





import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available







def    UpperCAmelCase				(  _lowerCamelCase   :	int      , _lowerCamelCase   :	List[Any]      , _lowerCamelCase   :	int      , _lowerCamelCase   :	int=None  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[int] =       None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype  ) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												SCREAMING_SNAKE_CASE__  :					Any =       weight.view(torch.intaa  )
												SCREAMING_SNAKE_CASE__  :					int =       "bfloat16"
						SCREAMING_SNAKE_CASE__  :					List[Any] =       weight.cpu().numpy()
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       os.path.join(_lowerCamelCase      , f"""{weight_name}.dat"""  )
						if index is not None:
												if dtype is None:
																		SCREAMING_SNAKE_CASE__  :					Dict =       str(array.dtype  )
												SCREAMING_SNAKE_CASE__  :					List[Any] =       {"dtype": dtype, "shape": list(array.shape  )}
						if array.ndim == 0:
												SCREAMING_SNAKE_CASE__  :					Dict =       array[None]
						SCREAMING_SNAKE_CASE__  :					List[Any] =       np.memmap(_lowerCamelCase      , dtype=array.dtype      , mode="w+"      , shape=array.shape  )
						SCREAMING_SNAKE_CASE__  :					int =       array[:]
						file_array.flush()
						return index







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





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tuple(weight_info["shape"]  )
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												SCREAMING_SNAKE_CASE__  :					List[str] =       (1,)

						SCREAMING_SNAKE_CASE__  :					Any =       weight_info["dtype"]
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												SCREAMING_SNAKE_CASE__  :					List[Any] =       "int16"

						SCREAMING_SNAKE_CASE__  :					str =       np.memmap(_lowerCamelCase      , dtype=_lowerCamelCase      , shape=_lowerCamelCase      , mode="r"  )

						if len(weight_info["shape"]  ) == 0:
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       weight[0]
						SCREAMING_SNAKE_CASE__  :					List[Any] =       torch.tensor(_lowerCamelCase  )
						if weight_info["dtype"] == "bfloat16":
												SCREAMING_SNAKE_CASE__  :					int =       weight.view(torch.bfloataa  )

						return weight







def    UpperCAmelCase				(  _lowerCamelCase   :	List[str]      , _lowerCamelCase   :	Dict  ):





						'''simple docstring'''


						if index is None or len(_lowerCamelCase  ) == 0:
												# Nothing to save
												return

						SCREAMING_SNAKE_CASE__  :					str =       os.path.join(_lowerCamelCase      , "index.json"  )
						if os.path.isfile(_lowerCamelCase  ):
												with open(_lowerCamelCase      , "r"      , encoding="utf-8"  ) as f:
																		SCREAMING_SNAKE_CASE__  :					str =       json.load(_lowerCamelCase  )
						else:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       {}
						current_index.update(_lowerCamelCase  )

						with open(_lowerCamelCase      , "w"      , encoding="utf-8"  ) as f:
												json.dump(_lowerCamelCase      , _lowerCamelCase      , indent=2  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Union[str, os.PathLike]      , _lowerCamelCase   :	Dict[str, torch.Tensor]  ):





						'''simple docstring'''


						os.makedirs(_lowerCamelCase      , exist_ok=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       {}
						for name, parameter in state_dict.items():
												SCREAMING_SNAKE_CASE__  :					Optional[Any] =       offload_weight(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase      , index=_lowerCamelCase  )

						# Update index
						save_offload_index(_lowerCamelCase      , _lowerCamelCase  )



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Tuple				,						a					:  Mapping				,						a					:  str     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					int =       dataset
													SCREAMING_SNAKE_CASE__  :					str =       prefix

							def __getitem__(   self					:  Optional[Any]				,						a					:  Tuple     )						->List[str]:
													return self.dataset[f"""{self.prefix}{key}"""]

							def __iter__(   self					:  Dict     )						->Any:
													return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )






							def __len__(   self					:  Optional[Any]     )						->List[str]:
													return len(self.dataset     )







class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  int				,						a					:  Dict[str, torch.Tensor] = None				,						a					:  Optional[Union[str, os.PathLike]] = None				,						a					:  Mapping = None				,						a					:  Optional[int]=None				,						)						->List[str]:
													if state_dict is None and save_folder is None:
																			raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights."     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       {} if state_dict is None else state_dict
													SCREAMING_SNAKE_CASE__  :					List[Any] =       save_folder
													if index is None and save_folder is not None:
																			with open(os.path.join(a				,						"index.json"     )     ) as f:
																									SCREAMING_SNAKE_CASE__  :					Tuple =       json.load(a     )
													SCREAMING_SNAKE_CASE__  :					Any =       {} if index is None else index
													SCREAMING_SNAKE_CASE__  :					List[Any] =       list(self.state_dict.keys()     )
													self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       device

							def __getitem__(   self					:  Dict				,						a					:  str     )						->Tuple:
													# State dict gets priority
													if key in self.state_dict:
																			return self.state_dict[key]
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.index[key]
													if weight_info.get("safetensors_file"     ) is not None:
																			if not is_safetensors_available():
																									raise ImportError("These offloaded weights require the use of safetensors: `pip install safetensors`."     )

																			from safetensors import safe_open

																			SCREAMING_SNAKE_CASE__  :					int =       "cpu" if self.device is None else self.device
																			with safe_open(weight_info["safetensors_file"]				,						framework="pt"				,						device=a     ) as f:
																									SCREAMING_SNAKE_CASE__  :					str =       f.get_tensor(weight_info.get("weight_name"				,						a     )     )

																			if "dtype" in weight_info:
																									return tensor.to(getattr(a				,						weight_info["dtype"]     )     )
																			else:
																									return tensor

													SCREAMING_SNAKE_CASE__  :					Tuple =       os.path.join(self.save_folder				,						f"""{key}.dat"""     )
													return load_offloaded_weight(a				,						a     )

							def __iter__(   self					:  List[Any]     )						->Optional[int]:
													return iter(self.all_keys     )






							def __len__(   self					:  List[str]     )						->Optional[Any]:
													return len(self.all_keys     )







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict[str, torch.Tensor]      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + "."  )
												    }  )
						return result






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)))