





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,
				)


__lowercase							:Union[str, Any]							=	logging.get_logger(__name__)


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



@add_end_docstrings(lowercase__				)
class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  List[Any]				,						*a					:  Optional[Any]				,						**a					:  Dict     )						->List[Any]:
													super().__init__(*a				,						**a     )

													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					:  List[Any]				,						**a					:  str     )						->Any:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       {}
													SCREAMING_SNAKE_CASE__  :					List[str] =       {}
													if "subtask" in kwargs:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       kwargs["subtask"]
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       kwargs["subtask"]
													if "threshold" in kwargs:
																			SCREAMING_SNAKE_CASE__  :					str =       kwargs["threshold"]
													if "mask_threshold" in kwargs:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       kwargs["mask_threshold"]
													if "overlap_mask_area_threshold" in kwargs:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       kwargs["overlap_mask_area_threshold"]

													return preprocess_kwargs, {}, postprocess_kwargs

							def __call__(   self					:  List[str]				,						a					:  Tuple				,						**a					:  Tuple     )						->Union[Predictions, List[Prediction]]:
													return super().__call__(a				,						**a     )

							def   A_	(   self					:  Tuple				,						a					:  Tuple				,						a					:  Any=None     )						->List[Any]:
													SCREAMING_SNAKE_CASE__  :					int =       load_image(a     )
													SCREAMING_SNAKE_CASE__  :					List[str] =       [(image.height, image.width)]
													if self.model.config.__class__.__name__ == "OneFormerConfig":
																			if subtask is None:
																									SCREAMING_SNAKE_CASE__  :					int =       {}
																			else:
																									SCREAMING_SNAKE_CASE__  :					int =       {"task_inputs": [subtask]}
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.image_processor(images=[image]				,						return_tensors="pt"				,						**a     )
																			SCREAMING_SNAKE_CASE__  :					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:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.image_processor(images=[image]				,						return_tensors="pt"     )
													SCREAMING_SNAKE_CASE__  :					Dict =       target_size
													return inputs

							def   A_	(   self					:  Optional[Any]				,						a					:  int     )						->int:
													SCREAMING_SNAKE_CASE__  :					Dict =       model_inputs.pop("target_size"     )
													SCREAMING_SNAKE_CASE__  :					Dict =       self.model(**a     )
													SCREAMING_SNAKE_CASE__  :					str =       target_size
													return model_outputs






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

													if fn is not None:
																			SCREAMING_SNAKE_CASE__  :					str =       fn(
																			    a				,						threshold=a				,						mask_threshold=a				,						overlap_mask_area_threshold=a				,						target_sizes=model_outputs["target_size"]				,						)[0]

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

																			for segment in outputs["segments_info"]:
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       (segmentation == segment["id"]) * 2_55
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       Image.fromarray(mask.numpy().astype(np.uinta     )				,						mode="L"     )
																									SCREAMING_SNAKE_CASE__  :					Tuple =       self.model.config.idalabel[segment["label_id"]]
																									SCREAMING_SNAKE_CASE__  :					Optional[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"     ):
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.image_processor.post_process_semantic_segmentation(
																			    a				,						target_sizes=model_outputs["target_size"]     )[0]

																			SCREAMING_SNAKE_CASE__  :					Dict =       []
																			SCREAMING_SNAKE_CASE__  :					List[str] =       outputs.numpy()
																			SCREAMING_SNAKE_CASE__  :					Tuple =       np.unique(a     )

																			for label in labels:
																									SCREAMING_SNAKE_CASE__  :					str =       (segmentation == label) * 2_55
																									SCREAMING_SNAKE_CASE__  :					Any =       Image.fromarray(mask.astype(np.uinta     )				,						mode="L"     )
																									SCREAMING_SNAKE_CASE__  :					int =       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 argparse
import json
import re
import string
import sys

import numpy as np


__lowercase							:Optional[Any]							=	0.5







def    UpperCAmelCase				(  _lowerCamelCase   :	int      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       [".", ",", ";", ":"]

						for token in remove_tokens:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.replace(_lowerCamelCase      , ""  )
												SCREAMING_SNAKE_CASE__  :					List[Any] =       prediction.replace(_lowerCamelCase      , ""  )

						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.lower(), prediction.lower()
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Dict =       ground_truth.replace("/"      , " "  ), prediction.replace("/"      , " "  )
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					str =       set(ground_truth.split(" "  )  ), set(prediction.split(" "  )  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       ground_truth.intersection(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       ground_truth.union(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       len(_lowerCamelCase  ) / len(_lowerCamelCase  )
						return jaccard







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						def remove_articles(_lowerCamelCase   :	Any  ):
												return re.sub(r"\b(a|an|the)\b"      , " "      , _lowerCamelCase  )

						def white_space_fix(_lowerCamelCase   :	Any  ):
												return " ".join(text.split()  )

						def remove_punc(_lowerCamelCase   :	List[Any]  ):
												SCREAMING_SNAKE_CASE__  :					Tuple =       set(string.punctuation  )
												return "".join(ch for ch in text if ch not in exclude  )

						def lower(_lowerCamelCase   :	str  ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase  )  )  )  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[int] =       0, 0, 0

