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 OwlViTImageProcessor
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=7       ,  lowerCAmelCase_=3       ,  lowerCAmelCase_=18       ,  lowerCAmelCase_=30       ,  lowerCAmelCase_=400       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]       ,  lowerCAmelCase_=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]       ,  lowerCAmelCase_=True       ,  ):
		_lowercase   =parent
		_lowercase   =batch_size
		_lowercase   =num_channels
		_lowercase   =image_size
		_lowercase   =min_resolution
		_lowercase   =max_resolution
		_lowercase   =do_resize
		_lowercase   =size if size is not None else {"height": 18, "width": 18}
		_lowercase   =do_center_crop
		_lowercase   =crop_size if crop_size is not None else {"height": 18, "width": 18}
		_lowercase   =do_normalize
		_lowercase   =image_mean
		_lowercase   =image_std
		_lowercase   =do_convert_rgb


	def __lowerCAmelCase      (   self       ):
		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,
		    "do_convert_rgb": self.do_convert_rgb,
		}

@require_torch
@require_vision
class 	_a  (							lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					OwlViTImageProcessor if is_vision_available() else None


	def __lowerCAmelCase      (   self       ):
		_lowercase   =OwlViTImageProcessingTester(self       )


	@property
	def __lowerCAmelCase      (   self       ):
		return self.image_processor_tester.prepare_image_processor_dict()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_resize"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "size"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_center_crop"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "center_crop"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_normalize"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "image_mean"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "image_std"       )       )
		self.assertTrue(hasattr(lowerCAmelCase_       ,  "do_convert_rgb"       )       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.image_processing_class.from_dict(self.image_processor_dict       )
		self.assertEqual(image_processor.size       ,  {"height": 18, "width": 18}       )
		self.assertEqual(image_processor.crop_size       ,  {"height": 18, "width": 18}       )

		_lowercase   =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 __lowerCAmelCase      (   self       ):
		# Initialize image_processing
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		# create random PIL images
		_lowercase   =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowerCAmelCase_       )

		for image in image_inputs:
			self.assertIsInstance(lowerCAmelCase_       ,  Image.Image       )

		# Test not batched input
		_lowercase   =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
		_lowercase   =image_processing(lowerCAmelCase_       ,  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 __lowerCAmelCase      (   self       ):
		# Initialize image_processing
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		# create random numpy tensors
		_lowercase   =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowerCAmelCase_       ,  numpify=lowerCAmelCase_       )
		for image in image_inputs:
			self.assertIsInstance(lowerCAmelCase_       ,  np.ndarray       )

		# Test not batched input
		_lowercase   =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
		_lowercase   =image_processing(lowerCAmelCase_       ,  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 __lowerCAmelCase      (   self       ):
		# Initialize image_processing
		_lowercase   =self.image_processing_class(**self.image_processor_dict       )
		# create random PyTorch tensors
		_lowercase   =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowerCAmelCase_       ,  torchify=lowerCAmelCase_       )
		for image in image_inputs:
			self.assertIsInstance(lowerCAmelCase_       ,  torch.Tensor       )

		# Test not batched input
		_lowercase   =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
		_lowercase   =image_processing(lowerCAmelCase_       ,  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 _A		(		lowercase						):







    """simple docstring"""

    def remove_articles(lowercase						):
        return re.sub(R'''\b(a|an|the)\b'''			,       ''' '''			,       lowercase						)

    def white_space_fix(lowercase						):
        return " ".join(text.split()						)

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

    def lower(lowercase						):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(lowercase						)						)						)						)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=normalize_answer(lowercase						).split()
    a											=normalize_answer(lowercase						).split()
    a											=Counter(lowercase						) & Counter(lowercase						)
    a											=sum(common.values()						)
    if num_same == 0:
        return 0
    a											=1.0 * num_same / len(lowercase						)
    a											=1.0 * num_same / len(lowercase						)
    a											=(2 * precision * recall) / (precision + recall)
    return fa
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    return normalize_answer(lowercase						) == normalize_answer(lowercase						)
def _A		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    a											=[]
    for ground_truth in ground_truths:
        a											=metric_fn(lowercase			,       lowercase						)
        scores_for_ground_truths.append(lowercase						)
    return max(lowercase						)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=a											=a											=0
    for article in dataset:
        for paragraph in article["paragraphs"]:
            for qa in paragraph["qas"]:
                total += 1
                if qa["id"] not in predictions:
                    a											='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
                    print(lowercase			,       file=sys.stderr						)
                    continue
                a											=[x['''text'''] for x in qa['''answers''']]
                a											=predictions[qa['''id''']]
                exact_match += metric_max_over_ground_truths(lowercase			,       lowercase			,       lowercase						)
                fa += metric_max_over_ground_truths(lowercase			,       lowercase			,       lowercase						)

    a											=100.0 * exact_match / total
    a											=100.0 * fa / total

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


if __name__ == "__main__":
       lowerCamelCase_  :  List[Any]     		=    """1.1"""
       lowerCamelCase_  :  Optional[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_  :  Optional[Any]     		=    parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              lowerCamelCase_  :  List[str]     		=    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_  :  Dict     		=    dataset_json["""data"""]
       with open(args.prediction_file) as prediction_file:
              lowerCamelCase_  :  Optional[Any]     		=    json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))