCREAMING_SNAKE_CASE ,  return_tensors='pt' ,  )
																				_snake_case													= torch.tensor([len(__SCREAMING_SNAKE_CASE     )]     )
																				if d is not None:
																									for k in d.keys():
																														_snake_case													= torch.cat((d[k], t[k]) ,  0     )
																				else:
																									_snake_case													= t

															_snake_case													= torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'     )     )
															_snake_case													= torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'     )     )

										elif isinstance(__SCREAMING_SNAKE_CASE ,  __SCREAMING_SNAKE_CASE     ) and all(isinstance(_x ,  __SCREAMING_SNAKE_CASE     ) for _x in x     ):
															_snake_case													= self.tokenizer(
															    __SCREAMING_SNAKE_CASE ,  padding='max_length' ,  max_length=384 ,  truncation=__SCREAMING_SNAKE_CASE ,  return_tensors='pt' ,  )

										else:
															raise Exception(
															    'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
															    ' strings` for supports are supported.'     )

										return d






					def 		lowerCAmelCase	(						self ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_=0.70     )						->							Optional[Any]:
										_snake_case													= []
										for idx in range(len(W_query['input_ids']     )     ):
															_snake_case													= _snake_case													= range(p_start.shape[1]     )

															_snake_case													= []
															for start_id in start_indexes:
																				for end_id in end_indexes:
																									if start_id < end_id:
																														output.append(
																														    (
																														        start_id,
																														        end_id,
																														        p_start[idx][start_id].item(),
																														        p_end[idx][end_id].item(),
																														    )     )

															output.sort(key=lambda lowerCAmelCase_     : (tup[2] * tup[3]) ,  reverse=__SCREAMING_SNAKE_CASE     )
															_snake_case													= []
															for k in range(len(__SCREAMING_SNAKE_CASE     )     ):
																				if output[k][2] * output[k][3] >= thresh:
																									_snake_case							,   _snake_case													= output[k][0], output[k][1]
																									_snake_case													= self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]     )
																									temp.append((decoded, output[k][2] * output[k][3])     )

															final_outputs.append(__SCREAMING_SNAKE_CASE     )

										return final_outputs










"""simple docstring"""


from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION



class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):

       def __init__(	self   ):






        '''simple docstring'''

        super().__init__()

        self.register_buffer("watermark_image"      ,    torch.zeros((62, 62, 4)   )   )
        snake_case__										= None

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= images[0].height
        snake_case__										= images[0].width

        snake_case__										= sample_size or h

        snake_case__										= min(h / sample_size      ,    w / sample_size   )
        snake_case__							,      snake_case__										= (int(h / coef   ), int(w / coef   )) if coef < 1 else (h, w)

        snake_case__							,      snake_case__										= 1024**2, img_w * img_h
        snake_case__										= (Sa / Sa) ** 0.5
        snake_case__							,      snake_case__							,      snake_case__										= int(K * 62   ), img_w - int(14 * K   ), img_h - int(14 * K   )

        if self.watermark_image_as_pil is None:
         snake_case__										= self.watermark_image.to(torch.uinta   ).cpu().numpy()
         snake_case__										= Image.fromarray(__SCREAMING_SNAKE_CASE      ,    mode="RGBA"   )
         snake_case__										= watermark_image

        snake_case__										= self.watermark_image_as_pil.resize(
            (wm_size, wm_size)      ,    PIL_INTERPOLATION["bicubic"]      ,    reducing_gap=__SCREAMING_SNAKE_CASE   )

        for pil_img in images:
         pil_img.paste(__SCREAMING_SNAKE_CASE      ,    box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)      ,    mask=wm_img.split()[-1]   )

        return images

