



'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    A__  :					Optional[Any]				= False

if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        PixaStructImageProcessor,
        PixaStructProcessor,
        PreTrainedTokenizerFast,
        TaTokenizer,
    )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,			reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''   ,			)
@require_vision
@require_torch
class       snake_case__		(  unittest.TestCase							):
 def A_					(					self		:				Tuple   )	->					Dict:







       '''simple docstring'''
       __snake_case      :					Union[str, Any]						=  tempfile.mkdtemp()

       __snake_case      :					Union[str, Any]						=  PixaStructImageProcessor()
       __snake_case      :					int						=  TaTokenizer.from_pretrained('t5-small'   )

       __snake_case      :					List[Any]						=  PixaStructProcessor(_lowerCamelCase	,			_lowerCamelCase   )

       processor.save_pretrained(self.tmpdirname   )
 def A_					(					self		:				List[str]	,			**__a		:				Optional[Any]   )	->					Optional[int]:







       '''simple docstring'''
       return AutoProcessor.from_pretrained(self.tmpdirname	,			**_lowerCamelCase   ).tokenizer
 def A_					(					self		:				Any	,			**__a		:				Optional[int]   )	->					Any:







       '''simple docstring'''
       return AutoProcessor.from_pretrained(self.tmpdirname	,			**_lowerCamelCase   ).image_processor
 def A_					(					self		:				Tuple   )	->					int:







       '''simple docstring'''
       shutil.rmtree(self.tmpdirname   )
 def A_					(					self		:				Optional[int]   )	->					int:







       '''simple docstring'''
       __snake_case      :					Optional[Any]						=  [np.random.randint(255	,			size=(3, 30, 400)	,			dtype=np.uinta   )]

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

       return image_inputs
 def A_					(					self		:				List[str]   )	->					Union[str, Any]:







       '''simple docstring'''
       __snake_case      :					int						=  PixaStructProcessor(tokenizer=self.get_tokenizer()	,			image_processor=self.get_image_processor()   )
       processor.save_pretrained(self.tmpdirname   )

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

       __snake_case      :					List[Any]						=  PixaStructProcessor.from_pretrained(
           self.tmpdirname	,			bos_token='(BOS)'	,			eos_token='(EOS)'	,			do_normalize=_lowerCamelCase	,			padding_value=1.0   )

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

       self.assertEqual(processor.image_processor.to_json_string()	,			image_processor_add_kwargs.to_json_string()   )
       self.assertIsInstance(processor.image_processor	,			_lowerCamelCase   )
 def A_					(					self		:				Optional[int]   )	->					List[Any]:







       '''simple docstring'''
       __snake_case      :					str						=  self.get_image_processor()
       __snake_case      :					Optional[int]						=  self.get_tokenizer()

       __snake_case      :					Optional[Any]						=  PixaStructProcessor(tokenizer=_lowerCamelCase	,			image_processor=_lowerCamelCase   )

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

       __snake_case      :					Optional[Any]						=  image_processor(_lowerCamelCase	,			return_tensors='np'   )
       __snake_case      :					List[str]						=  processor(images=_lowerCamelCase	,			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		:				Optional[int]   )	->					Union[str, Any]:







       '''simple docstring'''
       __snake_case      :					Optional[Any]						=  self.get_image_processor()
       __snake_case      :					int						=  self.get_tokenizer()

       __snake_case      :					Union[str, Any]						=  PixaStructProcessor(tokenizer=_lowerCamelCase	,			image_processor=_lowerCamelCase   )

       __snake_case      :					Dict						=  '''lower newer'''

       __snake_case      :					Dict						=  processor(text=_lowerCamelCase   )

       __snake_case      :					List[Any]						=  tokenizer(_lowerCamelCase	,			return_token_type_ids=_lowerCamelCase	,			add_special_tokens=_lowerCamelCase   )

       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]	,			encoded_processor[key]   )
 def A_					(					self		:				Optional[Any]   )	->					Optional[Any]:







       '''simple docstring'''
       __snake_case      :					Union[str, Any]						=  self.get_image_processor()
       __snake_case      :					Any						=  self.get_tokenizer()

       __snake_case      :					List[Any]						=  PixaStructProcessor(tokenizer=_lowerCamelCase	,			image_processor=_lowerCamelCase   )

