
"""simple docstring"""




from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION




class 					UpperCamelCase     (	A__  ,				A__				):

				"""simple docstring"""



				def __init__(    self      )   ->   Optional[int]:
									"""simple docstring"""







									super().__init__()

									self.register_buffer('watermark_image'		,     torch.zeros((62, 62, 4)      )      )
									SCREAMING_SNAKE_CASE           =     None




				def 							A (    self		,     lowercase__		,     lowercase__=None      )   ->   Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     images[0].height
									SCREAMING_SNAKE_CASE           =     images[0].width

									SCREAMING_SNAKE_CASE           =     sample_size or h

									SCREAMING_SNAKE_CASE           =     min(h / sample_size		,     w / sample_size      )
									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     (int(h / coef      ), int(w / coef      )) if coef < 1 else (h, w)

									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     1024**2, img_w * img_h
									SCREAMING_SNAKE_CASE           =     (Sa / Sa) ** 0.5
									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     int(K * 62      ), img_w - int(14 * K      ), img_h - int(14 * K      )

									if self.watermark_image_as_pil is None:
														SCREAMING_SNAKE_CASE           =     self.watermark_image.to(torch.uinta      ).cpu().numpy()
														SCREAMING_SNAKE_CASE           =     Image.fromarray(snake_case_		,     mode='RGBA'      )
														SCREAMING_SNAKE_CASE           =     watermark_image

									SCREAMING_SNAKE_CASE           =     self.watermark_image_as_pil.resize(
									    (wm_size, wm_size)		,     PIL_INTERPOLATION['bicubic']		,     reducing_gap=snake_case_      )

									for pil_img in images:
														pil_img.paste(snake_case_		,     box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)		,     mask=wm_img.split()[-1]      )

									return images


"""simple docstring"""




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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):

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

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

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

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

					return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_      )      )      )      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
					SCREAMING_SNAKE_CASE           =     normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
					SCREAMING_SNAKE_CASE           =     Counter(SCREAMING_SNAKE_CASE_      ) & Counter(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     sum(common.values()      )
					if num_same == 0:
										return 0
					SCREAMING_SNAKE_CASE           =     1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     (2 * precision * recall) / (precision + recall)
					return fa

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					return normalize_answer(SCREAMING_SNAKE_CASE_      ) == normalize_answer(SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     []
					for ground_truth in ground_truths:
										SCREAMING_SNAKE_CASE           =     metric_fn(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
										scores_for_ground_truths.append(SCREAMING_SNAKE_CASE_      )
					return max(SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     0
					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           =     'Unanswered question ' + qa['id'] + ' will receive score 0.'
																									print(SCREAMING_SNAKE_CASE_,					file=sys.stderr      )
																									continue
																				SCREAMING_SNAKE_CASE           =     [x['text'] for x in qa['answers']]
																				SCREAMING_SNAKE_CASE           =     predictions[qa['id']]
																				exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
																				fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )

					SCREAMING_SNAKE_CASE           =     100.0 * exact_match / total
					SCREAMING_SNAKE_CASE           =     100.0 * fa / total

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


if __name__ == "__main__":
	snake_case			       =		'1.1'
	snake_case			       =		argparse.ArgumentParser(description='Evaluation for SQuAD ' + expected_version)
	parser.add_argument('dataset_file', help='Dataset file')
	parser.add_argument('prediction_file', help='Prediction File')
	snake_case			       =		parser.parse_args()
	with open(args.dataset_file) as dataset_file:
		snake_case			       =		json.load(dataset_file)
		if dataset_json["version"] != expected_version:
			print(
			    'Evaluation expects v-' + expected_version + ', but got dataset with v-' + dataset_json['version'],
			    file=sys.stderr,
			)
		snake_case			       =		dataset_json['data']
	with open(args.prediction_file) as prediction_file:
		snake_case			       =		json.load(prediction_file)
	print(json.dumps(evaluate(dataset, predictions)))
