





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 				_a		(   _lowerCAmelCase       ):






				def __init__(				self	:					List[Any],				*lowerCAmelCase__	:					List[str],				**lowerCAmelCase__	:					Tuple							)			->   List[Any]:




										'''simple docstring'''



										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        snake_case			(				self	:					int,				**lowerCAmelCase__	:					str							)			->   List[str]:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					{}
										_UpperCamelCase    :      Dict						     =					{}
										if "subtask" in kwargs:
																_UpperCamelCase    :      str						     =					kwargs['''subtask''']
																_UpperCamelCase    :      List[Any]						     =					kwargs['''subtask''']
										if "threshold" in kwargs:
																_UpperCamelCase    :      str						     =					kwargs['''threshold''']
										if "mask_threshold" in kwargs:
																_UpperCamelCase    :      Optional[Any]						     =					kwargs['''mask_threshold''']
										if "overlap_mask_area_threshold" in kwargs:
																_UpperCamelCase    :      Dict						     =					kwargs['''overlap_mask_area_threshold''']

										return preprocess_kwargs, {}, postprocess_kwargs






				def __call__(				self	:					str,				lowerCAmelCase__	:					str,				**lowerCAmelCase__	:					Tuple							)			->   Union[Predictions, List[Prediction]]:




										'''simple docstring'''



										return super().__call__(lowerCAmelCase__,				**lowerCAmelCase__							)






				def        snake_case			(				self	:					Any,				lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					List[str]=None							)			->   Union[str, Any]:




										'''simple docstring'''



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






				def        snake_case			(				self	:					str,				lowerCAmelCase__	:					str							)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					model_inputs.pop('''target_size'''							)
										_UpperCamelCase    :      Optional[Any]						     =					self.model(**lowerCAmelCase__							)
										_UpperCamelCase    :      Optional[Any]						     =					target_size
										return model_outputs






				def        snake_case			(				self	:					int,				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					List[str]=None,				lowerCAmelCase__	:					Union[str, Any]=0.9,				lowerCAmelCase__	:					Tuple=0.5,				lowerCAmelCase__	:					List[str]=0.5							)			->   int:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					None
										if subtask in {"panoptic", None} and hasattr(self.image_processor,				'''post_process_panoptic_segmentation'''							):
																_UpperCamelCase    :      List[Any]						     =					self.image_processor.post_process_panoptic_segmentation
										elif subtask in {"instance", None} and hasattr(self.image_processor,				'''post_process_instance_segmentation'''							):
																_UpperCamelCase    :      Union[str, Any]						     =					self.image_processor.post_process_instance_segmentation

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

																_UpperCamelCase    :      str						     =					[]
																_UpperCamelCase    :      str						     =					outputs['''segmentation''']

																for segment in outputs["segments_info"]:
																						_UpperCamelCase    :      int						     =					(segmentation == segment['''id''']) * 2_5_5
																						_UpperCamelCase    :      List[str]						     =					Image.fromarray(mask.numpy().astype(np.uinta							),				mode='''L'''							)
																						_UpperCamelCase    :      Optional[int]						     =					self.model.config.idalabel[segment['''label_id''']]
																						_UpperCamelCase    :      Union[str, Any]						     =					segment['''score''']
																						annotation.append({'''score''': score, '''label''': label, '''mask''': mask}							)

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

																_UpperCamelCase    :      List[Any]						     =					[]
																_UpperCamelCase    :      Dict						     =					outputs.numpy()
																_UpperCamelCase    :      List[Any]						     =					np.unique(lowerCAmelCase__							)

																for label in labels:
																						_UpperCamelCase    :      Tuple						     =					(segmentation == label) * 2_5_5
																						_UpperCamelCase    :      List[str]						     =					Image.fromarray(mask.astype(np.uinta							),				mode='''L'''							)
																						_UpperCamelCase    :      Tuple						     =					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"""




UpperCamelCase_     =1_000





def  a_       (							_lowercase     ):
						return x * x





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase = 4     ):
						assert callable(
						    _lowercase     ), F"""the function(object) passed should be callable your input : {function}"""
						assert isinstance(_lowercase ,		(float, int)     ), F"""a should be float or integer your input : {a}"""
						assert isinstance(function(_lowercase     ) ,		(float, int)     ), (
						    "the function should return integer or float return type of your function, "
						    F"""{type(_lowercase     )}"""
						)
						assert isinstance(_lowercase ,		(float, int)     ), F"""b should be float or integer your input : {b}"""
						assert (
						    isinstance(_lowercase ,		_lowercase     ) and precision > 0
						), F"""precision should be positive integer your input : {precision}"""

						# just applying the formula of simpson for approximate integration written in
						# mentioned article in first comment of this file and above this function

						_UpperCamelCase    :      str						     =					(b - a) / N_STEPS
						_UpperCamelCase    :      Dict						     =					function(_lowercase     ) + function(_lowercase     )

						for i in range(1 ,		_lowercase     ):
												_UpperCamelCase    :      List[Any]						     =					a + h * i
												result += function(_lowercase     ) * (4 if i % 2 else 2)

						result *= h / 3
						return round(_lowercase ,		_lowercase     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



