





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__   :       int   						=			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							):

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			tempfile.mkdtemp()

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

        snake_case__      							=			PixaStructProcessor(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

        processor.save_pretrained(self.tmpdirname       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        return AutoProcessor.from_pretrained(self.tmpdirname				,   **__SCREAMING_SNAKE_CASE       ).tokenizer

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        return AutoProcessor.from_pretrained(self.tmpdirname				,   **__SCREAMING_SNAKE_CASE       ).image_processor

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        shutil.rmtree(self.tmpdirname       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			[np.random.randint(255				,   size=(3, 30, 400)				,   dtype=np.uinta       )]

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

        return image_inputs

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			PixaStructProcessor(tokenizer=self.get_tokenizer()				,   image_processor=self.get_image_processor()       )
        processor.save_pretrained(self.tmpdirname       )

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

        snake_case__      							=			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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.prepare_image_inputs()

        snake_case__      							=			image_processor(__SCREAMING_SNAKE_CASE				,   return_tensors="np"       )
        snake_case__      							=			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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			"lower newer"

        snake_case__      							=			processor(text=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			processor(text=__SCREAMING_SNAKE_CASE				,   images=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			[512, 1024, 2048, 4096]
        snake_case__      							=			[770, 770, 770, 770]
        # with text
        for i, max_patch in enumerate(__SCREAMING_SNAKE_CASE       ):
         snake_case__      							=			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       ):
         snake_case__      							=			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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			processor.batch_decode(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )

        self.assertListEqual(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_image_processor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   image_processor=__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			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"]       )

        snake_case__      							=			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"""


import math





def 							a__     (					_UpperCAmelCase		)      ->					list:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive integers have prime factors"		)
 snake_case__      							=			[]
 while n % 2 == 0:
  pf.append(2		)
  snake_case__      							=			int(n / 2		)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  while n % i == 0:
   pf.append(_UpperCAmelCase		)
   snake_case__      							=			int(n / i		)
 if n > 2:
  pf.append(_UpperCAmelCase		)
 return pf





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 div *= temp
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  div *= temp
 if n > 1:
  div *= 2
 return div





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 if temp > 1:
  s *= (2**temp - 1) / (2 - 1)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  if temp > 1:
   s *= (i**temp - 1) / (i - 1)
 return int(_UpperCAmelCase		)





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 snake_case__      							=			n
 for x in set(prime_factors(_UpperCAmelCase		)		):
  s *= (x - 1) / x
 return int(_UpperCAmelCase		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

