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,
			)


_SCREAMING_SNAKE_CASE:						Dict    	=       logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE:						str    	=       Dict[str, Any]
_SCREAMING_SNAKE_CASE:						List[str]    	=       List[Prediction]

@add_end_docstrings(_lowercase							)
class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				Any  ,					*__SCREAMING_SNAKE_CASE    :				Optional[int]  ,					**__SCREAMING_SNAKE_CASE    :				List[Any]   ):






					"""simple docstring"""
					super().__init__(*__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )

					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    :				Any  ,					**__SCREAMING_SNAKE_CASE    :				Union[str, Any]   ):






					"""simple docstring"""
					_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    :				Any  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]  ,					**__SCREAMING_SNAKE_CASE    :				Dict   ):






					"""simple docstring"""
					return super().__call__(__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				str=None   ):






					"""simple docstring"""
					_snake_case              =      load_image(__SCREAMING_SNAKE_CASE   )
					_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"  ,					**__SCREAMING_SNAKE_CASE   )
								_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    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      model_inputs.pop("target_size"   )
					_snake_case              =      self.model(**__SCREAMING_SNAKE_CASE   )
					_snake_case              =      target_size
					return model_outputs







		def 				_a				(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				int=None  ,					__SCREAMING_SNAKE_CASE    :				int=0.9  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=0.5  ,					__SCREAMING_SNAKE_CASE    :				int=0.5   ):






					"""simple docstring"""
					_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(
								    __SCREAMING_SNAKE_CASE  ,					threshold=__SCREAMING_SNAKE_CASE  ,					mask_threshold=__SCREAMING_SNAKE_CASE  ,					overlap_mask_area_threshold=__SCREAMING_SNAKE_CASE  ,					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(
								    __SCREAMING_SNAKE_CASE  ,					target_sizes=model_outputs["target_size"]   )[0]

								_snake_case              =      []
								_snake_case              =      outputs.numpy()
								_snake_case              =      np.unique(__SCREAMING_SNAKE_CASE   )

								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


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      0
					# max greyscale value for #FFFFFF
					_snake_case              =      int(self.get_greyscale(255  ,					255  ,					255   )   )

					if not self.min_threshold < threshold < self.max_threshold:
								_snake_case              =      F'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      input_img
					_snake_case              =      threshold
					_snake_case       , _snake_case              =      self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					_snake_case              =      [
					    [0 for _ in range(self.height + 4   )] for __ in range(self.width + 1   )
					]
					_snake_case              =      np.ones((self.width, self.height, 3)  ,					np.uinta   ) * 255



		@classmethod
		def 				_a				(			cls    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red







		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					for y in range(self.height   ):
								for x in range(self.width   ):
											_snake_case              =      int(self.get_greyscale(*self.input_img[y][x]   )   )
											if self.threshold > greyscale + self.error_table[y][x]:
														_snake_case              =      (0, 0, 0)
														_snake_case              =      greyscale + self.error_table[x][y]
											else:
														_snake_case              =      (255, 255, 255)
														_snake_case              =      greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error   )
											self.error_table[y][x + 2] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x] += int(8 / 32 * current_error   )
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error   )
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error   )


if __name__ == "__main__":
			# create Burke's instances with original images in greyscale
			_SCREAMING_SNAKE_CASE:						Tuple    	=       [
			    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
			    for threshold in (1, 126, 130, 140)
			]

			for burkes in burkes_instances:
						burkes.process()

			for burkes in burkes_instances:
						imshow(
						    F"Original image with dithering threshold: {burkes.threshold}",
						    burkes.output_img,
						)

			waitKey(0)
			destroyAllWindows()

