'''simple docstring'''

import argparse
import json
import re
import string
import sys
from collections import Counter


def        UpperCAmelCase__   (				_lowerCAmelCase    :			List[Any])    ->		List[Any]:



						"""simple docstring"""

						def remove_articles(_lowerCAmelCase    :			Optional[int]):
												return re.sub(r"\b(a|an|the)\b",      " ",      _lowerCAmelCase)

						def white_space_fix(_lowerCAmelCase    :			Optional[int]):
												return " ".join(text.split())

						def remove_punc(_lowerCAmelCase    :			int):
												snake_case :       List[Any]				=    set(string.punctuation)
												return "".join(ch for ch in text if ch not in exclude)

						def lower(_lowerCAmelCase    :			Union[str, Any]):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase))))


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			List[Any])    ->		List[str]:



						"""simple docstring"""

						snake_case :       Any				=    normalize_answer(_lowerCAmelCase).split()
						snake_case :       Dict				=    normalize_answer(_lowerCAmelCase).split()
						snake_case :       Optional[Any]				=    Counter(_lowerCAmelCase) & Counter(_lowerCAmelCase)
						snake_case :       Union[str, Any]				=    sum(common.values())
						if num_same == 0:
												return 0
						snake_case :       List[Any]				=    1.0 * num_same / len(_lowerCAmelCase)
						snake_case :       Dict				=    1.0 * num_same / len(_lowerCAmelCase)
						snake_case :       Union[str, Any]				=    (2 * precision * recall) / (precision + recall)
						return fa


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Dict,      _lowerCAmelCase    :			Any)    ->		List[str]:



						"""simple docstring"""

						return normalize_answer(_lowerCAmelCase) == normalize_answer(_lowerCAmelCase)


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Optional[int],      _lowerCAmelCase    :			int,      _lowerCAmelCase    :			Any)    ->		Dict:



						"""simple docstring"""

						snake_case :       str				=    []
						for ground_truth in ground_truths:
												snake_case :       List[str]				=    metric_fn(_lowerCAmelCase,      _lowerCAmelCase)
												scores_for_ground_truths.append(_lowerCAmelCase)
						return max(_lowerCAmelCase)


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Tuple,      _lowerCAmelCase    :			int)    ->		Tuple:



						"""simple docstring"""

						snake_case :       Any				=    0
						snake_case :       Union[str, Any]				=    []
						for passage in dataset:
												for qa in passage["qas"]:
																		total += 1
																		if qa["id"] not in predictions:
																								snake_case :       Any				=    f"""Unanswered question {qa["id"]} will receive score 0."""
																								print(_lowerCAmelCase,      file=sys.stderr)
																								continue

																		snake_case :       Optional[Any]				=    [x["text"] for x in qa["answers"]]
																		snake_case :       int				=    predictions[qa["id"]]

																		snake_case :       int				=    metric_max_over_ground_truths(_lowerCAmelCase,      _lowerCAmelCase,      _lowerCAmelCase)
																		if int(_exact_match) == 1:
																								correct_ids.append(qa["id"])
																		exact_match += _exact_match

																		fa += metric_max_over_ground_truths(_lowerCAmelCase,      _lowerCAmelCase,      _lowerCAmelCase)

						snake_case :       List[Any]				=    exact_match / total
						snake_case :       Union[str, Any]				=    fa / total

						return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
						A_	   = "1.0"
						A_	   = argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
						parser.add_argument("data_file", help="The dataset file in JSON format.")
						parser.add_argument("pred_file", help="The model prediction file in JSON format.")
						parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
						A_	   = parser.parse_args()

						with open(args.data_file) as data_file:
												A_	   = json.load(data_file)
												if dataset_json["version"] != expected_version:
																		print(
																		    f"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
																		    file=sys.stderr,
																		)
												A_	   = dataset_json["data"]

						with open(args.pred_file) as pred_file:
												A_	   = json.load(pred_file)

						A_   , A_	   = evaluate(dataset, predictions)

						if args.output_correct_ids:
												print(f"Output {len(correct_ids)} correctly answered question IDs.")
												with open("correct_ids.json", "w") as f:
																		json.dump(correct_ids, f)






