





from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather





@overload
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Tuple:







			'''simple docstring'''

			...





@overload
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			...





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    int:







			'''simple docstring'''

			UpperCAmelCase       		=    aatype == rc.restype_order['''G''']
			UpperCAmelCase       		=    rc.atom_order['''CA''']
			UpperCAmelCase       		=    rc.atom_order['''CB''']
			UpperCAmelCase       		=    torch.where(
			    is_gly[..., None].expand(*((-1,) * len(is_gly.shape			))      ,      3			)      ,      all_atom_positions[..., ca_idx, :]      ,      all_atom_positions[..., cb_idx, :]      ,      )

			if all_atom_masks is not None:
						UpperCAmelCase       		=    torch.where(
						    UpperCamelCase__      ,      all_atom_masks[..., ca_idx]      ,      all_atom_masks[..., cb_idx]      ,      )
						return pseudo_beta, pseudo_beta_mask
			else:
						return pseudo_beta





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    Dict:







			'''simple docstring'''

			UpperCAmelCase       		=    batched_gather(
			    UpperCamelCase__      ,      batch['''residx_atom37_to_atom14''']      ,      dim=-2      ,      no_batch_dims=len(atomaa.shape[:-2]			)      ,      )

			UpperCAmelCase       		=    atomaa_data * batch['''atom37_atom_exists'''][..., None]

			return atomaa_data





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    template_feats['''template_aatype''']
			UpperCAmelCase       		=    template_feats['''template_torsion_angles_sin_cos''']
			UpperCAmelCase       		=    template_feats['''template_alt_torsion_angles_sin_cos''']
			UpperCAmelCase       		=    template_feats['''template_torsion_angles_mask''']
			UpperCAmelCase       		=    torch.cat(
			    [
			        nn.functional.one_hot(UpperCamelCase__      ,      22			),
			        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]      ,      14			),
			        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]      ,      14			),
			        torsion_angles_mask,
			    ]      ,      dim=-1      ,      )

			return template_angle_feat





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__ = False      ,      UpperCamelCase__ = 1E-20      ,      UpperCamelCase__ = 1E8      ,      )  ->    Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    batch['''template_pseudo_beta_mask''']
			UpperCAmelCase       		=    template_mask[..., None] * template_mask[..., None, :]

			# Compute distogram (this seems to differ slightly from Alg. 5)
			UpperCAmelCase       		=    batch['''template_pseudo_beta''']
			UpperCAmelCase       		=    torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2      ,      dim=-1      ,      keepdim=UpperCamelCase__			)
			UpperCAmelCase       		=    torch.linspace(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      device=tpb.device			) ** 2
			UpperCAmelCase       		=    torch.cat([lower[1:], lower.new_tensor([inf]			)]      ,      dim=-1			)
			UpperCAmelCase       		=    ((dgram > lower) * (dgram < upper)).type(dgram.dtype			)

			UpperCAmelCase       		=    [dgram, template_mask_ad[..., None]]

			UpperCAmelCase       		=    nn.functional.one_hot(
			    batch['''template_aatype''']      ,      rc.restype_num + 2      ,      )

			UpperCAmelCase       		=    batch['''template_aatype'''].shape[-1]
			to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]      ,      UpperCamelCase__      ,      -1      ,      -1			)			)
			to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]      ,      -1      ,      UpperCamelCase__      ,      -1			)			)

			UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
			UpperCAmelCase       		=    Rigid.make_transform_from_reference(
			    n_xyz=batch['''template_all_atom_positions'''][..., n, :]      ,      ca_xyz=batch['''template_all_atom_positions'''][..., ca, :]      ,      c_xyz=batch['''template_all_atom_positions'''][..., c, :]      ,      eps=UpperCamelCase__      ,      )
			UpperCAmelCase       		=    rigids.get_trans()[..., None, :, :]
			UpperCAmelCase       		=    rigids[..., None].invert_apply(UpperCamelCase__			)

			UpperCAmelCase       		=    torch.rsqrt(eps + torch.sum(rigid_vec**2      ,      dim=-1			)			)

