




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        lowerCamelCase__(   __lowerCamelCase):
      def __init__(							self:					Optional[int]       ,			*UpperCamelCase_:					Optional[Any]       ,			**UpperCamelCase_:					Optional[int]  ):
             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      lowerCAmelCase__		(							self:					Any       ,			**UpperCamelCase_:					List[str]  ):
             __lowerCamelCase  =		{}
             __lowerCamelCase  =		{}
             if "subtask" in kwargs:
                    __lowerCamelCase  =		kwargs["""subtask"""]
                    __lowerCamelCase  =		kwargs["""subtask"""]
             if "threshold" in kwargs:
                    __lowerCamelCase  =		kwargs["""threshold"""]
             if "mask_threshold" in kwargs:
                    __lowerCamelCase  =		kwargs["""mask_threshold"""]
             if "overlap_mask_area_threshold" in kwargs:
                    __lowerCamelCase  =		kwargs["""overlap_mask_area_threshold"""]

             return preprocess_kwargs, {}, postprocess_kwargs
      def __call__(							self:					Dict       ,			UpperCamelCase_:					Dict       ,			**UpperCamelCase_:					List[Any]  ):
             return super().__call__(UpperCamelCase_       ,			**UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					List[str]       ,			UpperCamelCase_:					Optional[Any]       ,			UpperCamelCase_:					int=None  ):
             __lowerCamelCase  =		load_image(UpperCamelCase_  )
             __lowerCamelCase  =		[(image.height, image.width)]
             if self.model.config.__class__.__name__ == "OneFormerConfig":
                    if subtask is None:
                           __lowerCamelCase  =		{}
                    else:
                           __lowerCamelCase  =		{"""task_inputs""": [subtask]}
                    __lowerCamelCase  =		self.image_processor(images=[image]       ,			return_tensors="""pt"""       ,			**UpperCamelCase_  )
                    __lowerCamelCase  =		self.tokenizer(
                        inputs["""task_inputs"""]       ,			padding="""max_length"""       ,			max_length=self.model.config.task_seq_len       ,			return_tensors=self.framework       ,			)["""input_ids"""]
             else:
                    __lowerCamelCase  =		self.image_processor(images=[image]       ,			return_tensors="""pt"""  )
             __lowerCamelCase  =		target_size
             return inputs
      def      lowerCAmelCase__		(							self:					Any       ,			UpperCamelCase_:					int  ):
             __lowerCamelCase  =		model_inputs.pop("""target_size"""  )
             __lowerCamelCase  =		self.model(**UpperCamelCase_  )
             __lowerCamelCase  =		target_size
             return model_outputs




      def      lowerCAmelCase__		(							self:					int       ,			UpperCamelCase_:					Tuple       ,			UpperCamelCase_:					Tuple=None       ,			UpperCamelCase_:					Optional[int]=0.9       ,			UpperCamelCase_:					str=0.5       ,			UpperCamelCase_:					Optional[int]=0.5  ):
             __lowerCamelCase  =		None
             if subtask in {"panoptic", None} and hasattr(self.image_processor       ,			"""post_process_panoptic_segmentation"""  ):
                    __lowerCamelCase  =		self.image_processor.post_process_panoptic_segmentation
             elif subtask in {"instance", None} and hasattr(self.image_processor       ,			"""post_process_instance_segmentation"""  ):
                    __lowerCamelCase  =		self.image_processor.post_process_instance_segmentation

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

                    __lowerCamelCase  =		[]
                    __lowerCamelCase  =		outputs["""segmentation"""]

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

                    __lowerCamelCase  =		[]
                    __lowerCamelCase  =		outputs.numpy()
                    __lowerCamelCase  =		np.unique(UpperCamelCase_  )

                    for label in labels:
                           __lowerCamelCase  =		(segmentation == label) * 2_55
                           __lowerCamelCase  =		Image.fromarray(mask.astype(np.uinta  )       ,			mode="""L"""  )
                           __lowerCamelCase  =		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


UpperCAmelCase_   							=      0.5




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Union[str, Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		[""".""", """,""", """;""", """:"""]

       for token in remove_tokens:
              __lowerCamelCase  =		ground_truth.replace(A__    ,     """"""      )
              __lowerCamelCase  =		prediction.replace(A__    ,     """"""      )

       __lowerCamelCase,  __lowerCamelCase  =		ground_truth.lower(), prediction.lower()
       __lowerCamelCase,  __lowerCamelCase  =		ground_truth.replace("""/"""    ,     """ """      ), prediction.replace("""/"""    ,     """ """      )
       __lowerCamelCase,  __lowerCamelCase  =		set(ground_truth.split(""" """      )      ), set(prediction.split(""" """      )      )

       __lowerCamelCase  =		ground_truth.intersection(A__      )
       __lowerCamelCase  =		ground_truth.union(A__      )
       __lowerCamelCase  =		len(A__      ) / len(A__      )
       return jaccard




def    lowerCamelCase__			(		A__			:   Optional[Any]      ):





