






'''simple docstring'''


# Imports
import numpy as np
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						Union[str, Any] ,      lowercase							:						List[Any]=None ,      lowercase							:						Tuple=None ,      lowercase							:						Any=None ,      lowercase							:						str=None ,      lowercase							:						Optional[Any]=None			)							->    Optional[Any]:

									'''simple docstring'''







									self.set_matricies(red=lowercase ,      green=lowercase ,      blue=lowercase ,      red_edge=lowercase ,      nir=lowercase			)






			def 				A      (   self							:						Tuple ,      lowercase							:						Union[str, Any]=None ,      lowercase							:						Union[str, Any]=None ,      lowercase							:						List[Any]=None ,      lowercase							:						List[Any]=None ,      lowercase							:						Optional[int]=None			)							->    int:

									'''simple docstring'''







									if red is not None:
															UpperCamelCase__         = red
									if green is not None:
															UpperCamelCase__         = green
									if blue is not None:
															UpperCamelCase__         = blue
									if red_edge is not None:
															UpperCamelCase__         = red_edge
									if nir is not None:
															UpperCamelCase__         = nir
									return True






			def 				A      (   self							:						Dict ,      lowercase							:						List[Any]="" ,      lowercase							:						int=None ,      lowercase							:						List[Any]=None ,      lowercase							:						Dict=None ,      lowercase							:						Dict=None ,      lowercase							:						Tuple=None			)							->    Optional[Any]:

									'''simple docstring'''







									self.set_matricies(red=lowercase ,      green=lowercase ,      blue=lowercase ,      red_edge=lowercase ,      nir=lowercase			)
									UpperCamelCase__         = {
									    """ARVI2""": self.arvaa,
									    """CCCI""": self.ccci,
									    """CVI""": self.cvi,
									    """GLI""": self.gli,
									    """NDVI""": self.ndvi,
									    """BNDVI""": self.bndvi,
									    """redEdgeNDVI""": self.red_edge_ndvi,
									    """GNDVI""": self.gndvi,
									    """GBNDVI""": self.gbndvi,
									    """GRNDVI""": self.grndvi,
									    """RBNDVI""": self.rbndvi,
									    """PNDVI""": self.pndvi,
									    """ATSAVI""": self.atsavi,
									    """BWDRVI""": self.bwdrvi,
									    """CIgreen""": self.ci_green,
									    """CIrededge""": self.ci_rededge,
									    """CI""": self.ci,
									    """CTVI""": self.ctvi,
									    """GDVI""": self.gdvi,
									    """EVI""": self.evi,
									    """GEMI""": self.gemi,
									    """GOSAVI""": self.gosavi,
									    """GSAVI""": self.gsavi,
									    """Hue""": self.hue,
									    """IVI""": self.ivi,
									    """IPVI""": self.ipvi,
									    """I""": self.i,
									    """RVI""": self.rvi,
									    """MRVI""": self.mrvi,
									    """MSAVI""": self.m_savi,
									    """NormG""": self.norm_g,
									    """NormNIR""": self.norm_nir,
									    """NormR""": self.norm_r,
									    """NGRDI""": self.ngrdi,
									    """RI""": self.ri,
									    """S""": self.s,
									    """IF""": self._if,
									    """DVI""": self.dvi,
									    """TVI""": self.tvi,
									    """NDRE""": self.ndre,
									}

									try:
															return funcs[index]()
									except KeyError:
															print("""Index not in the list!"""			)
															return False






			def 				A      (   self							:						Any			)							->    Optional[int]:

									'''simple docstring'''







									return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))






			def 				A      (   self							:						Dict			)							->    List[Any]:

									'''simple docstring'''







									return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
									    (self.nir - self.red) / (self.nir + self.red)
									)






			def 				A      (   self							:						int			)							->    Union[str, Any]:

									'''simple docstring'''







									return self.nir * (self.red / (self.green**2))






			def 				A      (   self							:						Union[str, Any]			)							->    Any:

									'''simple docstring'''







									return (2 * self.green - self.red - self.blue) / (
									    2 * self.green + self.red + self.blue
									)






			def 				A      (   self							:						Tuple			)							->    str:

									'''simple docstring'''







									return (self.nir - self.red) / (self.nir + self.red)






			def 				A      (   self							:						Tuple			)							->    Optional[Any]:

									'''simple docstring'''







									return (self.nir - self.blue) / (self.nir + self.blue)






			def 				A      (   self							:						str			)							->    Union[str, Any]:

									'''simple docstring'''







									return (self.redEdge - self.red) / (self.redEdge + self.red)






			def 				A      (   self							:						int			)							->    List[Any]:

									'''simple docstring'''







									return (self.nir - self.green) / (self.nir + self.green)






			def 				A      (   self							:						Dict			)							->    str:

									'''simple docstring'''







									return (self.nir - (self.green + self.blue)) / (
									    self.nir + (self.green + self.blue)
									)






			def 				A      (   self							:						List[Any]			)							->    Tuple:

									'''simple docstring'''







									return (self.nir - (self.green + self.red)) / (
									    self.nir + (self.green + self.red)
									)






			def 				A      (   self							:						int			)							->    Tuple:

									'''simple docstring'''







									return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))






			def 				A      (   self							:						Optional[int]			)							->    Any:

									'''simple docstring'''







									return (self.nir - (self.green + self.red + self.blue)) / (
									    self.nir + (self.green + self.red + self.blue)
									)






			def 				A      (   self							:						Any ,      lowercase							:						str=0.0_8 ,      lowercase							:						List[Any]=1.2_2 ,      lowercase							:						str=0.0_3			)							->    Optional[Any]:

									'''simple docstring'''







									return a * (
									    (self.nir - a * self.red - b)
									    / (a * self.nir + self.red - a * b + x * (1 + a**2))
									)






			def 				A      (   self							:						int			)							->    Optional[int]:

									'''simple docstring'''







									return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)






			def 				A      (   self							:						Union[str, Any]			)							->    List[str]:

									'''simple docstring'''







									return (self.nir / self.green) - 1






			def 				A      (   self							:						Dict			)							->    Optional[int]:

									'''simple docstring'''







									return (self.nir / self.redEdge) - 1






			def 				A      (   self							:						Dict			)							->    Optional[int]:

									'''simple docstring'''







									return (self.red - self.blue) / self.red






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = self.ndvi()
									return ((ndvi + 0.5) / (abs(ndvi + 0.5			))) * (abs(ndvi + 0.5			) ** (1 / 2))






			def 				A      (   self							:						Tuple			)							->    Union[str, Any]:

									'''simple docstring'''







									return self.nir - self.green






			def 				A      (   self							:						Tuple			)							->    List[str]:

									'''simple docstring'''







									return 2.5 * (
									    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
									)






			def 				A      (   self							:						List[Any]			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
									    self.nir + self.red + 0.5
									)
									return n * (1 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)






			def 				A      (   self							:						Dict ,      lowercase							:						Union[str, Any]=0.1_6			)							->    Any:

									'''simple docstring'''







									return (self.nir - self.green) / (self.nir + self.green + y)






			def 				A      (   self							:						Any ,      lowercase							:						Optional[int]=0.5			)							->    List[Any]:

									'''simple docstring'''







									return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)






			def 				A      (   self							:						Dict			)							->    Optional[Any]:

									'''simple docstring'''







									return np.arctan(
									    ((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue)			)






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

									'''simple docstring'''







									return (self.nir - b) / (a * self.red)






			def 				A      (   self							:						Optional[int]			)							->    Optional[Any]:

									'''simple docstring'''







									return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)






			def 				A      (   self							:						List[str]			)							->    Tuple:

									'''simple docstring'''







									return (self.red + self.green + self.blue) / 3_0.5






			def 				A      (   self							:						Tuple			)							->    int:

									'''simple docstring'''







									return self.nir / self.red






			def 				A      (   self							:						List[Any]			)							->    Tuple:

									'''simple docstring'''







									return (self.rvi() - 1) / (self.rvi() + 1)






			def 				A      (   self							:						Optional[Any]			)							->    Union[str, Any]:

									'''simple docstring'''







									return (
									    (2 * self.nir + 1)
									    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
									) / 2






			def 				A      (   self							:						Optional[Any]			)							->    Tuple:

									'''simple docstring'''







									return self.green / (self.nir + self.red + self.green)






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									return self.nir / (self.nir + self.red + self.green)






			def 				A      (   self							:						Optional[Any]			)							->    Tuple:

									'''simple docstring'''







									return self.red / (self.nir + self.red + self.green)






			def 				A      (   self							:						Tuple			)							->    List[Any]:

									'''simple docstring'''







									return (self.green - self.red) / (self.green + self.red)






			def 				A      (   self							:						int			)							->    List[str]:

									'''simple docstring'''







									return (self.red - self.green) / (self.red + self.green)






			def 				A      (   self							:						int			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = np.max([np.max(self.red			), np.max(self.green			), np.max(self.blue			)]			)
									UpperCamelCase__         = np.min([np.min(self.red			), np.min(self.green			), np.min(self.blue			)]			)
									return (max_value - min_value) / max_value






			def 				A      (   self							:						List[str]			)							->    Tuple:

									'''simple docstring'''







									return (2 * self.red - self.green - self.blue) / (self.green - self.blue)






			def 				A      (   self							:						int			)							->    Dict:

									'''simple docstring'''







									return self.nir / self.red






			def 				A      (   self							:						Dict			)							->    int:

									'''simple docstring'''







									return (self.ndvi() + 0.5) ** (1 / 2)







			def 				A      (   self							:						Optional[Any]			)							->    int:

									'''simple docstring'''







									return (self.nir - self.redEdge) / (self.nir + self.redEdge)








'''simple docstring'''


import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


lowerCamelCase_				: Union[str, Any]    	=							re.compile(r'''\b(a|an|the)\b''', re.UNICODE)

lowerCamelCase_				: int    	=							None







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0."""					)
						parser.add_argument("""data_file"""						,      metavar="""data.json"""						,      help="""Input data JSON file."""					)
						parser.add_argument("""pred_file"""						,      metavar="""pred.json"""						,      help="""Model predictions."""					)
						parser.add_argument(
						    """--out-file"""						,      """-o"""						,      metavar="""eval.json"""						,      help="""Write accuracy metrics to file (default is stdout)."""					)
						parser.add_argument(
						    """--na-prob-file"""						,      """-n"""						,      metavar="""na_prob.json"""						,      help="""Model estimates of probability of no answer."""					)
						parser.add_argument(
						    """--na-prob-thresh"""						,      """-t"""						,      type=_A						,      default=1.0						,      help="""Predict \"\" if no-answer probability exceeds this (default = 1.0)."""						,      )
						parser.add_argument(
						    """--out-image-dir"""						,      """-p"""						,      metavar="""out_images"""						,      default=_A						,      help="""Save precision-recall curves to directory."""					)
						parser.add_argument("""--verbose"""						,      """-v"""						,      action="""store_true"""					)
						if len(sys.argv					) == 1:
												parser.print_help()
												sys.exit(1					)
						return parser.parse_args()







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						for article in dataset:
												for p in article["paragraphs"]:
																		for qa in p["qas"]:
																								UpperCamelCase__         = bool(qa["""answers"""]["""text"""]					)
						return qid_to_has_ans







def 	__magic_name__( _A					):
						'''simple docstring'''





						def remove_articles(_A					):
												return ARTICLES_REGEX.sub(""" """						,      _A					)

						def white_space_fix(_A					):
												return " ".join(text.split()					)

						def remove_punc(_A					):
												UpperCamelCase__         = set(string.punctuation					)
												return "".join(ch for ch in text if ch not in exclude					)

						def lower(_A					):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_A					)					)					)					)







def 	__magic_name__( _A					):
						'''simple docstring'''





						if not s:
												return []
						return normalize_answer(_A					).split()







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						return int(normalize_answer(_A					) == normalize_answer(_A					)					)







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = get_tokens(_A					)
						UpperCamelCase__         = get_tokens(_A					)
						UpperCamelCase__         = collections.Counter(_A					) & collections.Counter(_A					)
						UpperCamelCase__         = sum(common.values()					)
						if len(_A					) == 0 or len(_A					) == 0:
												# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
												return int(gold_toks == pred_toks					)
						if num_same == 0:
												return 0
						UpperCamelCase__         = 1.0 * num_same / len(_A					)
						UpperCamelCase__         = 1.0 * num_same / len(_A					)
						UpperCamelCase__         = (2 * precision * recall) / (precision + recall)
						return fa