			UpperCAmelCase       		=    batch['''template_all_atom_mask''']
			UpperCAmelCase       		=    t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
			UpperCAmelCase       		=    template_mask[..., None] * template_mask[..., None, :]

			UpperCAmelCase       		=    inv_distance_scalar * template_mask_ad
			UpperCAmelCase       		=    rigid_vec * inv_distance_scalar[..., None]

			if not use_unit_vector:
						UpperCAmelCase       		=    unit_vector * 0.0

			to_concat.extend(torch.unbind(unit_vector[..., None, :]      ,      dim=-1			)			)
			to_concat.append(template_mask_ad[..., None]			)

			UpperCAmelCase       		=    torch.cat(UpperCamelCase__      ,      dim=-1			)
			UpperCAmelCase       		=    act * template_mask_ad[..., None]

			return act





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Dict:







			'''simple docstring'''

			UpperCAmelCase       		=    nn.functional.one_hot(batch['''extra_msa''']      ,      23			)
			UpperCAmelCase       		=    [
			    msa_ahot,
			    batch['''extra_has_deletion'''].unsqueeze(-1			),
			    batch['''extra_deletion_value'''].unsqueeze(-1			),
			]
			return torch.cat(UpperCamelCase__      ,      dim=-1			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      )  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    rrgdf[aatype, ...]

			# [*, N, 8] transformations, i.e.
			#   One [*, N, 8, 3, 3] rotation matrix and
			#   One [*, N, 8, 3]    translation matrix
			UpperCAmelCase       		=    r.from_tensor_axa(UpperCamelCase__			)

			UpperCAmelCase       		=    alpha.new_zeros((*((1,) * len(alpha.shape[:-1]			)), 2)			)
			UpperCAmelCase       		=    1

			# [*, N, 8, 2]
			UpperCAmelCase       		=    torch.cat([bb_rot.expand(*alpha.shape[:-2]      ,      -1      ,      -1			), alpha]      ,      dim=-2			)

			# [*, N, 8, 3, 3]
			# Produces rotation matrices of the form:
			# [
			#   [1, 0  , 0  ],
			#   [0, a_2,-a_1],
			#   [0, a_1, a_2]
			# ]
			# This follows the original code rather than the supplement, which uses
			# different indices.

			UpperCAmelCase       		=    alpha.new_zeros(default_r.get_rots().get_rot_mats().shape			)
			UpperCAmelCase       		=    1
			UpperCAmelCase       		=    alpha[..., 1]
			UpperCAmelCase       		=    -alpha[..., 0]
			UpperCAmelCase       		=    alpha

			UpperCAmelCase       		=    default_r.compose(Rigid(Rotation(rot_mats=UpperCamelCase__			)      ,      UpperCamelCase__			)			)

			UpperCAmelCase       		=    all_frames[..., 5]
			UpperCAmelCase       		=    all_frames[..., 6]
			UpperCAmelCase       		=    all_frames[..., 7]

			UpperCAmelCase       		=    all_frames[..., 4]
			UpperCAmelCase       		=    chia_frame_to_bb.compose(UpperCamelCase__			)
			UpperCAmelCase       		=    chia_frame_to_bb.compose(UpperCamelCase__			)
			UpperCAmelCase       		=    chia_frame_to_bb.compose(UpperCamelCase__			)

			UpperCAmelCase       		=    Rigid.cat(
			    [
			        all_frames[..., :5],
			        chia_frame_to_bb.unsqueeze(-1			),
			        chia_frame_to_bb.unsqueeze(-1			),
			        chia_frame_to_bb.unsqueeze(-1			),
			    ]      ,      dim=-1      ,      )

			UpperCAmelCase       		=    r[..., None].compose(UpperCamelCase__			)

			return all_frames_to_global






def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      )  ->    int:







			'''simple docstring'''

			UpperCAmelCase       		=    group_idx[aatype, ...]

