
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 		__magic_name__				( lowerCAmelCase__					):


  """simple docstring"""





  def __init__(					self	,		*a__	,		**a__		):
    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     _UpperCAmelCase  (					self	,		**a__		):
    _lowerCamelCase      =						{}
    _lowerCamelCase      =						{}
    if "subtask" in kwargs:
      _lowerCamelCase      =						kwargs['''subtask''']
      _lowerCamelCase      =						kwargs['''subtask''']
    if "threshold" in kwargs:
      _lowerCamelCase      =						kwargs['''threshold''']
    if "mask_threshold" in kwargs:
      _lowerCamelCase      =						kwargs['''mask_threshold''']
    if "overlap_mask_area_threshold" in kwargs:
      _lowerCamelCase      =						kwargs['''overlap_mask_area_threshold''']

    return preprocess_kwargs, {}, postprocess_kwargs


  def __call__(					self	,		a__	,		**a__		):
    return super().__call__(lowerCAmelCase__	,		**lowerCAmelCase__		)


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


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						model_inputs.pop('''target_size'''		)
    _lowerCamelCase      =						self.model(**lowerCAmelCase__		)
    _lowerCamelCase      =						target_size
    return model_outputs


  def     _UpperCAmelCase  (					self	,		a__	,		a__=None	,		a__=0.9	,		a__=0.5	,		a__=0.5		):
    _lowerCamelCase      =						None
    if subtask in {"panoptic", None} and hasattr(self.image_processor	,		'''post_process_panoptic_segmentation'''		):
      _lowerCamelCase      =						self.image_processor.post_process_panoptic_segmentation
    elif subtask in {"instance", None} and hasattr(self.image_processor	,		'''post_process_instance_segmentation'''		):
      _lowerCamelCase      =						self.image_processor.post_process_instance_segmentation

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

      _lowerCamelCase      =						[]
      _lowerCamelCase      =						outputs['''segmentation''']

      for segment in outputs["segments_info"]:
        _lowerCamelCase      =						(segmentation == segment['''id''']) * 2_55
        _lowerCamelCase      =						Image.fromarray(mask.numpy().astype(np.uinta		)	,		mode='''L'''		)
        _lowerCamelCase      =						self.model.config.idalabel[segment['''label_id''']]
        _lowerCamelCase      =						segment['''score''']
        annotation.append({'''score''': score, '''label''': label, '''mask''': mask}		)

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

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

      for label in labels:
        _lowerCamelCase      =						(segmentation == label) * 2_55
        _lowerCamelCase      =						Image.fromarray(mask.astype(np.uinta		)	,		mode='''L'''		)
        _lowerCamelCase      =						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)
        )
    )

