

'''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 ViTImageProcessor



class     lowercase_		(						unittest.TestCase	):

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_=7				,	a_=3				,	a_=1_8				,	a_=3_0				,	a_=4_0_0				,	a_=True				,	a_=None				,	a_=True				,	a_=[0.5, 0.5, 0.5]				,	a_=[0.5, 0.5, 0.5]				,	)							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		size if size is not None else {'height': 1_8, 'width': 1_8}
    UpperCAmelCase  							=		parent
    UpperCAmelCase  							=		batch_size
    UpperCAmelCase  							=		num_channels
    UpperCAmelCase  							=		image_size
    UpperCAmelCase  							=		min_resolution
    UpperCAmelCase  							=		max_resolution
    UpperCAmelCase  							=		do_resize
    UpperCAmelCase  							=		size
    UpperCAmelCase  							=		do_normalize
    UpperCAmelCase  							=		image_mean
    UpperCAmelCase  							=		image_std





  def   snake_case_     (       self       )							->			Optional[int]:


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




@require_torch
@require_vision
class     lowercase_		(						a	,    unittest.TestCase	):

  '''simple docstring'''





  __lowerCAmelCase   :	Optional[Any]      					=				ViTImageProcessor if is_vision_available() else None
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		ViTImageProcessingTester(self       )
  @property
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    return self.image_processor_tester.prepare_image_processor_dict()
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.image_processing_class(**self.image_processor_dict       )
    self.assertTrue(hasattr(a_				,	'image_mean'       )       )
    self.assertTrue(hasattr(a_				,	'image_std'       )       )
    self.assertTrue(hasattr(a_				,	'do_normalize'       )       )
    self.assertTrue(hasattr(a_				,	'do_resize'       )       )
    self.assertTrue(hasattr(a_				,	'size'       )       )
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		self.image_processing_class.from_dict(self.image_processor_dict       )
    self.assertEqual(image_processor.size				,	{'height': 1_8, 'width': 1_8}       )

    UpperCAmelCase  							=		self.image_processing_class.from_dict(self.image_processor_dict				,	size=4_2       )
    self.assertEqual(image_processor.size				,	{'height': 4_2, 'width': 4_2}       )
  def   snake_case_     (       self       )							->			Optional[Any]:


    """simple docstring"""
    pass
  def   snake_case_     (       self       )							->			Optional[Any]:


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

    # Test not batched input
    UpperCAmelCase  							=		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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)

    # Test batched
    UpperCAmelCase  							=		image_processing(a_				,	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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)
  def   snake_case_     (       self       )							->			Optional[int]:


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

    # Test not batched input
    UpperCAmelCase  							=		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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)

    # Test batched
    UpperCAmelCase  							=		image_processing(a_				,	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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)





  def   snake_case_     (       self       )							->			Tuple:


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

    # Test not batched input
    UpperCAmelCase  							=		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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)

    # Test batched
    UpperCAmelCase  							=		image_processing(a_				,	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.size['height'],
            self.image_processor_tester.size['width'],
        )				,	)





'''simple docstring'''




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


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]  ):

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

  def white_space_fix(SCREAMING_SNAKE_CASE :				Union[str, Any]  ):
    return " ".join(text.split()  )

  def remove_punc(SCREAMING_SNAKE_CASE :				List[Any]  ):
    UpperCAmelCase  							=		set(string.punctuation  )
    return "".join(ch for ch in text if ch not in exclude  )

  def lower(SCREAMING_SNAKE_CASE :				Any  ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE  )  )  )  )


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


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				int  ):
  return normalize_answer(SCREAMING_SNAKE_CASE  ) == normalize_answer(SCREAMING_SNAKE_CASE  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict    ,				SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Dict  ):
  UpperCAmelCase  							=		[]
  for ground_truth in ground_truths:
    UpperCAmelCase  							=		metric_fn(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )
    scores_for_ground_truths.append(SCREAMING_SNAKE_CASE  )
  return max(SCREAMING_SNAKE_CASE  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[Any]    ,				SCREAMING_SNAKE_CASE :				Optional[int]  ):
  UpperCAmelCase  							=		UpperCAmelCase  							=		UpperCAmelCase  							=		0
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          UpperCAmelCase  							=		'Unanswered question ' + qa['id'] + ' will receive score 0.'
          print(SCREAMING_SNAKE_CASE    ,				file=sys.stderr  )
          continue
        UpperCAmelCase  							=		[x['text'] for x in qa['answers']]
        UpperCAmelCase  							=		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  )

  UpperCAmelCase  							=		100.0 * exact_match / total
  UpperCAmelCase  							=		100.0 * fa / total

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


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