			# [*, N, 14, 8]
			UpperCAmelCase       		=    nn.functional.one_hot(
			    UpperCamelCase__      ,      num_classes=default_frames.shape[-3]      ,      )

			# [*, N, 14, 8]
			UpperCAmelCase       		=    r[..., None, :] * group_mask_one_hot

			# [*, N, 14]
			UpperCAmelCase       		=    t_atoms_to_global.map_tensor_fn(lambda UpperCamelCase__			: torch.sum(UpperCamelCase__      ,      dim=-1			)			)

			# [*, N, 14, 1]
			UpperCAmelCase       		=    atom_mask[aatype, ...].unsqueeze(-1			)

			# [*, N, 14, 3]
			UpperCAmelCase       		=    lit_positions[aatype, ...]
			UpperCAmelCase       		=    t_atoms_to_global.apply(UpperCamelCase__			)
			UpperCAmelCase       		=    pred_positions * atom_mask

			return pred_positions








import argparse
import json
import re
import string
import sys

import numpy as np


__A    :   Optional[Any]          =       0.5





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    ['''.''', ''',''', ''';''', ''':''']

			for token in remove_tokens:
						UpperCAmelCase       		=    ground_truth.replace(UpperCamelCase__      ,      ''''''			)
						UpperCAmelCase       		=    prediction.replace(UpperCamelCase__      ,      ''''''			)

			UpperCAmelCase		,      UpperCAmelCase       		=    ground_truth.lower(), prediction.lower()
			UpperCAmelCase		,      UpperCAmelCase       		=    ground_truth.replace('''/'''      ,      ''' '''			), prediction.replace('''/'''      ,      ''' '''			)
			UpperCAmelCase		,      UpperCAmelCase       		=    set(ground_truth.split(''' '''			)			), set(prediction.split(''' '''			)			)

			UpperCAmelCase       		=    ground_truth.intersection(UpperCamelCase__			)
			UpperCAmelCase       		=    ground_truth.union(UpperCamelCase__			)
			UpperCAmelCase       		=    len(UpperCamelCase__			) / len(UpperCamelCase__			)
			return jaccard





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

			def remove_articles(UpperCamelCase__			):
						return re.sub(R'''\b(a|an|the)\b'''      ,      ''' '''      ,      UpperCamelCase__			)

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__			)			)			)			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

			UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    0, 0, 0

			UpperCAmelCase       		=    '''Parties''' in qa_id

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

									if not match_found:
												fp += 1

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

			return precision, recall





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    process_precisions(UpperCamelCase__			)
			UpperCAmelCase       		=    np.trapz(UpperCamelCase__      ,      UpperCamelCase__			)
			if np.isnan(UpperCamelCase__			):
						return 0
			return aupr





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			return normalize_answer(UpperCamelCase__			) == normalize_answer(UpperCamelCase__			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    int:







			'''simple docstring'''

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

												precisions.append(UpperCamelCase__			)
												recalls.append(UpperCamelCase__			)

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

												exact_match += metric_max_over_ground_truths(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)

			UpperCAmelCase       		=    [x for _, x in sorted(zip(UpperCamelCase__      ,      UpperCamelCase__			)			)]
			recalls.sort()

			UpperCAmelCase       		=    100.0 * fa / total
			UpperCAmelCase       		=    100.0 * exact_match / total
			UpperCAmelCase       		=    get_aupr(UpperCamelCase__      ,      UpperCamelCase__			)

			UpperCAmelCase       		=    get_prec_at_recall(UpperCamelCase__      ,      UpperCamelCase__      ,      recall_thresh=0.9			)
			UpperCAmelCase       		=    get_prec_at_recall(UpperCamelCase__      ,      UpperCamelCase__      ,      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__":
				__A    :   Dict          =       argparse.ArgumentParser(description="Evaluation for CUAD")
				parser.add_argument("dataset_file", help="Dataset file")
				parser.add_argument("prediction_file", help="Prediction File")
				__A    :   Any          =       parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__A    :   Any          =       json.load(dataset_file)
								__A    :   Optional[int]          =       dataset_json["data"]
				with open(args.prediction_file) as prediction_file:
								__A    :   int          =       json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))



