
"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case			       =		logging.get_logger(__name__)




class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				List[Any]						  =    ['pixel_values']


				def __init__(    self		,     lowercase__ = True		,     lowercase__ = None		,     lowercase__ = PILImageResampling.BILINEAR		,     lowercase__ = True		,     lowercase__ = 1 / 255		,     lowercase__ = True		,     lowercase__ = None		,     lowercase__ = None		,     **lowercase__		,     )   ->   List[str]:
									"""simple docstring"""







									super().__init__(**lowercase__      )
									SCREAMING_SNAKE_CASE           =     size if size is not None else {"height": 224, "width": 224}
									SCREAMING_SNAKE_CASE           =     get_size_dict(lowercase__      )
									SCREAMING_SNAKE_CASE           =     do_resize
									SCREAMING_SNAKE_CASE           =     do_rescale
									SCREAMING_SNAKE_CASE           =     do_normalize
									SCREAMING_SNAKE_CASE           =     size
									SCREAMING_SNAKE_CASE           =     resample
									SCREAMING_SNAKE_CASE           =     rescale_factor
									SCREAMING_SNAKE_CASE           =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									SCREAMING_SNAKE_CASE           =     image_std if image_std is not None else IMAGENET_STANDARD_STD


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__ = PILImageResampling.BILINEAR		,     lowercase__ = None		,     **lowercase__		,     )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     get_size_dict(lowercase__      )
									if "height" not in size or "width" not in size:
														raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''      )
									return resize(
									    lowercase__		,     size=(size['height'], size['width'])		,     resample=lowercase__		,     data_format=lowercase__		,     **lowercase__      )


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







									return rescale(lowercase__		,     scale=lowercase__		,     data_format=lowercase__		,     **lowercase__      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__ = None		,     **lowercase__		,     )   ->   Dict:
									"""simple docstring"""







									return normalize(lowercase__		,     mean=lowercase__		,     std=lowercase__		,     data_format=lowercase__		,     **lowercase__      )




				def 							A (    self		,     lowercase__		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = ChannelDimension.FIRST		,     **lowercase__		,     )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE           =     do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE           =     do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE           =     resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE           =     rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE           =     image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE           =     image_std if image_std is not None else self.image_std

									SCREAMING_SNAKE_CASE           =     size if size is not None else self.size
									SCREAMING_SNAKE_CASE           =     get_size_dict(lowercase__      )

									SCREAMING_SNAKE_CASE           =     make_list_of_images(lowercase__      )

									if not valid_images(lowercase__      ):
														raise ValueError(
														    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
														    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

									if do_resize and size is None:
														raise ValueError('Size must be specified if do_resize is True.'      )

									if do_rescale and rescale_factor is None:
														raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

									# All transformations expect numpy arrays.
									SCREAMING_SNAKE_CASE           =     [to_numpy_array(lowercase__      ) for image in images]

									if do_resize:
														SCREAMING_SNAKE_CASE           =     [self.resize(image=lowercase__		,     size=lowercase__		,     resample=lowercase__      ) for image in images]

									if do_rescale:
														SCREAMING_SNAKE_CASE           =     [self.rescale(image=lowercase__		,     scale=lowercase__      ) for image in images]

									if do_normalize:
														SCREAMING_SNAKE_CASE           =     [self.normalize(image=lowercase__		,     mean=lowercase__		,     std=lowercase__      ) for image in images]

									SCREAMING_SNAKE_CASE           =     [to_channel_dimension_format(lowercase__		,     lowercase__      ) for image in images]

									SCREAMING_SNAKE_CASE           =     {"pixel_values": images}
									return BatchFeature(data=lowercase__		,     tensor_type=lowercase__      )


"""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)))
