
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 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      tempfile.mkdtemp()

													# fmt: off
													__UpperCamelCase  	=      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
													# fmt: on
													__UpperCamelCase  	=      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]      )      )

													__UpperCamelCase  	=      {
													    '''image_mean''': FLAVA_IMAGE_MEAN,
													    '''image_std''': FLAVA_IMAGE_STD,
													    '''do_normalize''': True,
													    '''do_resize''': True,
													    '''size''': 224,
													    '''do_center_crop''': True,
													    '''crop_size''': 224,
													    '''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''': 112,
													    '''codebook_do_center_crop''': True,
													    '''codebook_crop_size''': 112,
													    '''codebook_do_map_pixels''': True,
													    '''codebook_do_normalize''': True,
													    '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
													    '''codebook_image_std''': FLAVA_CODEBOOK_STD,
													}

													__UpperCamelCase  	=      os.path.join(self.tmpdirname  ,						SCREAMING_SNAKE_CASE_      )
													with open(self.image_processor_file  ,						'''w'''  ,						encoding='''utf-8'''      ) as fp:
																			json.dump(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						**SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






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



							def      A__		(	self  ,						**SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													return BertTokenizerFast.from_pretrained(self.tmpdirname  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						**SCREAMING_SNAKE_CASE_      )-> Any:





													'''simple docstring'''






													return FlavaImageProcessor.from_pretrained(self.tmpdirname  ,						**SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname      )



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      [np.random.randint(255  ,						size=(3, 30, 400)  ,						dtype=np.uinta      )]

													__UpperCamelCase  	=      [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_  ,						0  ,						-1      )      ) for x in image_inputs]

													return image_inputs



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_tokenizer()
													__UpperCamelCase  	=      self.get_rust_tokenizer()
													__UpperCamelCase  	=      self.get_image_processor()

													__UpperCamelCase  	=      FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )
													processor_slow.save_pretrained(self.tmpdirname      )
													__UpperCamelCase  	=      FlavaProcessor.from_pretrained(self.tmpdirname  ,						use_fast=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )
													processor_fast.save_pretrained(self.tmpdirname      )
													__UpperCamelCase  	=      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  ,						SCREAMING_SNAKE_CASE_      )
													self.assertIsInstance(processor_fast.tokenizer  ,						SCREAMING_SNAKE_CASE_      )

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



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      FlavaProcessor(tokenizer=self.get_tokenizer()  ,						image_processor=self.get_image_processor()      )
													processor.save_pretrained(self.tmpdirname      )

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

													__UpperCamelCase  	=      FlavaProcessor.from_pretrained(
													    self.tmpdirname  ,						bos_token='''(BOS)'''  ,						eos_token='''(EOS)'''  ,						do_normalize=SCREAMING_SNAKE_CASE_  ,						padding_value=1.0      )

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

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



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_image_processor()
													__UpperCamelCase  	=      self.get_tokenizer()

													__UpperCamelCase  	=      FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      self.prepare_image_inputs()

													__UpperCamelCase  	=      image_processor(SCREAMING_SNAKE_CASE_  ,						return_tensors='''np'''      )
													__UpperCamelCase  	=      processor(images=SCREAMING_SNAKE_CASE_  ,						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(1234      )
													__UpperCamelCase  	=      image_processor(
													    SCREAMING_SNAKE_CASE_  ,						return_image_mask=SCREAMING_SNAKE_CASE_  ,						return_codebook_pixels=SCREAMING_SNAKE_CASE_  ,						return_tensors='''np'''      )
													random.seed(1234      )
													__UpperCamelCase  	=      processor(
													    images=SCREAMING_SNAKE_CASE_  ,						return_image_mask=SCREAMING_SNAKE_CASE_  ,						return_codebook_pixels=SCREAMING_SNAKE_CASE_  ,						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      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_image_processor()
													__UpperCamelCase  	=      self.get_tokenizer()

													__UpperCamelCase  	=      FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      '''lower newer'''

													__UpperCamelCase  	=      processor(text=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      tokenizer(SCREAMING_SNAKE_CASE_      )

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



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_image_processor()
													__UpperCamelCase  	=      self.get_tokenizer()

													__UpperCamelCase  	=      FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )

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

													__UpperCamelCase  	=      processor(text=SCREAMING_SNAKE_CASE_  ,						images=SCREAMING_SNAKE_CASE_      )

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

													# add extra args
													__UpperCamelCase  	=      processor(text=SCREAMING_SNAKE_CASE_  ,						images=SCREAMING_SNAKE_CASE_  ,						return_codebook_pixels=SCREAMING_SNAKE_CASE_  ,						return_image_mask=SCREAMING_SNAKE_CASE_      )

													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(SCREAMING_SNAKE_CASE_      ):
																			processor()



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_image_processor()
													__UpperCamelCase  	=      self.get_tokenizer()

													__UpperCamelCase  	=      FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )

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

													__UpperCamelCase  	=      processor.batch_decode(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_      )

													self.assertListEqual(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      self.get_image_processor()
													__UpperCamelCase  	=      self.get_tokenizer()

													__UpperCamelCase  	=      FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,						image_processor=SCREAMING_SNAKE_CASE_      )

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

													__UpperCamelCase  	=      processor(text=SCREAMING_SNAKE_CASE_  ,						images=SCREAMING_SNAKE_CASE_      )

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






'''simple docstring'''







def   lowerCAmelCase(  a__    :  int = 1000      ):







       '''simple docstring'''






       lowerCamelCase__				   =      0
       for i in range(a__      ):
              if i % 3 == 0 or i % 5 == 0:
                     result += i
       return result


if __name__ == "__main__":
    print(f'{solution() = }')