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

       __snake_case      :					List[Any]						=  processor(text=_lowerCamelCase	,			images=_lowerCamelCase   )

       self.assertListEqual(
           list(inputs.keys()   )	,			['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']   )

       # test if it raises when no input is passed
       with pytest.raises(_lowerCamelCase   ):
             processor()
 def A_					(					self		:				List[str]   )	->					Any:







       '''simple docstring'''
       __snake_case      :					str						=  self.get_image_processor()
       __snake_case      :					Any						=  self.get_tokenizer()

       __snake_case      :					Optional[int]						=  PixaStructProcessor(tokenizer=_lowerCamelCase	,			image_processor=_lowerCamelCase   )

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

       __snake_case      :					str						=  processor(text=_lowerCamelCase	,			images=_lowerCamelCase   )

       __snake_case      :					str						=  [512, 1024, 2048, 4096]
       __snake_case      :					List[Any]						=  [770, 770, 770, 770]
       # with text
       for i, max_patch in enumerate(_lowerCamelCase   ):
             __snake_case      :					Optional[int]						=  processor(text=_lowerCamelCase	,			images=_lowerCamelCase	,			max_patches=_lowerCamelCase   )
             self.assertEqual(inputs['flattened_patches'][0].shape[0]	,			_lowerCamelCase   )
             self.assertEqual(inputs['flattened_patches'][0].shape[1]	,			expected_hidden_size[i]   )

       # without text input
       for i, max_patch in enumerate(_lowerCamelCase   ):
             __snake_case      :					Optional[int]						=  processor(images=_lowerCamelCase	,			max_patches=_lowerCamelCase   )
             self.assertEqual(inputs['flattened_patches'][0].shape[0]	,			_lowerCamelCase   )
             self.assertEqual(inputs['flattened_patches'][0].shape[1]	,			expected_hidden_size[i]   )
 def A_					(					self		:				List[str]   )	->					str:







       '''simple docstring'''
       __snake_case      :					Dict						=  self.get_image_processor()
       __snake_case      :					Union[str, Any]						=  self.get_tokenizer()

       __snake_case      :					Optional[int]						=  PixaStructProcessor(tokenizer=_lowerCamelCase	,			image_processor=_lowerCamelCase   )

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

       __snake_case      :					Optional[int]						=  processor.batch_decode(_lowerCamelCase   )
       __snake_case      :					Tuple						=  tokenizer.batch_decode(_lowerCamelCase   )

       self.assertListEqual(_lowerCamelCase	,			_lowerCamelCase   )





 def A_					(					self		:				Dict   )	->					Union[str, Any]:







       '''simple docstring'''
       __snake_case      :					List[Any]						=  self.get_image_processor()
       __snake_case      :					Optional[Any]						=  self.get_tokenizer()

       __snake_case      :					List[Any]						=  PixaStructProcessor(tokenizer=_lowerCamelCase	,			image_processor=_lowerCamelCase   )

       __snake_case      :					Dict						=  '''lower newer'''
       __snake_case      :					List[Any]						=  self.prepare_image_inputs()

       __snake_case      :					str						=  processor(text=_lowerCamelCase	,			images=_lowerCamelCase   )

       # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
       self.assertListEqual(
           list(inputs.keys()   )	,			['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']   )

       __snake_case      :					Optional[int]						=  processor(text=_lowerCamelCase   )

       # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
       self.assertListEqual(list(inputs.keys()   )	,			['input_ids', 'attention_mask']   )










"""simple docstring"""





import math




def 		lowerCAmelCase_	(  lowercase_   :							int		):

 '''simple docstring'''






 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5 ,      int(math.sqrt(lowercase_		) + 1		) ,      6		):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True




def 		lowerCAmelCase_	(  lowercase_   :							int = 6008_5147_5143		):

 '''simple docstring'''






 try:
  __SCREAMING_SNAKE_CASE :			Any												=       int(lowercase_		)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''		)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       0
 if is_prime(lowercase_		):
  return n
 while n % 2 == 0:
  n //= 2
 if is_prime(lowercase_		):
  return n
 for i in range(3 ,      int(math.sqrt(lowercase_		)		) + 1 ,      2		):
  if n % i == 0:
   if is_prime(n // i		):
    __SCREAMING_SNAKE_CASE :			Optional[int]												=       n // i
    break
   elif is_prime(lowercase_		):
    __SCREAMING_SNAKE_CASE :			Any												=       i
 return max_number


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

