


'''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__		: Dict												=  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        UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def 				lowercase_    (							self     )    ->					int:
         __lowerCamelCase       :     List[Any]        =		tempfile.mkdtemp()

         __lowerCamelCase       :     Optional[Any]        =		PixaStructImageProcessor()
         __lowerCamelCase       :     List[str]        =		TaTokenizer.from_pretrained('t5-small'     )

         __lowerCamelCase       :     List[str]        =		PixaStructProcessor(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )

         processor.save_pretrained(self.tmpdirname     )







    def 				lowercase_    (							self      ,					**SCREAMING_SNAKE_CASE_     )    ->					Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname      ,					**SCREAMING_SNAKE_CASE_     ).tokenizer







    def 				lowercase_    (							self      ,					**SCREAMING_SNAKE_CASE_     )    ->					str:
         return AutoProcessor.from_pretrained(self.tmpdirname      ,					**SCREAMING_SNAKE_CASE_     ).image_processor







    def 				lowercase_    (							self     )    ->					Optional[Any]:
         shutil.rmtree(self.tmpdirname     )







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

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

         return image_inputs







    def 				lowercase_    (							self     )    ->					Optional[int]:
         __lowerCamelCase       :     Optional[Any]        =		PixaStructProcessor(tokenizer=self.get_tokenizer()      ,					image_processor=self.get_image_processor()     )
         processor.save_pretrained(self.tmpdirname     )

         __lowerCamelCase       :     Optional[int]        =		self.get_tokenizer(bos_token='(BOS)'      ,					eos_token='(EOS)'     )
         __lowerCamelCase       :     List[str]        =		self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_      ,					padding_value=1.0     )

         __lowerCamelCase       :     int        =		PixaStructProcessor.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 				lowercase_    (							self     )    ->					int:
         __lowerCamelCase       :     str        =		self.get_image_processor()
         __lowerCamelCase       :     List[Any]        =		self.get_tokenizer()

         __lowerCamelCase       :     Any        =		PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_      ,					image_processor=SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     str        =		self.prepare_image_inputs()

         __lowerCamelCase       :     Any        =		image_processor(SCREAMING_SNAKE_CASE_      ,					return_tensors='np'     )
         __lowerCamelCase       :     Dict        =		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     )







    def 				lowercase_    (							self     )    ->					str:
         __lowerCamelCase       :     Any        =		self.get_image_processor()
         __lowerCamelCase       :     Tuple        =		self.get_tokenizer()

         __lowerCamelCase       :     Union[str, Any]        =		PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_      ,					image_processor=SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     List[Any]        =		'lower newer'

         __lowerCamelCase       :     Union[str, Any]        =		processor(text=SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Tuple        =		tokenizer(SCREAMING_SNAKE_CASE_      ,					return_token_type_ids=SCREAMING_SNAKE_CASE_      ,					add_special_tokens=SCREAMING_SNAKE_CASE_     )

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







    def 				lowercase_    (							self     )    ->					List[str]:
         __lowerCamelCase       :     Dict        =		self.get_image_processor()
         __lowerCamelCase       :     Dict        =		self.get_tokenizer()

         __lowerCamelCase       :     List[str]        =		PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_      ,					image_processor=SCREAMING_SNAKE_CASE_     )

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

         __lowerCamelCase       :     int        =		processor(text=SCREAMING_SNAKE_CASE_      ,					images=SCREAMING_SNAKE_CASE_     )

         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(SCREAMING_SNAKE_CASE_     ):
              processor()







    def 				lowercase_    (							self     )    ->					Dict:
         __lowerCamelCase       :     List[str]        =		self.get_image_processor()
         __lowerCamelCase       :     Any        =		self.get_tokenizer()

         __lowerCamelCase       :     Optional[Any]        =		PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_      ,					image_processor=SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Any        =		'lower newer'
         __lowerCamelCase       :     List[str]        =		self.prepare_image_inputs()

         __lowerCamelCase       :     Optional[int]        =		processor(text=SCREAMING_SNAKE_CASE_      ,					images=SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Tuple        =		[5_12, 10_24, 20_48, 40_96]
         __lowerCamelCase       :     List[str]        =		[7_70, 7_70, 7_70, 7_70]
         # with text
         for i, max_patch in enumerate(SCREAMING_SNAKE_CASE_     ):
              __lowerCamelCase       :     List[Any]        =		processor(text=SCREAMING_SNAKE_CASE_      ,					images=SCREAMING_SNAKE_CASE_      ,					max_patches=SCREAMING_SNAKE_CASE_     )
              self.assertEqual(inputs['flattened_patches'][0].shape[0]      ,					SCREAMING_SNAKE_CASE_     )
              self.assertEqual(inputs['flattened_patches'][0].shape[1]      ,					expected_hidden_size[i]     )

         # without text input
         for i, max_patch in enumerate(SCREAMING_SNAKE_CASE_     ):
              __lowerCamelCase       :     Optional[Any]        =		processor(images=SCREAMING_SNAKE_CASE_      ,					max_patches=SCREAMING_SNAKE_CASE_     )
              self.assertEqual(inputs['flattened_patches'][0].shape[0]      ,					SCREAMING_SNAKE_CASE_     )
              self.assertEqual(inputs['flattened_patches'][0].shape[1]      ,					expected_hidden_size[i]     )







    def 				lowercase_    (							self     )    ->					Union[str, Any]:
         __lowerCamelCase       :     List[Any]        =		self.get_image_processor()
         __lowerCamelCase       :     str        =		self.get_tokenizer()

         __lowerCamelCase       :     str        =		PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_      ,					image_processor=SCREAMING_SNAKE_CASE_     )

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

         __lowerCamelCase       :     List[str]        =		processor.batch_decode(SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     str        =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )

         self.assertListEqual(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self     )    ->					str:
         __lowerCamelCase       :     Tuple        =		self.get_image_processor()
         __lowerCamelCase       :     int        =		self.get_tokenizer()

         __lowerCamelCase       :     List[Any]        =		PixaStructProcessor(tokenizer=SCREAMING_SNAKE_CASE_      ,					image_processor=SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     List[str]        =		'lower newer'
         __lowerCamelCase       :     Optional[int]        =		self.prepare_image_inputs()

         __lowerCamelCase       :     Optional[int]        =		processor(text=SCREAMING_SNAKE_CASE_      ,					images=SCREAMING_SNAKE_CASE_     )

         # 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']     )

         __lowerCamelCase       :     Optional[Any]        =		processor(text=SCREAMING_SNAKE_CASE_     )

         # 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'''



def       UpperCAmelCase__ (     UpperCAmelCase_   :    int = 4_00_00_00							)					->		int:
     __lowerCamelCase       :     int        =		1
     __lowerCamelCase       :     Union[str, Any]        =		2
     __lowerCamelCase       :     Optional[int]        =		0
     while j <= n:
          if j % 2 == 0:
               total += j
          __lowerCamelCase    ,	__lowerCamelCase       :     List[Any]        =		j, i + j

     return total


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






