



"""simple docstring"""




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

def 						__snake_case						(      SCREAMING_SNAKE_CASE__	:						Tuple				)  ->   Any:





     '''simple docstring'''

     def remove_articles(SCREAMING_SNAKE_CASE__	:						Union[str, Any]				):
          return re.sub(R"\b(a|an|the)\b"						,  " "						,  a_				)

     def white_space_fix(SCREAMING_SNAKE_CASE__	:						Optional[Any]				):
          return " ".join(text.split()				)

     def remove_punc(SCREAMING_SNAKE_CASE__	:						Dict				):
          _UpperCAmelCase			:	Union[str, Any]								=    set(string.punctuation				)
          return "".join(ch for ch in text if ch not in exclude				)

     def lower(SCREAMING_SNAKE_CASE__	:						Optional[Any]				):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(a_				)				)				)				)

def 						__snake_case						(      SCREAMING_SNAKE_CASE__	:						Union[str, Any]						,  SCREAMING_SNAKE_CASE__	:						List[str]				)  ->   int:





     '''simple docstring'''

     _UpperCAmelCase			:	str								=    normalize_answer(a_				).split()
     _UpperCAmelCase			:	Tuple								=    normalize_answer(a_				).split()
     _UpperCAmelCase			:	str								=    Counter(a_				) & Counter(a_				)
     _UpperCAmelCase			:	int								=    sum(common.values()				)
     if num_same == 0:
          return 0
     _UpperCAmelCase			:	List[str]								=    1.0 * num_same / len(a_				)
     _UpperCAmelCase			:	List[Any]								=    1.0 * num_same / len(a_				)
     _UpperCAmelCase			:	str								=    (2 * precision * recall) / (precision + recall)
     return fa

def 						__snake_case						(      SCREAMING_SNAKE_CASE__	:						Any						,  SCREAMING_SNAKE_CASE__	:						Any				)  ->   List[str]:





     '''simple docstring'''

     return normalize_answer(a_				) == normalize_answer(a_				)

def 						__snake_case						(      SCREAMING_SNAKE_CASE__	:						Optional[Any]						,  SCREAMING_SNAKE_CASE__	:						Dict						,  SCREAMING_SNAKE_CASE__	:						Any				)  ->   Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase			:	Tuple								=    []
     for ground_truth in ground_truths:
          _UpperCAmelCase			:	Optional[int]								=    metric_fn(a_						,  a_				)
          scores_for_ground_truths.append(a_				)
     return max(a_				)

def 						__snake_case						(      SCREAMING_SNAKE_CASE__	:						str						,  SCREAMING_SNAKE_CASE__	:						Tuple				)  ->   List[Any]:





     '''simple docstring'''

     _UpperCAmelCase			:	List[str]								=    0
     for article in dataset:
          for paragraph in article["paragraphs"]:
               for qa in paragraph["qas"]:
                    total += 1
                    if qa["id"] not in predictions:
                         _UpperCAmelCase			:	int								=    "Unanswered question " + qa["id"] + " will receive score 0."
                         print(a_						,  file=sys.stderr				)
                         continue
                    _UpperCAmelCase			:	Union[str, Any]								=    [x["text"] for x in qa["answers"]]
                    _UpperCAmelCase			:	Any								=    predictions[qa["id"]]
                    exact_match += metric_max_over_ground_truths(a_						,  a_						,  a_				)
                    fa += metric_max_over_ground_truths(a_						,  a_						,  a_				)

     _UpperCAmelCase			:	Any								=    100.0 * exact_match / total
     _UpperCAmelCase			:	int								=    100.0 * fa / total

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


if __name__ == "__main__":
 _lowerCAmelCase				:					List[Any]							=							"1.1"
 _lowerCAmelCase				:					Any							=							argparse.ArgumentParser(description="Evaluation for SQuAD " + expected_version)
 parser.add_argument("dataset_file", help="Dataset file")
 parser.add_argument("prediction_file", help="Prediction File")
 _lowerCAmelCase				:					Dict							=							parser.parse_args()
 with open(args.dataset_file) as dataset_file:
  _lowerCAmelCase				:					Optional[int]							=							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,
   )
  _lowerCAmelCase				:					List[Any]							=							dataset_json["data"]
 with open(args.prediction_file) as prediction_file:
  _lowerCAmelCase				:					Union[str, Any]							=							json.load(prediction_file)
 print(json.dumps(evaluate(dataset, predictions)))








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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase_						     =  logging.get_logger(__name__)




class 							a_ (						a_		):


							'''simple docstring'''






							__a:	Optional[Any]     =    ['''pixel_values''']

							def __init__(		self			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = PILImageResampling.BICUBIC			,			lowercase_ = True			,			lowercase_ = 1 / 2_5_5			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					None:
														'''simple docstring'''

														super().__init__(**lowercase_   )
														lowerCAmelCase_										=     crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
														lowerCAmelCase_										=     get_size_dict(lowercase_   )

														lowerCAmelCase_										=     do_center_crop
														lowerCAmelCase_										=     crop_size
														lowerCAmelCase_										=     do_resize
														lowerCAmelCase_										=     size
														lowerCAmelCase_										=     resample
														lowerCAmelCase_										=     do_rescale
														lowerCAmelCase_										=     rescale_factor
														lowerCAmelCase_										=     do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														lowerCAmelCase_										=     image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     self.size if size is None else size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )

														lowerCAmelCase_						,   lowerCAmelCase_										=     get_image_size(lowercase_   )
														lowerCAmelCase_										=     min(lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     (size['height'] / crop_size['height']) * min_dim
														lowerCAmelCase_										=     (size['width'] / crop_size['width']) * min_dim
														return center_crop(lowercase_			,			size=(cropped_height, cropped_width)			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = PIL.Image.BICUBIC			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     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      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					Any:
														'''simple docstring'''

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

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

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






							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = ChannelDimension.FIRST			,			**lowercase_			,			)					->					PIL.Image.Image:
														'''simple docstring'''

														lowerCAmelCase_										=     do_center_crop if do_center_crop is not None else self.do_center_crop
														lowerCAmelCase_										=     crop_size if crop_size is not None else self.crop_size
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase_										=     size if size is not None else self.size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     resample if resample is not None else self.resample
														lowerCAmelCase_										=     do_rescale if do_rescale is not None else self.do_rescale
														lowerCAmelCase_										=     rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCAmelCase_										=     do_normalize if do_normalize is not None else self.do_normalize
														lowerCAmelCase_										=     image_mean if image_mean is not None else self.image_mean
														lowerCAmelCase_										=     image_std if image_std is not None else self.image_std

														lowerCAmelCase_										=     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_center_crop and crop_size is None:
																					raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'   )

														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.'   )

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'   )

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

														if do_center_crop:
																					lowerCAmelCase_										=     [self.center_crop(lowercase_			,			lowercase_			,			size=lowercase_   ) for image in images]

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

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

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

														lowerCAmelCase_										=     [to_channel_dimension_format(lowercase_			,			lowercase_   ) for image in images]

														lowerCAmelCase_										=     {'pixel_values': images}
														return BatchFeature(data=lowercase_			,			tensor_type=lowercase_   )



