






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      =				logging.get_logger(__name__)





@add_end_docstrings(__A					)
class 						SCREAMING_SNAKE_CASE				( __A					):





							"""simple docstring"""

							def __init__(  self:   List[str]		,     *__A:   List[Any]		,     **__A:   Optional[Any]   )    ->				Union[str, Any]:
												super().__init__(*__A		,     **__A   )
												self.check_model_type(__A   )

							def       __A (  self:   Any		,     __A:   List[Any]=None		,     __A:   Union[str, Any]=None		,     __A:   List[Any]=None		,     **__A:   Dict   )    ->				List[Any]:
												_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:   List[str]		,     __A:   List[Any]		,     __A:   Dict = None		,     **__A:   str   )    ->				str:





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

							def       __A (  self:   Dict		,     __A:   List[Any]		,     __A:   Dict=False		,     __A:   int=False   )    ->				int:
												_A								= load_image(inputs['''image''']   )
												_A								= self.tokenizer(
												    inputs['''question''']		,     return_tensors=self.framework		,     padding=__A		,     truncation=__A   )
												_A								= self.image_processor(images=__A		,     return_tensors=self.framework   )
												model_inputs.update(__A   )
												return model_inputs

							def       __A (  self:   List[str]		,     __A:   List[str]   )    ->				str:
												_A								= self.model(**__A   )
												return model_outputs



							def       __A (  self:   Optional[Any]		,     __A:   str		,     __A:   Tuple=5   )    ->				int:





												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								= probs.topk(__A   )
												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(__A		,     __A   )]











from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def      __A					(     _lowercase = "" ):

					'''simple docstring'''







					_A								= url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					_A								= BeautifulSoup(requests.get(_lowercase ).text					,			'''html.parser''' )
					_A								= soup.find_all('''td'''					,			attrs='''titleColumn''' )
					_A								= soup.find_all('''td'''					,			class_='''ratingColumn imdbRating''' )
					return {
					    title.a.text: float(rating.strong.text )
					    for title, rating in zip(_lowercase					,			_lowercase )
					}




def      __A					(     _lowercase = "IMDb_Top_250_Movies.csv" ):

					'''simple docstring'''







					_A								= get_imdb_top_aaa_movies()
					with open(_lowercase					,			'''w'''					,			newline='''''' ) as out_file:
										_A								= csv.writer(_lowercase )
										writer.writerow(['''Movie title''', '''IMDb rating'''] )
										for title, rating in movies.items():
															writer.writerow([title, rating] )


if __name__ == "__main__":
				write_movies()



