




'''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:
 lowerCamelCase_	 =		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 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			List[Any]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   tempfile.mkdtemp()

        _SCREAMING_SNAKE_CASE =   PixaStructImageProcessor()
        _SCREAMING_SNAKE_CASE =   TaTokenizer.from_pretrained("t5-small"					)

        _SCREAMING_SNAKE_CASE =   PixaStructProcessor(__lowerCamelCase						,   __lowerCamelCase					)

        processor.save_pretrained(self.tmpdirname					)



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   **__lowerCamelCase      :			Union[str, Any]					):

        """simple docstring"""
        return AutoProcessor.from_pretrained(self.tmpdirname						,   **__lowerCamelCase					).tokenizer



   def 							lowerCAmelCase_    (					self      :			int						,   **__lowerCamelCase      :			Optional[Any]					):

        """simple docstring"""
        return AutoProcessor.from_pretrained(self.tmpdirname						,   **__lowerCamelCase					).image_processor



   def 							lowerCAmelCase_    (					self      :			Tuple					):

        """simple docstring"""
        shutil.rmtree(self.tmpdirname					)



   def 							lowerCAmelCase_    (					self      :			int					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   [np.random.randint(2_5_5						,   size=(3, 3_0, 4_0_0)						,   dtype=np.uinta					)]

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

        return image_inputs



   def 							lowerCAmelCase_    (					self      :			int					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   PixaStructProcessor(tokenizer=self.get_tokenizer()						,   image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

        _SCREAMING_SNAKE_CASE =   self.get_tokenizer(bos_token="(BOS)"						,   eos_token="(EOS)"					)
        _SCREAMING_SNAKE_CASE =   self.get_image_processor(do_normalize=__lowerCamelCase						,   padding_value=1.0					)

        _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			str					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.get_image_processor()
        _SCREAMING_SNAKE_CASE =   self.get_tokenizer()

        _SCREAMING_SNAKE_CASE =   PixaStructProcessor(tokenizer=__lowerCamelCase						,   image_processor=__lowerCamelCase					)

        _SCREAMING_SNAKE_CASE =   self.prepare_image_inputs()

        _SCREAMING_SNAKE_CASE =   image_processor(__lowerCamelCase						,   return_tensors="np"					)
        _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Any					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.get_image_processor()
        _SCREAMING_SNAKE_CASE =   self.get_tokenizer()

        _SCREAMING_SNAKE_CASE =   PixaStructProcessor(tokenizer=__lowerCamelCase						,   image_processor=__lowerCamelCase					)

        _SCREAMING_SNAKE_CASE =   "lower newer"

        _SCREAMING_SNAKE_CASE =   processor(text=__lowerCamelCase					)

        _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Optional[int]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.get_image_processor()
        _SCREAMING_SNAKE_CASE =   self.get_tokenizer()

        _SCREAMING_SNAKE_CASE =   PixaStructProcessor(tokenizer=__lowerCamelCase						,   image_processor=__lowerCamelCase					)

        _SCREAMING_SNAKE_CASE =   "lower newer"
        _SCREAMING_SNAKE_CASE =   self.prepare_image_inputs()

        _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.get_image_processor()
        _SCREAMING_SNAKE_CASE =   self.get_tokenizer()

        _SCREAMING_SNAKE_CASE =   PixaStructProcessor(tokenizer=__lowerCamelCase						,   image_processor=__lowerCamelCase					)

        _SCREAMING_SNAKE_CASE =   "lower newer"
        _SCREAMING_SNAKE_CASE =   self.prepare_image_inputs()

        _SCREAMING_SNAKE_CASE =   processor(text=__lowerCamelCase						,   images=__lowerCamelCase					)

        _SCREAMING_SNAKE_CASE =   [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
        _SCREAMING_SNAKE_CASE =   [7_7_0, 7_7_0, 7_7_0, 7_7_0]
        # with text
        for i, max_patch in enumerate(__lowerCamelCase					):
             _SCREAMING_SNAKE_CASE =   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					):
             _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Optional[int]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.get_image_processor()
        _SCREAMING_SNAKE_CASE =   self.get_tokenizer()

        _SCREAMING_SNAKE_CASE =   PixaStructProcessor(tokenizer=__lowerCamelCase						,   image_processor=__lowerCamelCase					)

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

        _SCREAMING_SNAKE_CASE =   processor.batch_decode(__lowerCamelCase					)
        _SCREAMING_SNAKE_CASE =   tokenizer.batch_decode(__lowerCamelCase					)

        self.assertListEqual(__lowerCamelCase						,   __lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.get_image_processor()
        _SCREAMING_SNAKE_CASE =   self.get_tokenizer()

        _SCREAMING_SNAKE_CASE =   PixaStructProcessor(tokenizer=__lowerCamelCase						,   image_processor=__lowerCamelCase					)

        _SCREAMING_SNAKE_CASE =   "lower newer"
        _SCREAMING_SNAKE_CASE =   self.prepare_image_inputs()

        _SCREAMING_SNAKE_CASE =   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"]					)

        _SCREAMING_SNAKE_CASE =   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 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple						)				->				List[Any]:
   _SCREAMING_SNAKE_CASE =   0
   if n % 2 == 0:
      count += 1
      while n % 2 == 0:
         _SCREAMING_SNAKE_CASE =   int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

   _SCREAMING_SNAKE_CASE =   3

   while i <= int(math.sqrt(__A						)						):
      if n % i == 0:
         count += 1
         while n % i == 0:
            _SCREAMING_SNAKE_CASE =   int(n / i						)
      _SCREAMING_SNAKE_CASE =   i + 2

   # this condition checks the prime
   # number n is greater than 2

   if n > 2:
      count += 1
   return count


if __name__ == "__main__":
 lowerCamelCase_	 =		51_24_21_83
 print(f'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
 print(f'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')