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       "Parties" in qa_id

						# first check if ground truth is empty
						if len(_lowerCamelCase  ) == 0:
												if len(_lowerCamelCase  ) > 0:
																		fp += len(_lowerCamelCase  )  # false positive for each one
						else:
												for ground_truth in ground_truths:
																		assert len(_lowerCamelCase  ) > 0
																		# check if there is a match
																		SCREAMING_SNAKE_CASE__  :					Tuple =       False
																		for pred in predictions:
																								if substr_ok:
																														SCREAMING_SNAKE_CASE__  :					Optional[Any] =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
																								else:
																														SCREAMING_SNAKE_CASE__  :					Tuple =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH
																								if is_match:
																														SCREAMING_SNAKE_CASE__  :					str =       True

																		if match_found:
																								tp += 1
																		else:
																								fn += 1

        # now also get any fps by looping through preds
												for pred in predictions:
																		# Check if there's a match. if so, don't count (don't want to double count based on the above)
																		# but if there's no match, then this is a false positive.
																		# (Note: we get the true positives in the above loop instead of this loop so that we don't double count
																		# multiple predictions that are matched with the same answer.)
																		SCREAMING_SNAKE_CASE__  :					Tuple =       False
																		for ground_truth in ground_truths:
																								assert len(_lowerCamelCase  ) > 0
																								if substr_ok:
																														SCREAMING_SNAKE_CASE__  :					Tuple =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
																								else:
																														SCREAMING_SNAKE_CASE__  :					Dict =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH
																								if is_match:
																														SCREAMING_SNAKE_CASE__  :					List[Any] =       True

																		if not match_found:
																								fp += 1

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tp / (tp + fp) if tp + fp > 0 else np.nan
						SCREAMING_SNAKE_CASE__  :					Dict =       tp / (tp + fn) if tp + fn > 0 else np.nan

						return precision, recall







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					int =       precisions[::-1]
						for i in range(1      , len(_lowerCamelCase  )  ):
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       max(precision_best[i - 1]      , precision_best[i]  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       precision_best[::-1]
						return precisions







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict      , _lowerCamelCase   :	str  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Tuple =       process_precisions(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       np.trapz(_lowerCamelCase      , _lowerCamelCase  )
						if np.isnan(_lowerCamelCase  ):
												return 0
						return aupr







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       process_precisions(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[str] =       0
						for prec, recall in zip(_lowerCamelCase      , _lowerCamelCase  ):
												if recall >= recall_thresh:
																		SCREAMING_SNAKE_CASE__  :					Optional[int] =       prec
																		break
						return prec_at_recall







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						return normalize_answer(_lowerCamelCase  ) == normalize_answer(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	str      , _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       0
						for pred in predictions:
												for ground_truth in ground_truths:
																		SCREAMING_SNAKE_CASE__  :					Optional[Any] =       metric_fn(_lowerCamelCase      , _lowerCamelCase  )
																		if score == 1:  # break the loop when one prediction matches the ground truth
																								break
												if score == 1:
																		break
						return score







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       0
						SCREAMING_SNAKE_CASE__  :					List[str] =       []
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
						for article in dataset:
												for paragraph in article["paragraphs"]:
																		for qa in paragraph["qas"]:
																								total += 1
																								if qa["id"] not in predictions:
																														SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       "Unanswered question " + qa["id"] + " will receive score 0."
																														print(_lowerCamelCase      , file=sys.stderr  )
																														continue
																								SCREAMING_SNAKE_CASE__  :					Any =       [x["text"] for x in qa["answers"]]
																								SCREAMING_SNAKE_CASE__  :					int =       predictions[qa["id"]]
																								SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       compute_precision_recall(_lowerCamelCase      , _lowerCamelCase      , qa["id"]  )

																								precisions.append(_lowerCamelCase  )
																								recalls.append(_lowerCamelCase  )

																								if precision == 0 and recall == 0:
																														fa += 0
																								else:
																														fa += 2 * (precision * recall) / (precision + recall)

																								exact_match += metric_max_over_ground_truths(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       [x for _, x in sorted(zip(_lowerCamelCase      , _lowerCamelCase  )  )]
						recalls.sort()

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       1_0_0.0 * fa / total
						SCREAMING_SNAKE_CASE__  :					Dict =       1_0_0.0 * exact_match / total
						SCREAMING_SNAKE_CASE__  :					str =       get_aupr(_lowerCamelCase      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       get_prec_at_recall(_lowerCamelCase      , _lowerCamelCase      , recall_thresh=0.9  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       get_prec_at_recall(_lowerCamelCase      , _lowerCamelCase      , recall_thresh=0.8  )

						return {
						    "exact_match": exact_match,
						    "f1": fa,
						    "aupr": aupr,
						    "prec_at_80_recall": prec_at_aa_recall,
						    "prec_at_90_recall": prec_at_aa_recall,
						}


if __name__ == "__main__":
				__lowercase							:Tuple							=	argparse.ArgumentParser(description="Evaluation for CUAD")
				parser.add_argument("dataset_file", help="Dataset file")
				parser.add_argument("prediction_file", help="Prediction File")
				__lowercase							:Tuple							=	parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__lowercase							:Union[str, Any]							=	json.load(dataset_file)
								__lowercase							:Union[str, Any]							=	dataset_json["data"]
				with open(args.prediction_file) as prediction_file:
								__lowercase							:Optional[Any]							=	json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))