
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     :	Union[str, Any]    = logging.get_logger(__name__)







@add_end_docstrings(__UpperCAmelCase			)
class 			__A( __UpperCAmelCase			):




				def __init__(     self		,     *_snake_case		,     **_snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								super().__init__(*__SCREAMING_SNAKE_CASE		,     **__SCREAMING_SNAKE_CASE				)
								self.check_model_type(__SCREAMING_SNAKE_CASE				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      List[Any]:


								'''simple docstring'''




								__a							,     __a										=    {}, {}
								if padding is not None:
												__a										=    padding
								if truncation is not None:
												__a										=    truncation
								if top_k is not None:
												__a										=    top_k
								return preprocess_params, {}, postprocess_params




				def __call__(     self		,     _snake_case		,     _snake_case = None		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




								if isinstance(__SCREAMING_SNAKE_CASE		,     (Image.Image, str)				) and isinstance(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE				):
												__a										=    {'''image''': image, '''question''': question}
								else:
												__a										=    image
								__a										=    super().__call__(__SCREAMING_SNAKE_CASE		,     **__SCREAMING_SNAKE_CASE				)
								return results




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=False		,     _snake_case=False				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    load_image(inputs['''image''']				)
								__a										=    self.tokenizer(
								    inputs['''question''']		,     return_tensors=self.framework		,     padding=__SCREAMING_SNAKE_CASE		,     truncation=__SCREAMING_SNAKE_CASE				)
								__a										=    self.image_processor(images=__SCREAMING_SNAKE_CASE		,     return_tensors=self.framework				)
								model_inputs.update(__SCREAMING_SNAKE_CASE				)
								return model_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.model(**__SCREAMING_SNAKE_CASE				)
								return model_outputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=5				)    ->      Optional[Any]:


								'''simple docstring'''




								if top_k > self.model.config.num_labels:
												__a										=    self.model.config.num_labels

								if self.framework == "pt":
												__a										=    model_outputs.logits.sigmoid()[0]
												__a							,     __a										=    probs.topk(__SCREAMING_SNAKE_CASE				)
								else:
												raise ValueError(F"""Unsupported framework: {self.framework}"""				)

								__a										=    scores.tolist()
								__a										=    ids.tolist()
								return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE				)]




from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests





def      __snake_case	(			_UpperCAmelCase = "isbn/0140328726"				):
    __a						       =				olid.strip().strip('''/'''				)  # Remove leading/trailing whitespace & slashes
    if new_olid.count('''/'''				) != 1:
        __a						       =				f'{olid} is not a valid Open Library olid'
        raise ValueError(_UpperCAmelCase				)
    return requests.get(f'https://openlibrary.org/{new_olid}.json'				).json()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				{
        '''title''': '''Title''',
        '''publish_date''': '''Publish date''',
        '''authors''': '''Authors''',
        '''number_of_pages''': '''Number of pages:''',
        '''first_sentence''': '''First sentence''',
        '''isbn_10''': '''ISBN (10)''',
        '''isbn_13''': '''ISBN (13)''',
    }
    __a						       =				{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
    __a						       =				[
        get_openlibrary_data(author['''key''']				)['''name'''] for author in data['''Authors''']
    ]
    __a						       =				data['''First sentence''']['''value''']
    for key, value in data.items():
        if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
            __a						       =				''', '''.join(_UpperCAmelCase				)
    return data


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 while True:
  __snake_case						:List[Any]      		=     input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
  if isbn.lower() in ("", "q", "quit", "exit", "stop"):
   break

  if len(isbn) not in (10, 13) or not isbn.isdigit():
   print(f'Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.')
   continue

  print(f'\nSearching Open Library for ISBN: {isbn}...\n')

  try:
   __snake_case						:Optional[Any]      		=     summarize_book(get_openlibrary_data(f'isbn/{isbn}'))
   print('''\n'''.join(f'{key}: {value}' for key, value in book_summary.items()))
  except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
   print(f'Sorry, there are no results for ISBN: {isbn}.')





