






from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
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_IMAGE_SEGMENTATION_MAPPING,
       MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
       MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
       MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
   )


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


A_		:		Union[str, Any]			= Dict[str, Any]
A_		:		Optional[int]			= List[Prediction]




@add_end_docstrings(__SCREAMING_SNAKE_CASE					)
class 				_lowerCAmelCase(       __SCREAMING_SNAKE_CASE					):





       """simple docstring"""







       def __init__(					self  ,			*_lowerCamelCase  ,			**_lowerCamelCase							):
         super().__init__(*UpperCamelCase__  ,			**UpperCamelCase__							)

         if self.framework == "tf":
           raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''							)

         requires_backends(self  ,			'vision'							)
         self.check_model_type(
             dict(
                 MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                 + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                 + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                 + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()							)							)





       def 			_a    (					self  ,			**_lowerCamelCase							):
         UpperCamelCase_:					List[str]          =				{}
         UpperCamelCase_:					Tuple          =				{}
         if "subtask" in kwargs:
           UpperCamelCase_:					Any          =				kwargs['''subtask''']
           UpperCamelCase_:					Optional[int]          =				kwargs['''subtask''']
         if "threshold" in kwargs:
           UpperCamelCase_:					int          =				kwargs['''threshold''']
         if "mask_threshold" in kwargs:
           UpperCamelCase_:					Any          =				kwargs['''mask_threshold''']
         if "overlap_mask_area_threshold" in kwargs:
           UpperCamelCase_:					Any          =				kwargs['''overlap_mask_area_threshold''']

         return preprocess_kwargs, {}, postprocess_kwargs





       def __call__(					self  ,			_lowerCamelCase  ,			**_lowerCamelCase							):
         return super().__call__(UpperCamelCase__  ,			**UpperCamelCase__							)





       def 			_a    (					self  ,			_lowerCamelCase  ,			_lowerCamelCase=None							):
         UpperCamelCase_:					Optional[Any]          =				load_image(UpperCamelCase__							)
         UpperCamelCase_:					str          =				[(image.height, image.width)]
         if self.model.config.__class__.__name__ == "OneFormerConfig":
           if subtask is None:
             UpperCamelCase_:					Union[str, Any]          =				{}
           else:
             UpperCamelCase_:					Any          =				{'''task_inputs''': [subtask]}
           UpperCamelCase_:					str          =				self.image_processor(images=[image]  ,			return_tensors='pt'  ,			**UpperCamelCase__							)
           UpperCamelCase_:					Union[str, Any]          =				self.tokenizer(
               inputs['task_inputs']  ,			padding='max_length'  ,			max_length=self.model.config.task_seq_len  ,			return_tensors=self.framework  ,			)['''input_ids''']
         else:
           UpperCamelCase_:					Union[str, Any]          =				self.image_processor(images=[image]  ,			return_tensors='pt'							)
         UpperCamelCase_:					Optional[Any]          =				target_size
         return inputs





       def 			_a    (					self  ,			_lowerCamelCase							):
         UpperCamelCase_:					List[Any]          =				model_inputs.pop('target_size'							)
         UpperCamelCase_:					Union[str, Any]          =				self.model(**UpperCamelCase__							)
         UpperCamelCase_:					str          =				target_size
         return model_outputs





       def 			_a    (					self  ,			_lowerCamelCase  ,			_lowerCamelCase=None  ,			_lowerCamelCase=0.9  ,			_lowerCamelCase=0.5  ,			_lowerCamelCase=0.5							):
         UpperCamelCase_:					Any          =				None
         if subtask in {"panoptic", None} and hasattr(self.image_processor  ,			'post_process_panoptic_segmentation'							):
           UpperCamelCase_:					Optional[Any]          =				self.image_processor.post_process_panoptic_segmentation
         elif subtask in {"instance", None} and hasattr(self.image_processor  ,			'post_process_instance_segmentation'							):
           UpperCamelCase_:					int          =				self.image_processor.post_process_instance_segmentation

         if fn is not None:
           UpperCamelCase_:					Any          =				fn(
               UpperCamelCase__  ,			threshold=UpperCamelCase__  ,			mask_threshold=UpperCamelCase__  ,			overlap_mask_area_threshold=UpperCamelCase__  ,			target_sizes=model_outputs['target_size']  ,			)[0]

           UpperCamelCase_:					List[Any]          =				[]
           UpperCamelCase_:					Union[str, Any]          =				outputs['''segmentation''']

           for segment in outputs["segments_info"]:
             UpperCamelCase_:					Any          =				(segmentation == segment['''id''']) * 2_5_5
             UpperCamelCase_:					Dict          =				Image.fromarray(mask.numpy().astype(np.uinta							)  ,			mode='L'							)
             UpperCamelCase_:					Optional[Any]          =				self.model.config.idalabel[segment['''label_id''']]
             UpperCamelCase_:					Dict          =				segment['''score''']
             annotation.append({'score': score, 'label': label, 'mask': mask}							)

         elif subtask in {"semantic", None} and hasattr(self.image_processor  ,			'post_process_semantic_segmentation'							):
           UpperCamelCase_:					Optional[int]          =				self.image_processor.post_process_semantic_segmentation(
               UpperCamelCase__  ,			target_sizes=model_outputs['target_size']							)[0]

           UpperCamelCase_:					str          =				[]
           UpperCamelCase_:					Dict          =				outputs.numpy()
           UpperCamelCase_:					List[Any]          =				np.unique(UpperCamelCase__							)

           for label in labels:
             UpperCamelCase_:					List[Any]          =				(segmentation == label) * 2_5_5
             UpperCamelCase_:					Dict          =				Image.fromarray(mask.astype(np.uinta							)  ,			mode='L'							)
             UpperCamelCase_:					List[Any]          =				self.model.config.idalabel[label]
             annotation.append({'score': None, 'label': label, 'mask': mask}							)
         else:
           raise ValueError(f'''Subtask {subtask} is not supported for model {type(self.model							)}'''							)
         return annotation

'''simple docstring'''
import bsa
import requests




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				dict[str, str]:


		'''simple docstring'''


		return {
		    "name": soup.ha.a.text,
		    "genre": soup.find('''span'''      ,    class_='''genre'''       ).text.strip(),
		    "rating": soup.strong.text,
		    "page_link": F'https://www.imdb.com{soup.a.get("href"       )}',
		}




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__ = 5       )   ->				tuple:


		'''simple docstring'''


		snake_case   :    List[Any]			       =		int(float(SCREAMING_SNAKE_CASE__       )       )
		if num_movies < 1:
				return ()
		snake_case   :    int			       =		(
		    '''https://www.imdb.com/search/title?title_type='''
		    F'feature&sort=num_votes,desc&count={num_movies}'
		)
		snake_case   :    Optional[Any]			       =		bsa.BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__       ).content      ,    '''html.parser'''       )
		return tuple(
		    get_movie_data_from_soup(SCREAMING_SNAKE_CASE__       )
		    for movie in source.find_all('''div'''      ,    class_='''lister-item mode-advanced'''       )       )


if __name__ == "__main__":
						import json

						lowercase__		    =	int(input("How many movies would you like to see? "))
						print(
						    ", ".join(
						        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
						    )
						)


