


"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
	from PIL import Image

	from transformers import AlignProcessor, EfficientNetImageProcessor



@require_vision
class 			lowerCamelCase__						(   unittest.TestCase			):







						def __a						(							self   :    int		):
								A					       = tempfile.mkdtemp()

								A					       = [
								    '[UNK]',
								    '[CLS]',
								    '[SEP]',
								    '[PAD]',
								    '[MASK]',
								    'want',
								    '##want',
								    '##ed',
								    'wa',
								    'un',
								    'runn',
								    '##ing',
								    ',',
								    'low',
								    'lowest',
								]
								A					       = 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]		)		)

								A					       = {
								    'do_resize': True,
								    'size': 20,
								    'do_center_crop': True,
								    'crop_size': 18,
								    'do_normalize': True,
								    'image_mean': [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],
								    'image_std': [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],
								}
								A					       = os.path.join(self.tmpdirname			,			_lowercase		)
								with open(self.image_processor_file			,			'w'			,			encoding='utf-8'		) as fp:
										json.dump(_lowercase			,			_lowercase		)







						def __a						(							self   :    Any			,			**_lowercase   :    Union[str, Any]		):
								return BertTokenizer.from_pretrained(self.tmpdirname			,			**_lowercase		)







						def __a						(							self   :    Tuple			,			**_lowercase   :    int		):
								return BertTokenizerFast.from_pretrained(self.tmpdirname			,			**_lowercase		)







						def __a						(							self   :    int			,			**_lowercase   :    List[Any]		):
								return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,			**_lowercase		)







						def __a						(							self   :    Union[str, Any]		):
								shutil.rmtree(self.tmpdirname		)







						def __a						(							self   :    List[str]		):
								A					       = [np.random.randint(255			,			size=(3, 30, 400)			,			dtype=np.uinta		)]
								A					       = [Image.fromarray(np.moveaxis(_lowercase			,			0			,			-1		)		) for x in image_inputs]
								return image_inputs







						def __a						(							self   :    Dict		):
								A					       = self.get_tokenizer()
								A					       = self.get_rust_tokenizer()
								A					       = self.get_image_processor()

								A					       = AlignProcessor(tokenizer=_lowercase			,			image_processor=_lowercase		)
								processor_slow.save_pretrained(self.tmpdirname		)
								A					       = AlignProcessor.from_pretrained(self.tmpdirname			,			use_fast=_lowercase		)

								A					       = AlignProcessor(tokenizer=_lowercase			,			image_processor=_lowercase		)
								processor_fast.save_pretrained(self.tmpdirname		)
								A					       = AlignProcessor.from_pretrained(self.tmpdirname		)

								self.assertEqual(processor_slow.tokenizer.get_vocab()			,			tokenizer_slow.get_vocab()		)
								self.assertEqual(processor_fast.tokenizer.get_vocab()			,			tokenizer_fast.get_vocab()		)
								self.assertEqual(tokenizer_slow.get_vocab()			,			tokenizer_fast.get_vocab()		)
								self.assertIsInstance(processor_slow.tokenizer			,			_lowercase		)
								self.assertIsInstance(processor_fast.tokenizer			,			_lowercase		)

								self.assertEqual(processor_slow.image_processor.to_json_string()			,			image_processor.to_json_string()		)
								self.assertEqual(processor_fast.image_processor.to_json_string()			,			image_processor.to_json_string()		)
								self.assertIsInstance(processor_slow.image_processor			,			_lowercase		)
								self.assertIsInstance(processor_fast.image_processor			,			_lowercase		)







						def __a						(							self   :    Optional[Any]		):
								A					       = AlignProcessor(tokenizer=self.get_tokenizer()			,			image_processor=self.get_image_processor()		)
								processor.save_pretrained(self.tmpdirname		)

								A					       = self.get_tokenizer(bos_token='(BOS)'			,			eos_token='(EOS)'		)
								A					       = self.get_image_processor(do_normalize=_lowercase			,			padding_value=1.0		)

								A					       = AlignProcessor.from_pretrained(
								    self.tmpdirname			,			bos_token='(BOS)'			,			eos_token='(EOS)'			,			do_normalize=_lowercase			,			padding_value=1.0		)

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

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







						def __a						(							self   :    Dict		):
								A					       = self.get_image_processor()
								A					       = self.get_tokenizer()

								A					       = AlignProcessor(tokenizer=_lowercase			,			image_processor=_lowercase		)

								A					       = self.prepare_image_inputs()

								A					       = image_processor(_lowercase			,			return_tensors='np'		)
								A					       = processor(images=_lowercase			,			return_tensors='np'		)

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







						def __a						(							self   :    Dict		):
								A					       = self.get_image_processor()
								A					       = self.get_tokenizer()

								A					       = AlignProcessor(tokenizer=_lowercase			,			image_processor=_lowercase		)

								A					       = 'lower newer'

								A					       = processor(text=_lowercase		)

								A					       = tokenizer(_lowercase			,			padding='max_length'			,			max_length=64		)

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







						def __a						(							self   :    List[str]		):
								A					       = self.get_image_processor()
								A					       = self.get_tokenizer()

								A					       = AlignProcessor(tokenizer=_lowercase			,			image_processor=_lowercase		)

								A					       = 'lower newer'
								A					       = self.prepare_image_inputs()

								A					       = processor(text=_lowercase			,			images=_lowercase		)

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

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







						def __a						(							self   :    Tuple		):
								A					       = self.get_image_processor()
								A					       = self.get_tokenizer()

								A					       = AlignProcessor(tokenizer=_lowercase			,			image_processor=_lowercase		)

								A					       = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								A					       = processor.batch_decode(_lowercase		)
								A					       = tokenizer.batch_decode(_lowercase		)

								self.assertListEqual(_lowercase			,			_lowercase		)







						def __a						(							self   :    Union[str, Any]		):
								A					       = self.get_image_processor()
								A					       = self.get_tokenizer()

								A					       = AlignProcessor(tokenizer=_lowercase			,			image_processor=_lowercase		)

								A					       = 'lower newer'
								A					       = self.prepare_image_inputs()

								A					       = processor(text=_lowercase			,			images=_lowercase		)

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









"""simple docstring"""


def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = " "	)      ->		list:







		"""simple docstring"""





		A					       = []

		A					       = 0
		for index, char in enumerate(UpperCamelCase__	):
				if char == separator:
						split_words.append(string[last_index:index]	)
						A					       = index + 1
				elif index + 1 == len(UpperCamelCase__	):
						split_words.append(string[last_index : index + 1]	)
		return split_words


if __name__ == "__main__":
	from doctest import testmod

	testmod()





