
'''simple docstring'''
from __future__ import annotations

import collections
import pprint
from pathlib import Path







def        A_     (						__SCREAMING_SNAKE_CASE						:  str )				->       str:





  """simple docstring"""

  return "".join(sorted(__SCREAMING_SNAKE_CASE ) )







def        A_     (						__SCREAMING_SNAKE_CASE						:  str )				->       list[str]:





  """simple docstring"""

  return word_by_signature[signature(__SCREAMING_SNAKE_CASE )]


A__   :		str 		=Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
A__   :		Dict 		=sorted({word.strip().lower() for word in data.splitlines()})

A__   :		Union[str, Any] 		=collections.defaultdict(list)
for word in word_list:
     word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
     A__   :		Tuple 		={word: anagram(word) for word in word_list if len(anagram(word)) > 1}

     with open('anagrams.txt', 'w') as file:
          file.write('all_anagrams = \n ')
          file.write(pprint.pformat(all_anagrams))





'''simple docstring'''
from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

A__   :		Any 		=logging.get_logger(__name__)







@add_end_docstrings(_SCREAMING_SNAKE_CASE			)
class  __A				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    Union[str, Any]				,   *lowerCamelCase			:    int				,   **lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   super().__init__(*lowerCamelCase				,   **lowerCamelCase       )
   self.check_model_type(lowerCamelCase       )
 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    Dict=None				,   lowerCamelCase			:    Union[str, Any]=None				,   lowerCamelCase			:    int=None				,   **lowerCamelCase			:    int       ):



   """simple docstring"""





   __A							,					__A		:     Tuple												=		{}, {}
   if padding is not None:
     __A		:     Any												=		padding
   if truncation is not None:
     __A		:     Optional[Any]												=		truncation
   if top_k is not None:
     __A		:     List[str]												=		top_k
   return preprocess_params, {}, postprocess_params
 def __call__(   self			:    Any				,   lowerCamelCase			:    Union["Image.Image", str]				,   lowerCamelCase			:    str = None				,   **lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   if isinstance(lowerCamelCase				,   (Image.Image, str)       ) and isinstance(lowerCamelCase				,   lowerCamelCase       ):
     __A		:     Tuple												=		{"""image""": image, """question""": question}
   else:
     __A		:     List[Any]												=		image
   __A		:     Any												=		super().__call__(lowerCamelCase				,   **lowerCamelCase       )
   return results
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    Optional[int]				,   lowerCamelCase			:    Dict=False				,   lowerCamelCase			:    str=False       ):



   """simple docstring"""





   __A		:     List[str]												=		load_image(inputs["""image"""]       )
   __A		:     Optional[Any]												=		self.tokenizer(
       inputs["""question"""]				,   return_tensors=self.framework				,   padding=lowerCamelCase				,   truncation=lowerCamelCase       )
   __A		:     Union[str, Any]												=		self.image_processor(images=lowerCamelCase				,   return_tensors=self.framework       )
   model_inputs.update(lowerCamelCase       )
   return model_inputs
 def 		lowercase_(   self			:    List[str]				,   lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   __A		:     List[str]												=		self.model(**lowerCamelCase       )
   return model_outputs






 def 		lowercase_(   self			:    str				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    List[Any]=5       ):



   """simple docstring"""





   if top_k > self.model.config.num_labels:
     __A		:     str												=		self.model.config.num_labels

   if self.framework == "pt":
     __A		:     Optional[Any]												=		model_outputs.logits.sigmoid()[0]
     __A							,					__A		:     int												=		probs.topk(lowerCamelCase       )
   else:
     raise ValueError(f"Unsupported framework: {self.framework}"       )

   __A		:     Optional[Any]												=		scores.tolist()
   __A		:     Dict												=		ids.tolist()
   return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase				,   lowerCamelCase       )]



