"""simple docstring"""







import json
import os
import random
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 FlavaImageProcessor, FlavaProcessor
	from transformers.models.flava.image_processing_flava import (
	    FLAVA_CODEBOOK_MEAN,
	    FLAVA_CODEBOOK_STD,
	    FLAVA_IMAGE_MEAN,
	    FLAVA_IMAGE_STD,
	)

@require_vision
class 						UpperCamelCase     (       unittest.TestCase		):



				def    _lowercase	(self							: List[Any])       ->      Union[str, Any]:
					__snake_case     :				int 						=					tempfile.mkdtemp()

					# fmt: off
					__snake_case     :				Optional[Any] 						=					['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
					# fmt: on
					__snake_case     :				Dict 						=					os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES['vocab_file'])

					with open(self.vocab_file      ,    'w'      ,    encoding='utf-8') as fp:
						fp.write(''.join([x + '\n' for x in vocab_tokens]))

					__snake_case     :				Union[str, Any] 						=					{
					    'image_mean': FLAVA_IMAGE_MEAN,
					    'image_std': FLAVA_IMAGE_STD,
					    'do_normalize': True,
					    'do_resize': True,
					    'size': 2_24,
					    'do_center_crop': True,
					    'crop_size': 2_24,
					    'input_size_patches': 14,
					    'total_mask_patches': 75,
					    'mask_group_max_patches': None,
					    'mask_group_min_patches': 16,
					    'mask_group_min_aspect_ratio': 0.3,
					    'mask_group_max_aspect_ratio': None,
					    'codebook_do_resize': True,
					    'codebook_size': 1_12,
					    'codebook_do_center_crop': True,
					    'codebook_crop_size': 1_12,
					    'codebook_do_map_pixels': True,
					    'codebook_do_normalize': True,
					    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
					    'codebook_image_std': FLAVA_CODEBOOK_STD,
					}

					__snake_case     :				List[Any] 						=					os.path.join(self.tmpdirname      ,    _A)
					with open(self.image_processor_file      ,    'w'      ,    encoding='utf-8') as fp:
						json.dump(_A      ,    _A)



				def    _lowercase	(self							: Any      ,    **_A							: List[Any])       ->      Optional[int]:
					return BertTokenizer.from_pretrained(self.tmpdirname      ,    **_A)



				def    _lowercase	(self							: Tuple      ,    **_A							: Optional[Any])       ->      Any:
					return BertTokenizerFast.from_pretrained(self.tmpdirname      ,    **_A)



				def    _lowercase	(self							: Optional[int]      ,    **_A							: Any)       ->      Any:
					return FlavaImageProcessor.from_pretrained(self.tmpdirname      ,    **_A)



				def    _lowercase	(self							: Optional[Any])       ->      Union[str, Any]:
					shutil.rmtree(self.tmpdirname)



				def    _lowercase	(self							: Optional[int])       ->      Dict:
					__snake_case     :				Any 						=					[np.random.randint(2_55      ,    size=(3, 30, 4_00)      ,    dtype=np.uinta)]

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

					return image_inputs



				def    _lowercase	(self							: Dict)       ->      Any:
					__snake_case     :				List[str] 						=					self.get_tokenizer()
					__snake_case     :				List[Any] 						=					self.get_rust_tokenizer()
					__snake_case     :				int 						=					self.get_image_processor()

					__snake_case     :				Optional[Any] 						=					FlavaProcessor(tokenizer=_A      ,    image_processor=_A)
					processor_slow.save_pretrained(self.tmpdirname)
					__snake_case     :				List[Any] 						=					FlavaProcessor.from_pretrained(self.tmpdirname      ,    use_fast=_A)

					__snake_case     :				Any 						=					FlavaProcessor(tokenizer=_A      ,    image_processor=_A)
					processor_fast.save_pretrained(self.tmpdirname)
					__snake_case     :				int 						=					FlavaProcessor.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      ,    _A)
					self.assertIsInstance(processor_fast.tokenizer      ,    _A)

					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      ,    _A)
					self.assertIsInstance(processor_fast.image_processor      ,    _A)



				def    _lowercase	(self							: Optional[int])       ->      int:
					__snake_case     :				List[str] 						=					FlavaProcessor(tokenizer=self.get_tokenizer()      ,    image_processor=self.get_image_processor())
					processor.save_pretrained(self.tmpdirname)

