


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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a_  :    Dict      =			logging.get_logger(__name__)


if is_vision_available():
     import PIL





class 					snake_case   (   lowercase_      ):





  """simple docstring"""




  _lowerCamelCase		   =  ["pixel_values"]






  def __init__(   self      ,  UpperCamelCase = True      ,  UpperCamelCase = None      ,  UpperCamelCase = PILImageResampling.BICUBIC      ,  UpperCamelCase = True      ,  UpperCamelCase = None      ,  UpperCamelCase = True      ,  UpperCamelCase = 1 / 255      ,  UpperCamelCase = True      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = True      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   super().__init__(**UpperCamelCase_					)
   lowerCamelCase_								=  size if size is not None else {"shortest_edge": 224}
   lowerCamelCase_								=  get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_					)
   lowerCamelCase_								=  crop_size if crop_size is not None else {"height": 224, "width": 224}
   lowerCamelCase_								=  get_size_dict(UpperCamelCase_					)

   lowerCamelCase_								=  do_resize
   lowerCamelCase_								=  size
   lowerCamelCase_								=  resample
   lowerCamelCase_								=  do_center_crop
   lowerCamelCase_								=  crop_size
   lowerCamelCase_								=  do_rescale
   lowerCamelCase_								=  rescale_factor
   lowerCamelCase_								=  do_normalize
   lowerCamelCase_								=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   lowerCamelCase_								=  image_std if image_std is not None else OPENAI_CLIP_STD
   lowerCamelCase_								=  do_convert_rgb






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase = PILImageResampling.BICUBIC      ,  UpperCamelCase = None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_					)
   lowerCamelCase_								=  get_resize_output_image_size(
       UpperCamelCase_      ,  size=(size["height"], size["width"])      ,  default_to_square=UpperCamelCase_					)
   return resize(UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase = None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  get_size_dict(UpperCamelCase_					)
   return center_crop(UpperCamelCase_      ,  size=(size["height"], size["width"])      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase = None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   return rescale(UpperCamelCase_      ,  scale=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase = None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   return normalize(UpperCamelCase_      ,  mean=UpperCamelCase_      ,  std=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_					)







  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = ChannelDimension.FIRST      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   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(UpperCamelCase_      ,  default_to_square=UpperCamelCase_					)
   lowerCamelCase_								=  resample if resample is not None else self.resample
   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(UpperCamelCase_					)
   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_								=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   lowerCamelCase_								=  make_list_of_images(UpperCamelCase_					)

   if not valid_images(UpperCamelCase_					):
    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_center_crop and crop_size is None:
    raise ValueError("Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True."					)

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
    lowerCamelCase_								=  [convert_to_rgb(UpperCamelCase_					) for image in images]

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

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

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

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

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

   lowerCamelCase_								=  [to_channel_dimension_format(UpperCamelCase_      ,  UpperCamelCase_					) for image in images]

   lowerCamelCase_								=  {"pixel_values": images}
   return BatchFeature(data=UpperCamelCase_      ,  tensor_type=UpperCamelCase_					)







'''simple docstring'''

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





def __snake_case (				UpperCAmelCase_						:						str		):

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

 def white_space_fix(UpperCAmelCase_						:						List[str]		):
  return " ".join(text.split()		)

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

 def lower(UpperCAmelCase_						:						List[str]		):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase_		)		)		)		)





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						int		):
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  Counter(UpperCAmelCase_		) & Counter(UpperCAmelCase_		)
 lowerCamelCase_								=  sum(common.values()		)
 if num_same == 0:
  return 0
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  (2 * precision * recall) / (precision + recall)
 return fa





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						List[Any]		):
 return normalize_answer(UpperCAmelCase_		) == normalize_answer(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_								=  []
 for ground_truth in ground_truths:
  lowerCamelCase_								=  metric_fn(UpperCAmelCase_ ,    UpperCAmelCase_		)
  scores_for_ground_truths.append(UpperCAmelCase_		)
 return max(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						Dict		):
 lowerCamelCase_								=  lowerCamelCase_								=  lowerCamelCase_								=  0
 lowerCamelCase_								=  []
 for passage in dataset:
  for qa in passage["qas"]:
   total += 1
   if qa["id"] not in predictions:
    lowerCamelCase_								=  F'''Unanswered question {qa["id"]} will receive score 0.'''
    print(UpperCAmelCase_ ,    file=sys.stderr		)
    continue

   lowerCamelCase_								=  [x["text"] for x in qa["answers"]]
   lowerCamelCase_								=  predictions[qa["id"]]

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

   fa += metric_max_over_ground_truths(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  exact_match / total
 lowerCamelCase_								=  fa / total

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


if __name__ == "__main__":
     a_  :    Optional[int]      =			"""1.0"""
     a_  :    Dict      =			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_  :    Optional[int]      =			parser.parse_args()

     with open(args.data_file) as data_file:
          a_  :    Any      =			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_  :    Any      =			dataset_json["""data"""]

     with open(args.pred_file) as pred_file:
          a_  :    List[str]      =			json.load(pred_file)

     a_     ,       a_  :    int      =			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)



