


'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import DeiTImageProcessor





class 					snake_case   (   unittest.TestCase      ):





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase      ,  UpperCamelCase=7      ,  UpperCamelCase=3      ,  UpperCamelCase=18      ,  UpperCamelCase=30      ,  UpperCamelCase=400      ,  UpperCamelCase=True      ,  UpperCamelCase=None      ,  UpperCamelCase=True      ,  UpperCamelCase=None      ,  UpperCamelCase=True      ,  UpperCamelCase=[0.5, 0.5, 0.5]      ,  UpperCamelCase=[0.5, 0.5, 0.5]      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  size if size is not None else {"height": 20, "width": 20}
   lowerCamelCase_								=  crop_size if crop_size is not None else {"height": 18, "width": 18}

   lowerCamelCase_								=  parent
   lowerCamelCase_								=  batch_size
   lowerCamelCase_								=  num_channels
   lowerCamelCase_								=  image_size
   lowerCamelCase_								=  min_resolution
   lowerCamelCase_								=  max_resolution
   lowerCamelCase_								=  do_resize
   lowerCamelCase_								=  size
   lowerCamelCase_								=  do_center_crop
   lowerCamelCase_								=  crop_size
   lowerCamelCase_								=  do_normalize
   lowerCamelCase_								=  image_mean
   lowerCamelCase_								=  image_std







  def   snake_case		(   self					):







   """simple docstring"""
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
   }





@require_torch
@require_vision
class 					snake_case   (   lowercase      ,						unittest.TestCase      ):





  """simple docstring"""




  _lowerCamelCase		   =  DeiTImageProcessor if is_vision_available() else None
  _lowerCamelCase		   =  True






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  DeiTImageProcessingTester(self					)






  @property
  def   snake_case		(   self					):







   """simple docstring"""
   return self.image_processor_tester.prepare_image_processor_dict()






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.image_processing_class(**self.image_processor_dict					)
   self.assertTrue(hasattr(UpperCamelCase      ,  "do_resize"					)					)
   self.assertTrue(hasattr(UpperCamelCase      ,  "size"					)					)
   self.assertTrue(hasattr(UpperCamelCase      ,  "do_center_crop"					)					)
   self.assertTrue(hasattr(UpperCamelCase      ,  "center_crop"					)					)
   self.assertTrue(hasattr(UpperCamelCase      ,  "do_normalize"					)					)
   self.assertTrue(hasattr(UpperCamelCase      ,  "image_mean"					)					)
   self.assertTrue(hasattr(UpperCamelCase      ,  "image_std"					)					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.image_processing_class.from_dict(self.image_processor_dict					)
   self.assertEqual(image_processor.size      ,  {"height": 20, "width": 20}					)
   self.assertEqual(image_processor.crop_size      ,  {"height": 18, "width": 18}					)

   lowerCamelCase_								=  self.image_processing_class.from_dict(self.image_processor_dict      ,  size=42      ,  crop_size=84					)
   self.assertEqual(image_processor.size      ,  {"height": 42, "width": 42}					)
   self.assertEqual(image_processor.crop_size      ,  {"height": 84, "width": 84}					)






  def   snake_case		(   self					):







   """simple docstring"""
   pass






  def   snake_case		(   self					):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_								=  self.image_processing_class(**self.image_processor_dict					)
   # create random PIL images
   lowerCamelCase_								=  prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCamelCase					)
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase      ,  Image.Image					)

   # Test not batched input
   lowerCamelCase_								=  image_processing(image_inputs[0]      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )

   # Test batched
   lowerCamelCase_								=  image_processing(UpperCamelCase      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )






  def   snake_case		(   self					):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_								=  self.image_processing_class(**self.image_processor_dict					)
   # create random numpy tensors
   lowerCamelCase_								=  prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCamelCase      ,  numpify=UpperCamelCase					)
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase      ,  np.ndarray					)

   # Test not batched input
   lowerCamelCase_								=  image_processing(image_inputs[0]      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )

   # Test batched
   lowerCamelCase_								=  image_processing(UpperCamelCase      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )







  def   snake_case		(   self					):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_								=  self.image_processing_class(**self.image_processor_dict					)
   # create random PyTorch tensors
   lowerCamelCase_								=  prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCamelCase      ,  torchify=UpperCamelCase					)
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase      ,  torch.Tensor					)

   # Test not batched input
   lowerCamelCase_								=  image_processing(image_inputs[0]      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )

   # Test batched
   lowerCamelCase_								=  image_processing(UpperCamelCase      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )







'''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)



