




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  ):

    '''simple docstring'''



    def 					_lowercase  (					self  )  ->   Dict:
           lowerCamelCase     :   Tuple    				=      tempfile.mkdtemp()

           # fmt: off
           lowerCamelCase     :   Dict    				=      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
           # fmt: on
           lowerCamelCase     :   str    				=      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]  )  )

           lowerCamelCase     :   Any    				=      {
               "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,
           }

           lowerCamelCase     :   Any    				=      os.path.join(self.tmpdirname  ,						UpperCamelCase__  )
           with open(self.image_processor_file  ,						"w"  ,						encoding="utf-8"  ) as fp:
                  json.dump(UpperCamelCase__  ,						UpperCamelCase__  )







    def 					_lowercase  (					self  ,						**UpperCamelCase__  )  ->   Dict:
           return BertTokenizer.from_pretrained(self.tmpdirname  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						**UpperCamelCase__  )  ->   Tuple:
           return BertTokenizerFast.from_pretrained(self.tmpdirname  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						**UpperCamelCase__  )  ->   Dict:
           return FlavaImageProcessor.from_pretrained(self.tmpdirname  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   str:
           shutil.rmtree(self.tmpdirname  )







    def 					_lowercase  (					self  )  ->   List[Any]:
           lowerCamelCase     :   Union[str, Any]    				=      [np.random.randint(255  ,						size=(3, 30, 400)  ,						dtype=np.uinta  )]

           lowerCamelCase     :   int    				=      [Image.fromarray(np.moveaxis(UpperCamelCase__  ,						0  ,						-1  )  ) for x in image_inputs]

           return image_inputs







    def 					_lowercase  (					self  )  ->   Any:
           lowerCamelCase     :   Union[str, Any]    				=      self.get_tokenizer()
           lowerCamelCase     :   Dict    				=      self.get_rust_tokenizer()
           lowerCamelCase     :   Optional[int]    				=      self.get_image_processor()

           lowerCamelCase     :   Optional[int]    				=      FlavaProcessor(tokenizer=UpperCamelCase__  ,						image_processor=UpperCamelCase__  )
           processor_slow.save_pretrained(self.tmpdirname  )
           lowerCamelCase     :   int    				=      FlavaProcessor.from_pretrained(self.tmpdirname  ,						use_fast=UpperCamelCase__  )

           lowerCamelCase     :   Any    				=      FlavaProcessor(tokenizer=UpperCamelCase__  ,						image_processor=UpperCamelCase__  )
           processor_fast.save_pretrained(self.tmpdirname  )
           lowerCamelCase     :   str    				=      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  ,						UpperCamelCase__  )
           self.assertIsInstance(processor_fast.tokenizer  ,						UpperCamelCase__  )

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







    def 					_lowercase  (					self  )  ->   Optional[Any]:
           lowerCamelCase     :   Any    				=      FlavaProcessor(tokenizer=self.get_tokenizer()  ,						image_processor=self.get_image_processor()  )
           processor.save_pretrained(self.tmpdirname  )

           lowerCamelCase     :   Dict    				=      self.get_tokenizer(bos_token="(BOS)"  ,						eos_token="(EOS)"  )
           lowerCamelCase     :   Union[str, Any]    				=      self.get_image_processor(do_normalize=UpperCamelCase__  ,						padding_value=1.0  )

           lowerCamelCase     :   Dict    				=      FlavaProcessor.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  ,						UpperCamelCase__  )

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







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

           lowerCamelCase     :   List[str]    				=      FlavaProcessor(tokenizer=UpperCamelCase__  ,						image_processor=UpperCamelCase__  )

           lowerCamelCase     :   str    				=      self.prepare_image_inputs()

           lowerCamelCase     :   List[str]    				=      image_processor(UpperCamelCase__  ,						return_tensors="np"  )
           lowerCamelCase     :   Optional[Any]    				=      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  )

           # With rest of the args
           random.seed(1234  )
           lowerCamelCase     :   str    				=      image_processor(
               UpperCamelCase__  ,						return_image_mask=UpperCamelCase__  ,						return_codebook_pixels=UpperCamelCase__  ,						return_tensors="np"  )
           random.seed(1234  )
           lowerCamelCase     :   Tuple    				=      processor(
               images=UpperCamelCase__  ,						return_image_mask=UpperCamelCase__  ,						return_codebook_pixels=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 					_lowercase  (					self  )  ->   Tuple:
           lowerCamelCase     :   int    				=      self.get_image_processor()
           lowerCamelCase     :   List[str]    				=      self.get_tokenizer()

           lowerCamelCase     :   Any    				=      FlavaProcessor(tokenizer=UpperCamelCase__  ,						image_processor=UpperCamelCase__  )

           lowerCamelCase     :   Optional[int]    				=      "lower newer"

           lowerCamelCase     :   Union[str, Any]    				=      processor(text=UpperCamelCase__  )

           lowerCamelCase     :   Optional[Any]    				=      tokenizer(UpperCamelCase__  )

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







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

           lowerCamelCase     :   List[str]    				=      FlavaProcessor(tokenizer=UpperCamelCase__  ,						image_processor=UpperCamelCase__  )

           lowerCamelCase     :   Dict    				=      "lower newer"
           lowerCamelCase     :   Any    				=      self.prepare_image_inputs()

           lowerCamelCase     :   Tuple    				=      processor(text=UpperCamelCase__  ,						images=UpperCamelCase__  )

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

           # add extra args
           lowerCamelCase     :   str    				=      processor(text=UpperCamelCase__  ,						images=UpperCamelCase__  ,						return_codebook_pixels=UpperCamelCase__  ,						return_image_mask=UpperCamelCase__  )

           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(UpperCamelCase__  ):
                  processor()







    def 					_lowercase  (					self  )  ->   Optional[Any]:
           lowerCamelCase     :   Tuple    				=      self.get_image_processor()
           lowerCamelCase     :   List[str]    				=      self.get_tokenizer()

           lowerCamelCase     :   Dict    				=      FlavaProcessor(tokenizer=UpperCamelCase__  ,						image_processor=UpperCamelCase__  )

           lowerCamelCase     :   Tuple    				=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowerCamelCase     :   Optional[int]    				=      processor.batch_decode(UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      tokenizer.batch_decode(UpperCamelCase__  )

           self.assertListEqual(UpperCamelCase__  ,						UpperCamelCase__  )







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

           lowerCamelCase     :   Optional[int]    				=      FlavaProcessor(tokenizer=UpperCamelCase__  ,						image_processor=UpperCamelCase__  )

           lowerCamelCase     :   int    				=      "lower newer"
           lowerCamelCase     :   str    				=      self.prepare_image_inputs()

           lowerCamelCase     :   Union[str, Any]    				=      processor(text=UpperCamelCase__  ,						images=UpperCamelCase__  )

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












from itertools import compress, repeat
from math import ceil, sqrt

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:

       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       lowerCamelCase     :   str    				=      bytearray(B"\x01"					) * ((num >> 1) - 1)

       for i in range(3				,int(sqrt(_SCREAMING_SNAKE_CASE					)					) + 1				,2					):
              if sieve[(i >> 1) - 1]:
                     lowerCamelCase     :   Tuple    				=      i**2
                     lowerCamelCase     :   str    				=      repeat(
                         0				,ceil((num - i_squared) / (i << 1)					)					)

       return [2] + list(compress(range(3				,_SCREAMING_SNAKE_CASE				,2					)				,_SCREAMING_SNAKE_CASE					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






