




"""simple docstring"""

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

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






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

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

	def remove_punc(_UpperCAmelCase		):
		A_					:			Union[str, Any]						=						set(string.punctuation		)
		return "".join(ch for ch in text if ch not in exclude		)

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

	return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase		)		)		)		)

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			str						=						normalize_answer(_UpperCAmelCase		).split()
	A_					:			str						=						normalize_answer(_UpperCAmelCase		).split()
	A_					:			Tuple						=						Counter(_UpperCAmelCase		) & Counter(_UpperCAmelCase		)
	A_					:			Optional[int]						=						sum(common.values()		)
	if num_same == 0:
		return 0
	A_					:			List[str]						=						1.0 * num_same / len(_UpperCAmelCase		)
	A_					:			Union[str, Any]						=						1.0 * num_same / len(_UpperCAmelCase		)
	A_					:			Dict						=						(2 * precision * recall) / (precision + recall)
	return fa

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	return normalize_answer(_UpperCAmelCase		) == normalize_answer(_UpperCAmelCase		)

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			List[str]						=						[]
	for ground_truth in ground_truths:
		A_					:			List[str]						=						metric_fn(_UpperCAmelCase					, _UpperCAmelCase		)
		scores_for_ground_truths.append(_UpperCAmelCase		)
	return max(_UpperCAmelCase		)

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






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

	A_					:			Union[str, Any]						=						100.0 * exact_match / total
	A_					:			Union[str, Any]						=						100.0 * fa / total

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


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











"""simple docstring"""

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			List[str]						=						tempfile.mkdtemp()

					A_					:			List[str]						=						CLIPImageProcessor()
					A_					:			Any						=						BertTokenizer.from_pretrained(
					    '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']    )

					A_					:			str						=						GitProcessor(_lowerCamelCase ,       _lowerCamelCase    )

					processor.save_pretrained(self.tmpdirname    )







				def    a_					(							self				:   Tuple ,       **_lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).tokenizer







				def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   int    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).image_processor







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			List[str]						=						[np.random.randint(2_55 ,       size=(3, 30, 4_00) ,       dtype=np.uinta    )]

					A_					:			Any						=						[Image.fromarray(np.moveaxis(_lowerCamelCase ,       0 ,       -1    )    ) for x in image_inputs]

					return image_inputs







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()    )
					processor.save_pretrained(self.tmpdirname    )

					A_					:			Union[str, Any]						=						self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''    )
					A_					:			List[Any]						=						self.get_image_processor(do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					A_					:			List[str]						=						GitProcessor.from_pretrained(
					    self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()    )
					self.assertIsInstance(processor.tokenizer ,       _lowerCamelCase    )

					self.assertEqual(processor.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()    )
					self.assertIsInstance(processor.image_processor ,       _lowerCamelCase    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Tuple						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						self.prepare_image_inputs()

					A_					:			Any						=						image_processor(_lowerCamelCase ,       return_tensors='''np'''    )
					A_					:			Optional[Any]						=						processor(images=_lowerCamelCase ,       return_tensors='''np'''    )

					for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum() ,       input_processor[key].sum() ,       delta=1E-2    )







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						self.get_image_processor()
					A_					:			Union[str, Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			List[str]						=						'''lower newer'''

					A_					:			Tuple						=						processor(text=_lowerCamelCase    )

					A_					:			List[str]						=						tokenizer(_lowerCamelCase ,       return_token_type_ids=_lowerCamelCase    )

					for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key] ,       encoded_processor[key]    )







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						'''lower newer'''
					A_					:			str						=						self.prepare_image_inputs()

					A_					:			Optional[int]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )

					# test if it raises when no input is passed
					with pytest.raises(_lowerCamelCase    ):
						processor()







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			List[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Dict						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A_					:			str						=						processor.batch_decode(_lowerCamelCase    )
					A_					:			int						=						tokenizer.batch_decode(_lowerCamelCase    )

					self.assertListEqual(_lowerCamelCase ,       _lowerCamelCase    )







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Dict						=						self.get_image_processor()
					A_					:			List[str]						=						self.get_tokenizer()

					A_					:			Any						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			int						=						'''lower newer'''
					A_					:			Union[str, Any]						=						self.prepare_image_inputs()

					A_					:			List[str]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )





