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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import SwinaSRImageProcessor
 from transformers.image_transforms import get_image_size




class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[int]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int=7							,      lowerCamelCase							:		Any=3							,      lowerCamelCase							:		List[Any]=1_8							,      lowerCamelCase							:		List[Any]=3_0							,      lowerCamelCase							:		Any=4_0_0							,      lowerCamelCase							:		Optional[Any]=True							,      lowerCamelCase							:		int=1 / 2_5_5							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		Optional[int]=8							,      )			->				Dict:
        _UpperCamelCase  :							List[Any]	    =	parent
        _UpperCamelCase  :							Dict	    =	batch_size
        _UpperCamelCase  :							Optional[Any]	    =	num_channels
        _UpperCamelCase  :							Union[str, Any]	    =	image_size
        _UpperCamelCase  :							int	    =	min_resolution
        _UpperCamelCase  :							List[str]	    =	max_resolution
        _UpperCamelCase  :							str	    =	do_rescale
        _UpperCamelCase  :							Tuple	    =	rescale_factor
        _UpperCamelCase  :							Optional[int]	    =	do_pad
        _UpperCamelCase  :							int	    =	pad_size

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Any:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		str=False							,      lowerCamelCase							:		Union[str, Any]=False							,      lowerCamelCase							:		List[str]=False	)			->				Dict:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
               _UpperCamelCase  :							Optional[Any]	    =	[]
               for i in range(self.batch_size	):
                      image_inputs.append(
                          np.random.randint(
                              2_5_5							,      size=(self.num_channels, self.max_resolution, self.max_resolution)							,      dtype=np.uinta	)	)
        else:
               _UpperCamelCase  :							Any	    =	[]
               for i in range(self.batch_size	):
                      _UpperCamelCase    ,   _UpperCamelCase  :							int	    =	np.random.choice(np.arange(self.min_resolution							,      self.max_resolution	)							,      2	)
                      image_inputs.append(np.random.randint(2_5_5							,      size=(self.num_channels, width, height)							,      dtype=np.uinta	)	)

        if not numpify and not torchify:
               # PIL expects the channel dimension as last dimension
               _UpperCamelCase  :							Tuple	    =	[Image.fromarray(np.moveaxis(lowerCamelCase							,      0							,      -1	)	) for x in image_inputs]

        if torchify:
               _UpperCamelCase  :							int	    =	[torch.from_numpy(lowerCamelCase	) for x in image_inputs]

        return image_inputs







@require_torch
@require_vision
class  __lowerCAmelCase	(							a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			SwinaSRImageProcessor if is_vision_available() else None

 def        __snake_case       (      self							:		Union[str, Any]	)			->				List[Any]:
        _UpperCamelCase  :							int	    =	SwinaSRImageProcessingTester(self	)

 @property
 def        __snake_case       (      self							:		Union[str, Any]	)			->				int:
        return self.image_processor_tester.prepare_image_processor_dict()

 def        __snake_case       (      self							:		int	)			->				int:
        _UpperCamelCase  :							str	    =	self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(lowerCamelCase							,      'do_rescale'	)	)
        self.assertTrue(hasattr(lowerCamelCase							,      'rescale_factor'	)	)
        self.assertTrue(hasattr(lowerCamelCase							,      'do_pad'	)	)
        self.assertTrue(hasattr(lowerCamelCase							,      'pad_size'	)	)

 def        __snake_case       (      self							:		Any	)			->				Dict:
        pass

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Tuple	)			->				int:
        _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	get_image_size(lowerCamelCase	)
        _UpperCamelCase  :							int	    =	self.image_processor_tester.pad_size

        _UpperCamelCase  :							List[Any]	    =	(old_height // size + 1) * size - old_height
        _UpperCamelCase  :							Union[str, Any]	    =	(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width

 def        __snake_case       (      self							:		Any	)			->				Optional[Any]:
        # Initialize image_processor
        _UpperCamelCase  :							List[str]	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _UpperCamelCase  :							Any	    =	self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase	)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase							,      Image.Image	)

        # Test not batched input
        _UpperCamelCase  :							Dict	    =	image_processor(image_inputs[0]							,      return_tensors='pt'	).pixel_values
        _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	self.calculate_expected_size(np.array(image_inputs[0]	)	)
        self.assertEqual(
            encoded_images.shape							,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							,      )

 def        __snake_case       (      self							:		Any	)			->				Tuple:
        # Initialize image_processor
        _UpperCamelCase  :							Optional[int]	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        _UpperCamelCase  :							Tuple	    =	self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase							,      numpify=lowerCamelCase	)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase							,      np.ndarray	)

        # Test not batched input
        _UpperCamelCase  :							Optional[Any]	    =	image_processor(image_inputs[0]							,      return_tensors='pt'	).pixel_values
        _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	self.calculate_expected_size(image_inputs[0]	)
        self.assertEqual(
            encoded_images.shape							,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							,      )

 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        # Initialize image_processor
        _UpperCamelCase  :							Optional[Any]	    =	self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        _UpperCamelCase  :							Any	    =	self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase							,      torchify=lowerCamelCase	)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase							,      torch.Tensor	)

        # Test not batched input
        _UpperCamelCase  :							List[str]	    =	image_processor(image_inputs[0]							,      return_tensors='pt'	).pixel_values
        _UpperCamelCase    ,   _UpperCamelCase  :							Optional[Any]	    =	self.calculate_expected_size(image_inputs[0]	)
        self.assertEqual(
            encoded_images.shape							,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							,      )







"""simple docstring"""
import argparse
import json
import re
import string
import sys
from collections import Counter







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Any:

							def remove_articles(_SCREAMING_SNAKE_CASE					):
														return re.sub(r"""\b(a|an|the)\b"""					,			""" """					,			_SCREAMING_SNAKE_CASE					)

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

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

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

							return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE					)					)					)					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		normalize_answer(_SCREAMING_SNAKE_CASE					).split()
							snake_case_     							=		normalize_answer(_SCREAMING_SNAKE_CASE					).split()
							snake_case_     							=		Counter(_SCREAMING_SNAKE_CASE					) & Counter(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		sum(common.values()					)
							if num_same == 0:
														return 0
							snake_case_     							=		1.0 * num_same / len(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		1.0 * num_same / len(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		(2 * precision * recall) / (precision + recall)
							return fa







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							return normalize_answer(_SCREAMING_SNAKE_CASE					) == normalize_answer(_SCREAMING_SNAKE_CASE					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		[]
							for ground_truth in ground_truths:
														snake_case_     							=		metric_fn(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)
														scores_for_ground_truths.append(_SCREAMING_SNAKE_CASE					)
							return max(_SCREAMING_SNAKE_CASE					)







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

							snake_case_     							=		100.0 * exact_match / total
							snake_case_     							=		100.0 * fa / total

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE				:     Tuple		=     '1.1'
				__SCREAMING_SNAKE_CASE				:     Union[str, 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')
				__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     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,
												)
								__SCREAMING_SNAKE_CASE				:     str		=     dataset_json['data']
				with open(args.prediction_file) as prediction_file:
								__SCREAMING_SNAKE_CASE				:     int		=     json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))