'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=sys.maxsize					):
									snake_case :       Any				=    "bilinear"
									snake_case :       Dict				=    max_size
									snake_case :       Dict				=    short_edge_length






			def __call__(							self				,			snake_case_					):
									snake_case :       Optional[Any]				=    []
									for img in imgs:
															snake_case				,					snake_case :       Dict				=    img.shape[:2]
															# later: provide list and randomly choose index for resize
															snake_case :       Dict				=    np.random.randint(self.short_edge_length[0]				,			self.short_edge_length[1] + 1					)
															if size == 0:
																					return img
															snake_case :       Any				=    size * 1.0 / min(snake_case_				,			snake_case_					)
															if h < w:
																					snake_case				,					snake_case :       Optional[int]				=    size, scale * w
															else:
																					snake_case				,					snake_case :       Tuple				=    scale * h, size
															if max(snake_case_				,			snake_case_					) > self.max_size:
																					snake_case :       Optional[int]				=    self.max_size * 1.0 / max(snake_case_				,			snake_case_					)
																					snake_case :       Dict				=    newh * scale
																					snake_case :       List[Any]				=    neww * scale
															snake_case :       int				=    int(neww + 0.5					)
															snake_case :       List[Any]				=    int(newh + 0.5					)

															if img.dtype == np.uinta:
																					snake_case :       List[Any]				=    Image.fromarray(snake_case_					)
																					snake_case :       str				=    pil_image.resize((neww, newh)				,			PILImageResampling.BILINEAR					)
																					snake_case :       List[str]				=    np.asarray(snake_case_					)
															else:
																					snake_case :       List[Any]				=    img.permute(2				,			0				,			1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																					snake_case :       Tuple				=    nn.functional.interpolate(
																					    snake_case_				,			(newh, neww)				,			mode=self.interp_method				,			align_corners=snake_case_					).squeeze(0					)
															img_augs.append(snake_case_					)

									return img_augs







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,			cfg.INPUT.MAX_SIZE_TEST					)
									snake_case :       Any				=    cfg.INPUT.FORMAT
									snake_case :       Optional[int]				=    cfg.SIZE_DIVISIBILITY
									snake_case :       Optional[int]				=    cfg.PAD_VALUE
									snake_case :       int				=    cfg.INPUT.MAX_SIZE_TEST
									snake_case :       Optional[int]				=    cfg.MODEL.DEVICE
									snake_case :       int				=    torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       List[Any]				=    torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       Dict				=    lambda snake_case_					: (x - self.pixel_mean) / self.pixel_std



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       List[Any]				=    tuple(max(snake_case_					) for s in zip(*[img.shape for img in images]					)					)
									snake_case :       int				=    [im.shape[-2:] for im in images]
									snake_case :       List[Any]				=    [
									    nn.functional.pad(
									        snake_case_				,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,			value=self.pad_value				,			)
									    for size, im in zip(snake_case_				,			snake_case_					)
									]

									return torch.stack(snake_case_					), torch.tensor(snake_case_					)






			def __call__(							self				,			snake_case_				,			snake_case_=False					):
									with torch.no_grad():
															if not isinstance(snake_case_				,			snake_case_					):
																					snake_case :       int				=    [images]
															if single_image:
																					assert len(snake_case_					) == 1
															for i in range(len(snake_case_					)					):
																					if isinstance(images[i]				,			torch.Tensor					):
																											images.insert(snake_case_				,			images.pop(snake_case_					).to(self.device					).float()					)
																					elif not isinstance(images[i]				,			torch.Tensor					):
																											images.insert(
																											    snake_case_				,			torch.as_tensor(img_tensorize(images.pop(snake_case_					)				,			input_format=self.input_format					)					)
																											    .to(self.device					)
																											    .float()				,			)
            # resize smallest edge
															snake_case :       List[Any]				=    torch.tensor([im.shape[:2] for im in images]					)
															snake_case :       Any				=    self.aug(snake_case_					)
															# transpose images and convert to torch tensors
															# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
															# now normalize before pad to avoid useless arithmetic
															snake_case :       str				=    [self.normalizer(snake_case_					) for x in images]
															# now pad them to do the following operations
															snake_case				,					snake_case :       int				=    self.pad(snake_case_					)
															# Normalize

															if self.size_divisibility > 0:
																					raise NotImplementedError()
															# pad
															snake_case :       Union[str, Any]				=    torch.true_divide(snake_case_				,			snake_case_					)
															if single_image:
																					return images[0], sizes[0], scales_yx[0]
															else:
																					return images, sizes, scales_yx







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int])    ->		int:



						"""simple docstring"""

						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes



def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Tuple[int, int])    ->		List[str]:



						"""simple docstring"""

						assert torch.isfinite(_lowerCAmelCase).all(), "Box tensor contains infinite or NaN!"
						snake_case				,					snake_case :       Union[str, Any]				=    box_size
						tensor[:, 0].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 1].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 2].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 3].clamp_(min=0,      max=_lowerCAmelCase)





