code: 
  
  
  
  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__	:  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      UpperCAmelCase_						(     unittest.TestCase ):
  
  						'''simple docstring'''
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      tempfile.mkdtemp()
  
  													__SCREAMING_SNAKE_CASE								=      PixaStructImageProcessor()
  													__SCREAMING_SNAKE_CASE								=      TaTokenizer.from_pretrained('t5-small'	)
  
  													__SCREAMING_SNAKE_CASE								=      PixaStructProcessor(_A   ,							_A	)
  
  													processor.save_pretrained(self.tmpdirname	)
  
  
  
  
  
  
  						def  _A			(					self   ,							**_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return AutoProcessor.from_pretrained(self.tmpdirname   ,							**_A	).tokenizer
  
  
  
  
  
  
  						def  _A			(					self   ,							**_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return AutoProcessor.from_pretrained(self.tmpdirname   ,							**_A	).image_processor
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													shutil.rmtree(self.tmpdirname	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      [np.random.randint(255   ,							size=(3, 30, 400)   ,							dtype=np.uinta	)]
  
  													__SCREAMING_SNAKE_CASE								=      [Image.fromarray(np.moveaxis(_A   ,							0   ,							-1	)	) for x in image_inputs]
  
  													return image_inputs
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''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=_A   ,							padding_value=1.0	)
  
  													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.get_image_processor()
  													__SCREAMING_SNAKE_CASE								=      self.get_tokenizer()
  
  													__SCREAMING_SNAKE_CASE								=      PixaStructProcessor(tokenizer=_A   ,							image_processor=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      self.prepare_image_inputs()
  
  													__SCREAMING_SNAKE_CASE								=      image_processor(_A   ,							return_tensors='np'	)
  													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.get_image_processor()
  													__SCREAMING_SNAKE_CASE								=      self.get_tokenizer()
  
  													__SCREAMING_SNAKE_CASE								=      PixaStructProcessor(tokenizer=_A   ,							image_processor=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      'lower newer'
  
  													__SCREAMING_SNAKE_CASE								=      processor(text=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.get_image_processor()
  													__SCREAMING_SNAKE_CASE								=      self.get_tokenizer()
  
  													__SCREAMING_SNAKE_CASE								=      PixaStructProcessor(tokenizer=_A   ,							image_processor=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      'lower newer'
  													__SCREAMING_SNAKE_CASE								=      self.prepare_image_inputs()
  
  													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.get_image_processor()
  													__SCREAMING_SNAKE_CASE								=      self.get_tokenizer()
  
  													__SCREAMING_SNAKE_CASE								=      PixaStructProcessor(tokenizer=_A   ,							image_processor=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      'lower newer'
  													__SCREAMING_SNAKE_CASE								=      self.prepare_image_inputs()
  
  													__SCREAMING_SNAKE_CASE								=      processor(text=_A   ,							images=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      [512, 1_024, 2_048, 4_096]
  													__SCREAMING_SNAKE_CASE								=      [770, 770, 770, 770]
  													# with text
  													for i, max_patch in enumerate(_A	):
  																				__SCREAMING_SNAKE_CASE								=      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	):
  																				__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.get_image_processor()
  													__SCREAMING_SNAKE_CASE								=      self.get_tokenizer()
  
  													__SCREAMING_SNAKE_CASE								=      PixaStructProcessor(tokenizer=_A   ,							image_processor=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  													__SCREAMING_SNAKE_CASE								=      processor.batch_decode(_A	)
  													__SCREAMING_SNAKE_CASE								=      tokenizer.batch_decode(_A	)
  
  													self.assertListEqual(_A   ,							_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.get_image_processor()
  													__SCREAMING_SNAKE_CASE								=      self.get_tokenizer()
  
  													__SCREAMING_SNAKE_CASE								=      PixaStructProcessor(tokenizer=_A   ,							image_processor=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      'lower newer'
  													__SCREAMING_SNAKE_CASE								=      self.prepare_image_inputs()
  
  													__SCREAMING_SNAKE_CASE								=      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']	)
  
  													__SCREAMING_SNAKE_CASE								=      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']	)
  
  
  
  
  
  
  
style_context: 
  
  
  
  from itertools import combinations, product
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__							,     a__							,     a__     )       ->				bool:
  							if xa == ya == 0 or xa == ya == 0:
  														return False
  							__SCREAMING_SNAKE_CASE								=      xa * xa + ya * ya
  							__SCREAMING_SNAKE_CASE								=      xa * xa + ya * ya
  							__SCREAMING_SNAKE_CASE								=      (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
  							return (
  							    a_square + b_square == c_square
  							    or a_square + c_square == b_square
  							    or b_square + c_square == a_square
  							)
  
  
  
  
  
  
  def 			__lowercase     (				a__ = 50     )       ->				int:
  							return sum(
  							    1
  							    for pta, pta in combinations(product(range(limit + 1     )							,     repeat=2     )							,     2     )
  							    if is_right(*a__							,     *a__     )     )
  
  
  if __name__ == "__main__":
  			print(F'''{solution() = }''')
  
  
  
  
  
  
  