

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase     					=  logging.get_logger(__name__)

__UpperCAmelCase     					=  {
    "facebook/sam-vit-huge": "https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json",
    "facebook/sam-vit-large": "https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json",
    "facebook/sam-vit-base": "https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json",
}





class A__     (			lowercase__			):



 """simple docstring"""







 def __init__(		self			:						List[Any]  ,    A_			:						int=2_5_6  ,    A_			:						Tuple=1_0_2_4  ,    A_			:						Optional[Any]=1_6  ,    A_			:						Dict=1_6  ,    A_			:						str=4  ,    A_			:						Union[str, Any]="gelu"  ,    A_			:						Optional[Any]=1E-6  ,    **A_			:						Dict  ,    ):






      '''simple docstring'''




      super().__init__(**A_							)
      _lowerCAmelCase :       Any	   =     hidden_size
      _lowerCAmelCase :       List[Any]	   =     image_size
      _lowerCAmelCase :       List[str]	   =     patch_size
      _lowerCAmelCase :       str	   =     image_size // patch_size
      _lowerCAmelCase :       Union[str, Any]	   =     mask_input_channels
      _lowerCAmelCase :       Any	   =     num_point_embeddings
      _lowerCAmelCase :       str	   =     hidden_act
      _lowerCAmelCase :       Union[str, Any]	   =     layer_norm_eps





class A__     (			lowercase__			):



 """simple docstring"""







 def __init__(		self			:						Union[str, Any]  ,    A_			:						Optional[int]=2_5_6  ,    A_			:						Dict="relu"  ,    A_			:						Dict=2_0_4_8  ,    A_			:						Union[str, Any]=2  ,    A_			:						str=8  ,    A_			:						str=2  ,    A_			:						List[Any]=3  ,    A_			:						str=3  ,    A_			:						List[str]=2_5_6  ,    A_			:						str=1E-6  ,    **A_			:						Optional[int]  ,    ):






      '''simple docstring'''




      super().__init__(**A_							)
      _lowerCAmelCase :       List[Any]	   =     hidden_size
      _lowerCAmelCase :       List[Any]	   =     hidden_act
      _lowerCAmelCase :       Dict	   =     mlp_dim
      _lowerCAmelCase :       List[Any]	   =     num_hidden_layers
      _lowerCAmelCase :       Union[str, Any]	   =     num_attention_heads
      _lowerCAmelCase :       List[Any]	   =     attention_downsample_rate
      _lowerCAmelCase :       List[str]	   =     num_multimask_outputs
      _lowerCAmelCase :       Optional[int]	   =     iou_head_depth
      _lowerCAmelCase :       Union[str, Any]	   =     iou_head_hidden_dim
      _lowerCAmelCase :       Optional[int]	   =     layer_norm_eps





class A__     (			lowercase__			):



 """simple docstring"""







 def __init__(		self			:						Tuple  ,    A_			:						str=7_6_8  ,    A_			:						Tuple=2_5_6  ,    A_			:						Tuple=1_2  ,    A_			:						List[str]=1_2  ,    A_			:						Optional[Any]=3  ,    A_			:						str=1_0_2_4  ,    A_			:						str=1_6  ,    A_			:						Union[str, Any]="gelu"  ,    A_			:						Optional[int]=1E-06  ,    A_			:						Optional[Any]=0.0  ,    A_			:						str=1E-10  ,    A_			:						Optional[int]=True  ,    A_			:						Dict=4.0  ,    A_			:						Optional[Any]=True  ,    A_			:						List[str]=True  ,    A_			:						str=1_4  ,    A_			:						List[str]=[2, 5, 8, 1_1]  ,    A_			:						int=1_2_8  ,    A_			:						int=None  ,    **A_			:						List[str]  ,    ):






      '''simple docstring'''




      super().__init__(**A_							)

      _lowerCAmelCase :       Tuple	   =     hidden_size
      _lowerCAmelCase :       List[Any]	   =     output_channels
      _lowerCAmelCase :       Optional[int]	   =     num_hidden_layers
      _lowerCAmelCase :       Optional[Any]	   =     num_attention_heads
      _lowerCAmelCase :       List[str]	   =     num_channels
      _lowerCAmelCase :       Optional[int]	   =     image_size
      _lowerCAmelCase :       Optional[Any]	   =     patch_size
      _lowerCAmelCase :       List[str]	   =     hidden_act
      _lowerCAmelCase :       str	   =     layer_norm_eps
      _lowerCAmelCase :       Optional[Any]	   =     attention_dropout
      _lowerCAmelCase :       List[Any]	   =     initializer_range
      _lowerCAmelCase :       Tuple	   =     qkv_bias
      _lowerCAmelCase :       Tuple	   =     mlp_ratio
      _lowerCAmelCase :       Tuple	   =     use_abs_pos
      _lowerCAmelCase :       Dict	   =     use_rel_pos
      _lowerCAmelCase :       Tuple	   =     window_size
      _lowerCAmelCase :       Optional[int]	   =     global_attn_indexes
      _lowerCAmelCase :       Any	   =     num_pos_feats
      _lowerCAmelCase :       int	   =     int(hidden_size * mlp_ratio							) if mlp_dim is None else mlp_dim





class A__     (			lowercase__			):



 """simple docstring"""







 _lowercase  :       Union[str, Any]  			=    '''sam'''
 _lowercase  :       int  			=    True

 def __init__(		self			:						str  ,    A_			:						Optional[int]=None  ,    A_			:						Any=None  ,    A_			:						Any=None  ,    A_			:						str=0.02  ,    **A_			:						int  ,    ):






      '''simple docstring'''




      super().__init__(**A_							)
      _lowerCAmelCase :       Dict	   =     vision_config if vision_config is not None else {}
      _lowerCAmelCase :       str	   =     prompt_encoder_config if prompt_encoder_config is not None else {}
      _lowerCAmelCase :       Tuple	   =     mask_decoder_config if mask_decoder_config is not None else {}

      if isinstance(A_  ,    A_							):
           _lowerCAmelCase :       Tuple	   =     vision_config.to_dict()
      if isinstance(A_  ,    A_							):
           _lowerCAmelCase :       Dict	   =     prompt_encoder_config.to_dict()
      if isinstance(A_  ,    A_							):
           _lowerCAmelCase :       List[Any]	   =     mask_decoder_config.to_dict()

      _lowerCAmelCase :       str	   =     SamVisionConfig(**A_							)
      _lowerCAmelCase :       str	   =     SamPromptEncoderConfig(**A_							)
      _lowerCAmelCase :       int	   =     SamMaskDecoderConfig(**A_							)
      _lowerCAmelCase :       List[str]	   =     initializer_range


 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     copy.deepcopy(self.__dict__							)
      _lowerCAmelCase :       str	   =     self.vision_config.to_dict()
      _lowerCAmelCase :       Union[str, Any]	   =     self.prompt_encoder_config.to_dict()
      _lowerCAmelCase :       Optional[Any]	   =     self.mask_decoder_config.to_dict()
      _lowerCAmelCase :       Tuple	   =     self.__class__.model_type
      return output













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)))