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						UpperCamelCase__         = {}
						for article in dataset:
												for p in article["paragraphs"]:
																		for qa in p["qas"]:
																								UpperCamelCase__         = qa["""id"""]
																								UpperCamelCase__         = [t for t in qa["""answers"""]["""text"""] if normalize_answer(_A					)]
																								if not gold_answers:
																														# For unanswerable questions, only correct answer is empty string
																														UpperCamelCase__         = [""""""]
																								if qid not in preds:
																														print(f"Missing prediction for {qid}"					)
																														continue
																								UpperCamelCase__         = preds[qid]
																								# Take max over all gold answers
																								UpperCamelCase__         = max(compute_exact(_A						,      _A					) for a in gold_answers					)
																								UpperCamelCase__         = max(compute_fa(_A						,      _A					) for a in gold_answers					)
						return exact_scores, fa_scores







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						for qid, s in scores.items():
												UpperCamelCase__         = na_probs[qid] > na_prob_thresh
												if pred_na:
																		UpperCamelCase__         = float(not qid_to_has_ans[qid]					)
												else:
																		UpperCamelCase__         = s
						return new_scores







def 	__magic_name__( _A						,      _A						,      _A=None					):
						'''simple docstring'''





						if not qid_list:
												UpperCamelCase__         = len(_A					)
												return collections.OrderedDict(
												    [
												        ("""exact""", 1_0_0.0 * sum(exact_scores.values()					) / total),
												        ("""f1""", 1_0_0.0 * sum(fa_scores.values()					) / total),
												        ("""total""", total),
												    ]					)
						else:
												UpperCamelCase__         = len(_A					)
												return collections.OrderedDict(
												    [
												        ("""exact""", 1_0_0.0 * sum(exact_scores[k] for k in qid_list					) / total),
												        ("""f1""", 1_0_0.0 * sum(fa_scores[k] for k in qid_list					) / total),
												        ("""total""", total),
												    ]					)







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						for k in new_eval:
												UpperCamelCase__         = new_eval[k]







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						plt.step(_A						,      _A						,      color="""b"""						,      alpha=0.2						,      where="""post"""					)
						plt.fill_between(_A						,      _A						,      step="""post"""						,      alpha=0.2						,      color="""b"""					)
						plt.xlabel("""Recall"""					)
						plt.ylabel("""Precision"""					)
						plt.xlim([0.0, 1.0_5]					)
						plt.ylim([0.0, 1.0_5]					)
						plt.title(_A					)
						plt.savefig(_A					)
						plt.clf()







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A=None						,      _A=None					):
						'''simple docstring'''





						UpperCamelCase__         = sorted(_A						,      key=lambda _A					: na_probs[k]					)
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = 1.0
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = [1.0]
						UpperCamelCase__         = [0.0]
						UpperCamelCase__         = 0.0
						for i, qid in enumerate(_A					):
												if qid_to_has_ans[qid]:
																		true_pos += scores[qid]
												UpperCamelCase__         = true_pos / float(i + 1					)
												UpperCamelCase__         = true_pos / float(_A					)
												if i == len(_A					) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
																		# i.e., if we can put a threshold after this point
																		avg_prec += cur_p * (cur_r - recalls[-1])
																		precisions.append(_A					)
																		recalls.append(_A					)
						if out_image:
												plot_pr_curve(_A						,      _A						,      _A						,      _A					)
						return {"ap": 1_0_0.0 * avg_prec}







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						if out_image_dir and not os.path.exists(_A					):
												os.makedirs(_A					)
						UpperCamelCase__         = sum(1 for v in qid_to_has_ans.values() if v					)
						if num_true_pos == 0:
												return
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_exact.png"""					)						,      title="""Precision-Recall curve for Exact Match score"""						,      )
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_f1.png"""					)						,      title="""Precision-Recall curve for F1 score"""						,      )
						UpperCamelCase__         = {k: float(_A					) for k, v in qid_to_has_ans.items()}
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_oracle.png"""					)						,      title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)"""						,      )
						merge_eval(_A						,      _A						,      """pr_exact"""					)
						merge_eval(_A						,      _A						,      """pr_f1"""					)
						merge_eval(_A						,      _A						,      """pr_oracle"""					)







