

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									=  logging.get_logger(__name__)


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






@add_end_docstrings(snake_case__   )
class 				A_					(						snake_case__   ):


     def __init__(				self     :						Any     ,    *UpperCAmelCase     :						str     ,    **UpperCAmelCase     :						int   )				->      str:
      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     UpperCAmelCase    (				self     :						List[str]     ,    **UpperCAmelCase     :						str   )				->      Union[str, Any]:
      __lowerCAmelCase:  Any        = {}
      __lowerCAmelCase:  Tuple        = {}
      if "subtask" in kwargs:
       __lowerCAmelCase:  List[str]        = kwargs['subtask']
       __lowerCAmelCase:  List[str]        = kwargs['subtask']
      if "threshold" in kwargs:
       __lowerCAmelCase:  int        = kwargs['threshold']
      if "mask_threshold" in kwargs:
       __lowerCAmelCase:  Dict        = kwargs['mask_threshold']
      if "overlap_mask_area_threshold" in kwargs:
       __lowerCAmelCase:  List[str]        = kwargs['overlap_mask_area_threshold']

      return preprocess_kwargs, {}, postprocess_kwargs


     def __call__(				self     :						List[str]     ,    UpperCAmelCase     :						Any     ,    **UpperCAmelCase     :						Dict   )				->      str:
      return super().__call__(UpperCAmelCase     ,    **UpperCAmelCase   )


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


     def     UpperCAmelCase    (				self     :						int     ,    UpperCAmelCase     :						Optional[Any]   )				->      Optional[Any]:
      __lowerCAmelCase:  Union[str, Any]        = model_inputs.pop('target_size'   )
      __lowerCAmelCase:  List[Any]        = self.model(**UpperCAmelCase   )
      __lowerCAmelCase:  str        = target_size
      return model_outputs


     def     UpperCAmelCase    (				self     :						List[str]     ,    UpperCAmelCase     :						Dict     ,    UpperCAmelCase     :						Dict=None     ,    UpperCAmelCase     :						Tuple=0.9     ,    UpperCAmelCase     :						int=0.5     ,    UpperCAmelCase     :						Dict=0.5   )				->      str:
      __lowerCAmelCase:  Optional[Any]        = None
      if subtask in {"panoptic", None} and hasattr(self.image_processor     ,    'post_process_panoptic_segmentation'   ):
       __lowerCAmelCase:  Dict        = self.image_processor.post_process_panoptic_segmentation
      elif subtask in {"instance", None} and hasattr(self.image_processor     ,    'post_process_instance_segmentation'   ):
       __lowerCAmelCase:  Any        = self.image_processor.post_process_instance_segmentation

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

       __lowerCAmelCase:  str        = []
       __lowerCAmelCase:  List[Any]        = outputs['segmentation']

       for segment in outputs["segments_info"]:
        __lowerCAmelCase:  str        = (segmentation == segment['id']) * 2_5_5
        __lowerCAmelCase:  int        = Image.fromarray(mask.numpy().astype(np.uinta   )     ,    mode='L'   )
        __lowerCAmelCase:  Dict        = self.model.config.idalabel[segment['label_id']]
        __lowerCAmelCase:  List[str]        = 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:  int        = self.image_processor.post_process_semantic_segmentation(
           UpperCAmelCase     ,    target_sizes=model_outputs['target_size']   )[0]

       __lowerCAmelCase:  Optional[Any]        = []
       __lowerCAmelCase:  Optional[int]        = outputs.numpy()
       __lowerCAmelCase:  Optional[Any]        = np.unique(UpperCAmelCase   )

       for label in labels:
        __lowerCAmelCase:  Optional[int]        = (segmentation == label) * 2_5_5
        __lowerCAmelCase:  str        = Image.fromarray(mask.astype(np.uinta   )     ,    mode='L'   )
        __lowerCAmelCase:  Union[str, 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 os
from string import ascii_letters

SCREAMING_SNAKE_CASE__       =	ascii_letters + " \t\n"





def  lowerCAmelCase__     (      )       ->					dict[str, None]:





						"""simple docstring"""






						snake_case             =      os.path.split(os.path.realpath(_UpperCamelCase       )       )
						snake_case             =      {}
						with open(path[0] + '/dictionary.txt'       ) as dictionary_file:
												for word in dictionary_file.read().split('\n'       ):
																		snake_case             =      None
						return english_words


SCREAMING_SNAKE_CASE__       =	load_dictionary()





def  lowerCAmelCase__     (      _UpperCamelCase							:					str       )       ->					float:





						"""simple docstring"""






						snake_case             =      message.upper()
						snake_case             =      remove_non_letters(_UpperCamelCase       )
						snake_case             =      message.split()
						snake_case             =      len([word for word in possible_words if word in ENGLISH_WORDS]       )
						return float(_UpperCamelCase       ) / len(_UpperCamelCase       )





def  lowerCAmelCase__     (      _UpperCamelCase							:					str       )       ->					str:





						"""simple docstring"""






						return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE       )





def  lowerCAmelCase__     (      _UpperCamelCase							:					str			,      _UpperCamelCase							:					int = 2_0			,      _UpperCamelCase							:					int = 8_5       )       ->					bool:





						"""simple docstring"""






						snake_case             =      get_english_count(_UpperCamelCase       ) * 1_0_0 >= word_percentage
						snake_case             =      len(remove_non_letters(_UpperCamelCase       )       )
						snake_case             =      (float(_UpperCamelCase       ) / len(_UpperCamelCase       )) * 1_0_0
						snake_case             =      message_letters_percentage >= letter_percentage
						return words_match and letters_match


if __name__ == "__main__":
					import doctest

					doctest.testmod()