       '''simple docstring'''



       def remove_articles(A__			:   Tuple      ):
              return re.sub(R"""\b(a|an|the)\b"""    ,     """ """    ,     A__      )

       def white_space_fix(A__			:   Optional[Any]      ):
              return " ".join(text.split()      )

       def remove_punc(A__			:   Optional[Any]      ):
              __lowerCamelCase  =		set(string.punctuation      )
              return "".join(ch for ch in text if ch not in exclude      )

       def lower(A__			:   Tuple      ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(A__      )      )      )      )




def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Optional[Any]    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		0, 0, 0

       __lowerCamelCase  =		"""Parties""" in qa_id

       # first check if ground truth is empty
       if len(A__      ) == 0:
              if len(A__      ) > 0:
                     fp += len(A__      )  # false positive for each one
       else:
              for ground_truth in ground_truths:
                     assert len(A__      ) > 0
                     # check if there is a match
                     __lowerCamelCase  =		False
                     for pred in predictions:
                            if substr_ok:
                                   __lowerCamelCase  =		get_jaccard(A__    ,     A__      ) >= IOU_THRESH or ground_truth in pred
                            else:
                                   __lowerCamelCase  =		get_jaccard(A__    ,     A__      ) >= IOU_THRESH
                            if is_match:
                                   __lowerCamelCase  =		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.)
                     __lowerCamelCase  =		False
                     for ground_truth in ground_truths:
                            assert len(A__      ) > 0
                            if substr_ok:
                                   __lowerCamelCase  =		get_jaccard(A__    ,     A__      ) >= IOU_THRESH or ground_truth in pred
                            else:
                                   __lowerCamelCase  =		get_jaccard(A__    ,     A__      ) >= IOU_THRESH
                            if is_match:
                                   __lowerCamelCase  =		True

                     if not match_found:
                            fp += 1

       __lowerCamelCase  =		tp / (tp + fp) if tp + fp > 0 else np.nan
       __lowerCamelCase  =		tp / (tp + fn) if tp + fn > 0 else np.nan

       return precision, recall




def    lowerCamelCase__			(		A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		precisions[::-1]
       for i in range(1    ,     len(A__      )      ):
              __lowerCamelCase  =		max(precision_best[i - 1]    ,     precision_best[i]      )
       __lowerCamelCase  =		precision_best[::-1]
       return precisions




def    lowerCamelCase__			(		A__			:   str    ,     A__			:   Union[str, Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		process_precisions(A__      )
       __lowerCamelCase  =		np.trapz(A__    ,     A__      )
       if np.isnan(A__      ):
              return 0
       return aupr




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   List[str]    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		process_precisions(A__      )
       __lowerCamelCase  =		0
       for prec, recall in zip(A__    ,     A__      ):
              if recall >= recall_thresh:
                     __lowerCamelCase  =		prec
                     break
       return prec_at_recall




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   List[Any]      ):





       '''simple docstring'''



       return normalize_answer(A__      ) == normalize_answer(A__      )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   int    ,     A__			:   Dict      ):





       '''simple docstring'''



       __lowerCamelCase  =		0
       for pred in predictions:
              for ground_truth in ground_truths:
                     __lowerCamelCase  =		metric_fn(A__    ,     A__      )
                     if score == 1:  # break the loop when one prediction matches the ground truth
                            break
              if score == 1:
                     break
       return score




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		__lowerCamelCase  =		__lowerCamelCase  =		0
       __lowerCamelCase  =		[]
       __lowerCamelCase  =		[]
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   __lowerCamelCase  =		"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
                                   print(A__    ,     file=sys.stderr      )
                                   continue
                            __lowerCamelCase  =		[x["""text"""] for x in qa["""answers"""]]
                            __lowerCamelCase  =		predictions[qa["""id"""]]
                            __lowerCamelCase,  __lowerCamelCase  =		compute_precision_recall(A__    ,     A__    ,     qa["""id"""]      )

                            precisions.append(A__      )
                            recalls.append(A__      )

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

                            exact_match += metric_max_over_ground_truths(A__    ,     A__    ,     A__      )

       __lowerCamelCase  =		[x for _, x in sorted(zip(A__    ,     A__      )      )]
       recalls.sort()

       __lowerCamelCase  =		100.0 * fa / total
       __lowerCamelCase  =		100.0 * exact_match / total
       __lowerCamelCase  =		get_aupr(A__    ,     A__      )

       __lowerCamelCase  =		get_prec_at_recall(A__    ,     A__    ,     recall_thresh=0.9      )
       __lowerCamelCase  =		get_prec_at_recall(A__    ,     A__    ,     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__":
       UpperCAmelCase_   							=      argparse.ArgumentParser(description='Evaluation for CUAD')
       parser.add_argument('dataset_file', help='Dataset file')
       parser.add_argument('prediction_file', help='Prediction File')
       UpperCAmelCase_   							=      parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              UpperCAmelCase_   							=      json.load(dataset_file)
              UpperCAmelCase_   							=      dataset_json['data']
       with open(args.prediction_file) as prediction_file:
              UpperCAmelCase_   							=      json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))

