



'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor



@require_tokenizers
@require_vision
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	str					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		tempfile.mkdtemp()

						# fmt: off
						_lowercase					:       Union[str, Any]       =		['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
						# fmt: on
						_lowercase					:       Any       =		os.path.join(self.tmpdirname     ,  VOCAB_FILES_NAMES['vocab_file']					)
						with open(self.vocab_file     ,  'w'     ,  encoding='utf-8'					) as vocab_writer:
							vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)

						_lowercase					:       List[Any]       =		{
						    'do_resize': True,
						    'size': {'height': 18, 'width': 18},
						    'do_normalize': True,
						    'image_mean': [0.5, 0.5, 0.5],
						    'image_std': [0.5, 0.5, 0.5],
						}
						_lowercase					:       Any       =		os.path.join(self.tmpdirname     ,  UpperCamelCase_					)
						with open(self.image_processor_file     ,  'w'     ,  encoding='utf-8'					) as fp:
							json.dump(UpperCamelCase_     ,  UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Any     ,  **UpperCamelCase_				:	List[str]					)  ->       Optional[int]:



						'''simple docstring'''





						return BertTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  **UpperCamelCase_				:	Dict					)  ->       Union[str, Any]:



						'''simple docstring'''





						return ViTImageProcessor.from_pretrained(self.tmpdirname     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						shutil.rmtree(self.tmpdirname					)




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		[np.random.randint(255     ,  size=(3, 30, 400)     ,  dtype=np.uinta					)]

						_lowercase					:       Union[str, Any]       =		[Image.fromarray(np.moveaxis(UpperCamelCase_     ,  0     ,  -1					)					) for x in image_inputs]

						return image_inputs




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       int:



						'''simple docstring'''





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

						_lowercase					:       Optional[int]       =		VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_					)

						processor.save_pretrained(self.tmpdirname					)
						_lowercase					:       str       =		VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname					)

						self.assertEqual(processor.tokenizer.get_vocab()     ,  tokenizer.get_vocab()					)
						self.assertIsInstance(processor.tokenizer     ,  (BertTokenizer, BertTokenizerFast)					)

						self.assertEqual(processor.image_processor.to_json_string()     ,  image_processor.to_json_string()					)
						self.assertIsInstance(processor.image_processor     ,  UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       str       =		VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()     ,  image_processor=self.get_image_processor()					)
						processor.save_pretrained(self.tmpdirname					)

						_lowercase					:       List[str]       =		self.get_tokenizer(bos_token='(BOS)'     ,  eos_token='(EOS)'					)
						_lowercase					:       Tuple       =		self.get_image_processor(do_normalize=UpperCamelCase_     ,  padding_value=1.0					)

						_lowercase					:       int       =		VisionTextDualEncoderProcessor.from_pretrained(
						    self.tmpdirname     ,  bos_token='(BOS)'     ,  eos_token='(EOS)'     ,  do_normalize=UpperCamelCase_     ,  padding_value=1.0					)

						self.assertEqual(processor.tokenizer.get_vocab()     ,  tokenizer_add_kwargs.get_vocab()					)
						self.assertIsInstance(processor.tokenizer     ,  (BertTokenizer, BertTokenizerFast)					)

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




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Tuple:



						'''simple docstring'''





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

						_lowercase					:       List[str]       =		VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_					)

						_lowercase					:       Union[str, Any]       =		self.prepare_image_inputs()

						_lowercase					:       Any       =		image_processor(UpperCamelCase_     ,  return_tensors='np'					)
						_lowercase					:       str       =		processor(images=UpperCamelCase_     ,  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 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.get_image_processor()
						_lowercase					:       Dict       =		self.get_tokenizer()

						_lowercase					:       Dict       =		VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_					)

						_lowercase					:       int       =		'lower newer'

						_lowercase					:       Tuple       =		processor(text=UpperCamelCase_					)

						_lowercase					:       Union[str, Any]       =		tokenizer(UpperCamelCase_					)

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




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.get_image_processor()
						_lowercase					:       str       =		self.get_tokenizer()

						_lowercase					:       List[Any]       =		VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_					)

						_lowercase					:       List[Any]       =		'lower newer'
						_lowercase					:       Optional[int]       =		self.prepare_image_inputs()

						_lowercase					:       Dict       =		processor(text=UpperCamelCase_     ,  images=UpperCamelCase_					)

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

						# test if it raises when no input is passed
						with self.assertRaises(UpperCamelCase_					):
							processor()




					def 						__UpperCAmelCase     (  self				:	str					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.get_image_processor()
						_lowercase					:       str       =		self.get_tokenizer()

						_lowercase					:       int       =		VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_					)

						_lowercase					:       List[str]       =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						_lowercase					:       Union[str, Any]       =		processor.batch_decode(UpperCamelCase_					)
						_lowercase					:       List[str]       =		tokenizer.batch_decode(UpperCamelCase_					)

						self.assertListEqual(UpperCamelCase_     ,  UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       str       =		self.get_image_processor()
						_lowercase					:       Tuple       =		self.get_tokenizer()

						_lowercase					:       str       =		VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_     ,  image_processor=UpperCamelCase_					)

						_lowercase					:       Any       =		'lower newer'
						_lowercase					:       str       =		self.prepare_image_inputs()

						_lowercase					:       Dict       =		processor(text=UpperCamelCase_     ,  images=UpperCamelCase_					)

						self.assertListEqual(list(inputs.keys()					)     ,  processor.model_input_names					)





'''simple docstring'''

import argparse
from collections import defaultdict







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase					) ->			int:
	_lowercase					:       Optional[int]       =		f'''{file}_{class_name}_{test_name}'''
	done_test[_id] += 1

	with open(_lowercase,				'r'					) as f:
		_lowercase					:       Optional[int]       =		f.readlines()

	_lowercase					:       Dict       =		f'''class {class_name}('''
	_lowercase					:       List[Any]       =		f'''{4 * " "}def {test_name}('''
	_lowercase					:       List[str]       =		f'''{8 * " "}{correct_line.split()[0]}'''
	_lowercase					:       List[str]       =		f'''{16 * " "}{correct_line.split()[0]}'''
	_lowercase					:       Dict       =		False
	_lowercase					:       str       =		False
	_lowercase					:       List[Any]       =		False
	_lowercase					:       Union[str, Any]       =		False
	_lowercase					:       Any       =		0
	_lowercase					:       Tuple       =		0

	_lowercase					:       Optional[int]       =		[]
	for line in lines:
		if line.startswith(_lowercase					):
			_lowercase					:       int       =		True
		elif in_class and line.startswith(_lowercase					):
			_lowercase					:       List[Any]       =		True
		elif in_class and in_func and (line.startswith(_lowercase					) or line.startswith(_lowercase					)):
			_lowercase					:       str       =		len(line.split(correct_line.split()[0]					)[0]					)
			count += 1

			if count == done_test[_id]:
				_lowercase					:       List[Any]       =		True

		if in_class and in_func and in_line:
			if ")" not in line:
				continue
			else:
				_lowercase					:       Any       =		True

		if in_class and in_func and in_line and insert_line:
			new_lines.append(f'''{spaces * " "}{correct_line}'''					)
			_lowercase					:       Any       =		False
		else:
			new_lines.append(_lowercase					)

	with open(_lowercase,				'w'					) as f:
		for line in new_lines:
			f.write(_lowercase					)







def 						__UpperCamelCase				(							_lowercase,				_lowercase=None					) ->			Optional[Any]:
	if fail is not None:
		with open(_lowercase,				'r'					) as f:
			_lowercase					:       Any       =		{l.strip() for l in f.readlines()}
	else:
		_lowercase					:       str       =		None

	with open(_lowercase,				'r'					) as f:
		_lowercase					:       str       =		f.readlines()

	_lowercase					:       Union[str, Any]       =		defaultdict(_lowercase					)
	for line in correct_lines:
		_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Union[str, Any]       =		line.split(';'					)
		if test_failures is None or "::".join([file, class_name, test_name]					) in test_failures:
			overwrite_file(_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase					)


if __name__ == "__main__":
		_A				:    str     						=argparse.ArgumentParser()
		parser.add_argument('''--correct_filename''', help='''filename of tests with expected result''')
		parser.add_argument('''--fail_filename''', help='''filename of test failures''', type=str, default=None)
		_A				:    Union[str, Any]     						=parser.parse_args()

		main(args.correct_filename, args.fail_filename)