					__snake_case     :				str 						=					self.get_tokenizer(bos_token='(BOS)'      ,    eos_token='(EOS)')
					__snake_case     :				int 						=					self.get_image_processor(do_normalize=_A      ,    padding_value=1.0)

					__snake_case     :				Optional[int] 						=					FlavaProcessor.from_pretrained(
					    self.tmpdirname      ,    bos_token='(BOS)'      ,    eos_token='(EOS)'      ,    do_normalize=_A      ,    padding_value=1.0)

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

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



				def    _lowercase	(self							: Any)       ->      List[str]:
					__snake_case     :				int 						=					self.get_image_processor()
					__snake_case     :				Union[str, Any] 						=					self.get_tokenizer()

					__snake_case     :				Any 						=					FlavaProcessor(tokenizer=_A      ,    image_processor=_A)

					__snake_case     :				List[Any] 						=					self.prepare_image_inputs()

					__snake_case     :				Any 						=					image_processor(_A      ,    return_tensors='np')
					__snake_case     :				Optional[Any] 						=					processor(images=_A      ,    return_tensors='np')

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

					# With rest of the args
					random.seed(12_34)
					__snake_case     :				List[str] 						=					image_processor(
					    _A      ,    return_image_mask=_A      ,    return_codebook_pixels=_A      ,    return_tensors='np')
					random.seed(12_34)
					__snake_case     :				List[Any] 						=					processor(
					    images=_A      ,    return_image_mask=_A      ,    return_codebook_pixels=_A      ,    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    _lowercase	(self							: List[str])       ->      Dict:
					__snake_case     :				List[Any] 						=					self.get_image_processor()
					__snake_case     :				Any 						=					self.get_tokenizer()

					__snake_case     :				Optional[Any] 						=					FlavaProcessor(tokenizer=_A      ,    image_processor=_A)

					__snake_case     :				Tuple 						=					'lower newer'

					__snake_case     :				List[str] 						=					processor(text=_A)

					__snake_case     :				Union[str, Any] 						=					tokenizer(_A)

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



				def    _lowercase	(self							: Optional[Any])       ->      Optional[int]:
					__snake_case     :				Optional[int] 						=					self.get_image_processor()
					__snake_case     :				Tuple 						=					self.get_tokenizer()

					__snake_case     :				List[Any] 						=					FlavaProcessor(tokenizer=_A      ,    image_processor=_A)

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

					__snake_case     :				int 						=					processor(text=_A      ,    images=_A)

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

					# add extra args
					__snake_case     :				Dict 						=					processor(text=_A      ,    images=_A      ,    return_codebook_pixels=_A      ,    return_image_mask=_A)

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

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



				def    _lowercase	(self							: List[str])       ->      Dict:
					__snake_case     :				int 						=					self.get_image_processor()
					__snake_case     :				Dict 						=					self.get_tokenizer()

					__snake_case     :				List[Any] 						=					FlavaProcessor(tokenizer=_A      ,    image_processor=_A)

					__snake_case     :				Optional[Any] 						=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__snake_case     :				Optional[Any] 						=					processor.batch_decode(_A)
					__snake_case     :				str 						=					tokenizer.batch_decode(_A)

					self.assertListEqual(_A      ,    _A)






				def    _lowercase	(self							: int)       ->      Tuple:
					__snake_case     :				Optional[Any] 						=					self.get_image_processor()
					__snake_case     :				Optional[Any] 						=					self.get_tokenizer()

					__snake_case     :				Optional[Any] 						=					FlavaProcessor(tokenizer=_A      ,    image_processor=_A)

					__snake_case     :				Optional[Any] 						=					'lower newer'
					__snake_case     :				str 						=					self.prepare_image_inputs()

					__snake_case     :				Dict 						=					processor(text=_A      ,    images=_A)

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






"""simple docstring"""







def     __UpperCAmelCase							(       UpperCAmelCase_       :							str    )				->     str:

	'''simple docstring'''





	return " ".join(sorted(set(sentence.split()    )    )    )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





