"""simple docstring"""


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,
    )


__UpperCamelCase										=     logging.get_logger(__name__)


__UpperCamelCase										=     Dict[str, Any]
__UpperCamelCase										=     List[Prediction]




@add_end_docstrings(lowerCAmelCase__  )
class 					UpperCamelCase   (							lowerCAmelCase__  ):







       def __init__(					self,				*lowerCAmelCase__,				**lowerCAmelCase__) -> Optional[Any]:
            super().__init__(*lowerCAmelCase__,				**lowerCAmelCase__)

            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__) -> List[Any]:
            snake_case_   			=   {}
            snake_case_   			=   {}
            if "subtask" in kwargs:
                 snake_case_   			=   kwargs['subtask']
                 snake_case_   			=   kwargs['subtask']
            if "threshold" in kwargs:
                 snake_case_   			=   kwargs['threshold']
            if "mask_threshold" in kwargs:
                 snake_case_   			=   kwargs['mask_threshold']
            if "overlap_mask_area_threshold" in kwargs:
                 snake_case_   			=   kwargs['overlap_mask_area_threshold']

            return preprocess_kwargs, {}, postprocess_kwargs







       def __call__(					self,				lowerCAmelCase__,				**lowerCAmelCase__) -> Union[Predictions, List[Prediction]]:
            return super().__call__(lowerCAmelCase__,				**lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__=None) -> str:
            snake_case_   			=   load_image(lowerCAmelCase__)
            snake_case_   			=   [(image.height, image.width)]
            if self.model.config.__class__.__name__ == "OneFormerConfig":
                 if subtask is None:
                      snake_case_   			=   {}
                 else:
                      snake_case_   			=   {'task_inputs': [subtask]}
                 snake_case_   			=   self.image_processor(images=[image],				return_tensors='pt',				**lowerCAmelCase__)
                 snake_case_   			=   self.tokenizer(
                     inputs['task_inputs'],				padding='max_length',				max_length=self.model.config.task_seq_len,				return_tensors=self.framework,				)['input_ids']
            else:
                 snake_case_   			=   self.image_processor(images=[image],				return_tensors='pt')
            snake_case_   			=   target_size
            return inputs







       def 						a_		(					self,				lowerCAmelCase__) -> Optional[Any]:
            snake_case_   			=   model_inputs.pop('target_size')
            snake_case_   			=   self.model(**lowerCAmelCase__)
            snake_case_   			=   target_size
            return model_outputs







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__=None,				lowerCAmelCase__=0.9,				lowerCAmelCase__=0.5,				lowerCAmelCase__=0.5) -> Optional[int]:
            snake_case_   			=   None
            if subtask in {"panoptic", None} and hasattr(self.image_processor,				'post_process_panoptic_segmentation'):
                 snake_case_   			=   self.image_processor.post_process_panoptic_segmentation
            elif subtask in {"instance", None} and hasattr(self.image_processor,				'post_process_instance_segmentation'):
                 snake_case_   			=   self.image_processor.post_process_instance_segmentation

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

                 snake_case_   			=   []
                 snake_case_   			=   outputs['segmentation']

                 for segment in outputs["segments_info"]:
                      snake_case_   			=   (segmentation == segment['id']) * 255
                      snake_case_   			=   Image.fromarray(mask.numpy().astype(np.uinta),				mode='L')
                      snake_case_   			=   self.model.config.idalabel[segment['label_id']]
                      snake_case_   			=   segment['score']
                      annotation.append({'score': score, 'label': label, 'mask': mask})

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

                 snake_case_   			=   []
                 snake_case_   			=   outputs.numpy()
                 snake_case_   			=   np.unique(lowerCAmelCase__)

                 for label in labels:
                      snake_case_   			=   (segmentation == label) * 255
                      snake_case_   			=   Image.fromarray(mask.astype(np.uinta),				mode='L')
                      snake_case_   			=   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       (						UpperCAmelCase   )							-> dict[str, str]:
     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       (						UpperCAmelCase = 5   )							-> tuple:
     snake_case_   			=   int(float(UpperCAmelCase   )   )
     if num_movies < 1:
          return ()
     snake_case_   			=   (
         'https://www.imdb.com/search/title?title_type='
         f'feature&sort=num_votes,desc&count={num_movies}'
     )
     snake_case_   			=   bsa.BeautifulSoup(requests.get(UpperCAmelCase   ).content		,   'html.parser'   )
     return tuple(
         get_movie_data_from_soup(UpperCAmelCase   )
         for movie in source.find_all('div'		,   class_='lister-item mode-advanced'   )   )


if __name__ == "__main__":
    import json

    __UpperCamelCase										=     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)
        )
    )

