



'''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:
   _UpperCamelCase							    =						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  lowerCamelCase_   (    unittest.TestCase					):
  """simple docstring"""




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

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

    __lowerCamelCase	:	Any   	=			PixaStructProcessor(_a      ,     _a       )

    processor.save_pretrained(self.tmpdirname       )




  def 						_lowercase					(			self				:     str      ,     **_a				:     int       ) -> int:
    return AutoProcessor.from_pretrained(self.tmpdirname      ,     **_a       ).tokenizer




  def 						_lowercase					(			self				:     Any      ,     **_a				:     Dict       ) -> Optional[int]:
    return AutoProcessor.from_pretrained(self.tmpdirname      ,     **_a       ).image_processor




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




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

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

    return image_inputs




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

    __lowerCamelCase	:	Tuple   	=			self.get_tokenizer(bos_token='(BOS)'      ,     eos_token='(EOS)'       )
    __lowerCamelCase	:	Dict   	=			self.get_image_processor(do_normalize=_a      ,     padding_value=1.0       )

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

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

    __lowerCamelCase	:	List[str]   	=			self.prepare_image_inputs()

    __lowerCamelCase	:	Tuple   	=			image_processor(_a      ,     return_tensors='np'       )
    __lowerCamelCase	:	Tuple   	=			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       )




  def 						_lowercase					(			self				:     str       ) -> int:
    __lowerCamelCase	:	Dict   	=			self.get_image_processor()
    __lowerCamelCase	:	str   	=			self.get_tokenizer()

    __lowerCamelCase	:	int   	=			PixaStructProcessor(tokenizer=_a      ,     image_processor=_a       )

    __lowerCamelCase	:	Tuple   	=			'lower newer'

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

    __lowerCamelCase	:	Union[str, Any]   	=			tokenizer(_a      ,     return_token_type_ids=_a      ,     add_special_tokens=_a       )

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




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

    __lowerCamelCase	:	Tuple   	=			PixaStructProcessor(tokenizer=_a      ,     image_processor=_a       )

    __lowerCamelCase	:	Any   	=			'lower newer'
    __lowerCamelCase	:	int   	=			self.prepare_image_inputs()

    __lowerCamelCase	:	Dict   	=			processor(text=_a      ,     images=_a       )

    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(_a       ):
      processor()




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

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

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

    __lowerCamelCase	:	Any   	=			processor(text=_a      ,     images=_a       )

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

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




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

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

    __lowerCamelCase	:	List[str]   	=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    __lowerCamelCase	:	Tuple   	=			processor.batch_decode(_a       )
    __lowerCamelCase	:	Optional[int]   	=			tokenizer.batch_decode(_a       )

    self.assertListEqual(_a      ,     _a       )




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

    __lowerCamelCase	:	Dict   	=			PixaStructProcessor(tokenizer=_a      ,     image_processor=_a       )

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

    __lowerCamelCase	:	Any   	=			processor(text=_a      ,     images=_a       )

    # 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	:	int   	=			processor(text=_a       )

    # 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
from timeit import timeit

def 	a_					(							_lowerCAmelCase	)				->				int:
  __lowerCamelCase	:	int   	=			0
  __lowerCamelCase	:	Optional[int]   	=			abs(_lowerCAmelCase	)
  while True:
    __lowerCamelCase	:	List[str]   	=			n // 10
    digits += 1
    if n == 0:
      break
  return digits

def 	a_					(							_lowerCAmelCase	)				->				int:
  return 1 if n == 0 else math.floor(math.log(abs(_lowerCAmelCase	)	,10	) + 1	)

def 	a_					(							_lowerCAmelCase	)				->				int:
  return len(str(abs(_lowerCAmelCase	)	)	)

def 	a_					(							)				->				None:
  from collections.abc import Callable

  def benchmark_a_function(_lowerCAmelCase	,_lowerCAmelCase	) -> None:
    __lowerCamelCase	:	Any   	=			F'{func.__name__}({value})'
    __lowerCamelCase	:	int   	=			timeit(F'__main__.{call}'	,setup='import __main__'	)
    print(F'{call}: {func(_lowerCAmelCase	)} -- {timing} seconds'	)

  for value in (262144, 1125899906842624, 1267650600228229401496703205376):
    for func in (num_digits, num_digits_fast, num_digits_faster):
      benchmark_a_function(_lowerCAmelCase	,_lowerCAmelCase	)
    print()


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()






