






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,
		)


__magic_name__:  Dict  	=			logging.get_logger(__name__)


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





@add_end_docstrings(_lowerCAmelCase   )
class  snake_case__      (							_lowerCAmelCase   ):
	def __init__(       self    ,     *lowerCAmelCase__    ,     **lowerCAmelCase__				)							->   Dict:
							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     __magic_name__       (       self    ,     **lowerCAmelCase__				)							->   Union[str, Any]:
							__magic_name__    :     Union[str, Any]     		=      {}
							__magic_name__    :     Tuple     		=      {}
							if "subtask" in kwargs:
													__magic_name__    :     Tuple     		=      kwargs["""subtask"""]
													__magic_name__    :     Optional[int]     		=      kwargs["""subtask"""]
							if "threshold" in kwargs:
													__magic_name__    :     Tuple     		=      kwargs["""threshold"""]
							if "mask_threshold" in kwargs:
													__magic_name__    :     Any     		=      kwargs["""mask_threshold"""]
							if "overlap_mask_area_threshold" in kwargs:
													__magic_name__    :     Optional[Any]     		=      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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__=None				)							->   Any:
							__magic_name__    :     int     		=      load_image(lowerCAmelCase__				)
							__magic_name__    :     Optional[int]     		=      [(image.height, image.width)]
							if self.model.config.__class__.__name__ == "OneFormerConfig":
													if subtask is None:
																			__magic_name__    :     Union[str, Any]     		=      {}
													else:
																			__magic_name__    :     Dict     		=      {"""task_inputs""": [subtask]}
													__magic_name__    :     Tuple     		=      self.image_processor(images=[image]    ,     return_tensors="""pt"""    ,     **lowerCAmelCase__				)
													__magic_name__    :     Optional[int]     		=      self.tokenizer(
													    inputs["""task_inputs"""]    ,     padding="""max_length"""    ,     max_length=self.model.config.task_seq_len    ,     return_tensors=self.framework    ,     )["""input_ids"""]
							else:
													__magic_name__    :     List[str]     		=      self.image_processor(images=[image]    ,     return_tensors="""pt"""				)
							__magic_name__    :     Optional[Any]     		=      target_size
							return inputs
	def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   Any:
							__magic_name__    :     List[str]     		=      model_inputs.pop("""target_size"""				)
							__magic_name__    :     str     		=      self.model(**lowerCAmelCase__				)
							__magic_name__    :     List[str]     		=      target_size
							return model_outputs







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

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

													__magic_name__    :     str     		=      []
													__magic_name__    :     int     		=      outputs["""segmentation"""]

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

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

													__magic_name__    :     Optional[int]     		=      []
													__magic_name__    :     Dict     		=      outputs.numpy()
													__magic_name__    :     Optional[int]     		=      np.unique(lowerCAmelCase__				)

													for label in labels:
																			__magic_name__    :     Optional[Any]     		=      (segmentation == label) * 2_55
																			__magic_name__    :     Dict     		=      Image.fromarray(mask.astype(np.uinta				)    ,     mode="""L"""				)
																			__magic_name__    :     Optional[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








from typing import Literal

__magic_name__:  List[Any]  	=			"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

def 					UpperCamelCase		( _A,					_A,					_A				):






						"""simple docstring"""







						__magic_name__    :     int     		=      LETTERS if mode == """decrypt""" else key
						__magic_name__    :     Optional[Any]     		=      key if mode == """decrypt""" else LETTERS
						__magic_name__    :     Optional[Any]     		=      """"""
						# loop through each symbol in the message
						for symbol in message:
												if symbol.upper() in chars_a:
																		# encrypt/decrypt the symbol
																		__magic_name__    :     Optional[Any]     		=      chars_a.find(symbol.upper()				)
																		if symbol.isupper():
																								translated += chars_b[sym_index].upper()
																		else:
																								translated += chars_b[sym_index].lower()
												else:
																		# symbol is not in LETTERS, just add it
																		translated += symbol
						return translated

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						return translate_message(_A,					_A,					"""encrypt"""				)

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						return translate_message(_A,					_A,					"""decrypt"""				)

def 					UpperCamelCase		( ):






						"""simple docstring"""







						__magic_name__    :     Optional[Any]     		=      """Hello World"""
						__magic_name__    :     str     		=      """QWERTYUIOPASDFGHJKLZXCVBNM"""
						__magic_name__    :     List[str]     		=      """decrypt"""  # set to 'encrypt' or 'decrypt'

						if mode == "encrypt":
												__magic_name__    :     Dict     		=      encrypt_message(_A,					_A				)
						elif mode == "decrypt":
												__magic_name__    :     Any     		=      decrypt_message(_A,					_A				)
						print(f'Using the key {key}, the {mode}ed message is: {translated}'				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()