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						if not qid_list:
												return
						UpperCamelCase__         = [na_probs[k] for k in qid_list]
						UpperCamelCase__         = np.ones_like(_A					) / float(len(_A					)					)
						plt.hist(_A						,      weights=_A						,      bins=20						,      range=(0.0, 1.0)					)
						plt.xlabel("""Model probability of no-answer"""					)
						plt.ylabel("""Proportion of dataset"""					)
						plt.title(f"Histogram of no-answer probability: {name}"					)
						plt.savefig(os.path.join(_A						,      f"na_prob_hist_{name}.png"					)					)
						plt.clf()







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]					)
						UpperCamelCase__         = num_no_ans
						UpperCamelCase__         = cur_score
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = sorted(_A						,      key=lambda _A					: na_probs[k]					)
						for i, qid in enumerate(_A					):
												if qid not in scores:
																		continue
												if qid_to_has_ans[qid]:
																		UpperCamelCase__         = scores[qid]
												else:
																		if preds[qid]:
																								UpperCamelCase__         = -1
																		else:
																								UpperCamelCase__         = 0
												cur_score += diff
												if cur_score > best_score:
																		UpperCamelCase__         = cur_score
																		UpperCamelCase__         = na_probs[qid]
						return 1_0_0.0 * best_score / len(_A					), best_thresh







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__    ,   UpperCamelCase__         = find_best_thresh(_A						,      _A						,      _A						,      _A					)
						UpperCamelCase__    ,   UpperCamelCase__         = find_best_thresh(_A						,      _A						,      _A						,      _A					)
						UpperCamelCase__         = best_exact
						UpperCamelCase__         = exact_thresh
						UpperCamelCase__         = best_fa
						UpperCamelCase__         = fa_thresh







def 	__magic_name__( ):
						'''simple docstring'''





						with open(OPTS.data_file					) as f:
												UpperCamelCase__         = json.load(_A					)
												UpperCamelCase__         = dataset_json["""data"""]
						with open(OPTS.pred_file					) as f:
												UpperCamelCase__         = json.load(_A					)
						if OPTS.na_prob_file:
												with open(OPTS.na_prob_file					) as f:
																		UpperCamelCase__         = json.load(_A					)
						else:
												UpperCamelCase__         = {k: 0.0 for k in preds}
						UpperCamelCase__         = make_qid_to_has_ans(_A					)  # maps qid to True/False
						UpperCamelCase__         = [k for k, v in qid_to_has_ans.items() if v]
						UpperCamelCase__         = [k for k, v in qid_to_has_ans.items() if not v]
						UpperCamelCase__    ,   UpperCamelCase__         = get_raw_scores(_A						,      _A					)
						UpperCamelCase__         = apply_no_ans_threshold(_A						,      _A						,      _A						,      OPTS.na_prob_thresh					)
						UpperCamelCase__         = apply_no_ans_threshold(_A						,      _A						,      _A						,      OPTS.na_prob_thresh					)
						UpperCamelCase__         = make_eval_dict(_A						,      _A					)
						if has_ans_qids:
												UpperCamelCase__         = make_eval_dict(_A						,      _A						,      qid_list=_A					)
												merge_eval(_A						,      _A						,      """HasAns"""					)
						if no_ans_qids:
												UpperCamelCase__         = make_eval_dict(_A						,      _A						,      qid_list=_A					)
												merge_eval(_A						,      _A						,      """NoAns"""					)
						if OPTS.na_prob_file:
												find_all_best_thresh(_A						,      _A						,      _A						,      _A						,      _A						,      _A					)
						if OPTS.na_prob_file and OPTS.out_image_dir:
												run_precision_recall_analysis(_A						,      _A						,      _A						,      _A						,      _A						,      OPTS.out_image_dir					)
												histogram_na_prob(_A						,      _A						,      OPTS.out_image_dir						,      """hasAns"""					)
												histogram_na_prob(_A						,      _A						,      OPTS.out_image_dir						,      """noAns"""					)
						if OPTS.out_file:
												with open(OPTS.out_file						,      """w"""					) as f:
																		json.dump(_A						,      _A					)
						else:
												print(json.dumps(_A						,      indent=2					)					)


if __name__ == "__main__":
					lowerCamelCase_				: str    	=							parse_args()
					if OPTS.out_image_dir:
										import matplotlib

										matplotlib.use('''Agg''')
										import matplotlib.pyplot as plt
					main()
