code: 
  
  
  '''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   :Optional[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 _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Tuple:
  			__magic_name__ :					int  			=  tempfile.mkdtemp()
  
  			__magic_name__ :					Any  			=  PixaStructImageProcessor()
  			__magic_name__ :					Dict  			=  TaTokenizer.from_pretrained('t5-small'    )
  
  			__magic_name__ :					str  			=  PixaStructProcessor(_A						,  _A    )
  
  			processor.save_pretrained(self.tmpdirname    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple						,  **_A	:		int    )						->				str:
  			return AutoProcessor.from_pretrained(self.tmpdirname						,  **_A    ).tokenizer
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]						,  **_A	:		Optional[int]    )						->				str:
  			return AutoProcessor.from_pretrained(self.tmpdirname						,  **_A    ).image_processor
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any    )						->				Optional[Any]:
  			shutil.rmtree(self.tmpdirname    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any    )						->				Dict:
  			__magic_name__ :					Any  			=  [np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta    )]
  
  			__magic_name__ :					List[str]  			=  [Image.fromarray(np.moveaxis(_A						,  0						,  -1    )    ) for x in image_inputs]
  
  			return image_inputs
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				int:
  			__magic_name__ :					Optional[int]  			=  PixaStructProcessor(tokenizer=self.get_tokenizer()						,  image_processor=self.get_image_processor()    )
  			processor.save_pretrained(self.tmpdirname    )
  
  			__magic_name__ :					List[str]  			=  self.get_tokenizer(bos_token='(BOS)'						,  eos_token='(EOS)'    )
  			__magic_name__ :					int  			=  self.get_image_processor(do_normalize=_A						,  padding_value=1.0    )
  
  			__magic_name__ :					str  			=  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      __lowerCAmelCase	(      self	:		Any    )						->				Dict:
  			__magic_name__ :					Optional[int]  			=  self.get_image_processor()
  			__magic_name__ :					str  			=  self.get_tokenizer()
  
  			__magic_name__ :					Tuple  			=  PixaStructProcessor(tokenizer=_A						,  image_processor=_A    )
  
  			__magic_name__ :					Dict  			=  self.prepare_image_inputs()
  
  			__magic_name__ :					Dict  			=  image_processor(_A						,  return_tensors='np'    )
  			__magic_name__ :					Dict  			=  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      __lowerCAmelCase	(      self	:		int    )						->				Tuple:
  			__magic_name__ :					Union[str, Any]  			=  self.get_image_processor()
  			__magic_name__ :					Dict  			=  self.get_tokenizer()
  
  			__magic_name__ :					Dict  			=  PixaStructProcessor(tokenizer=_A						,  image_processor=_A    )
  
  			__magic_name__ :					List[Any]  			=  'lower newer'
  
  			__magic_name__ :					List[str]  			=  processor(text=_A    )
  
  			__magic_name__ :					List[str]  			=  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      __lowerCAmelCase	(      self	:		Any    )						->				Optional[Any]:
  			__magic_name__ :					Union[str, Any]  			=  self.get_image_processor()
  			__magic_name__ :					Union[str, Any]  			=  self.get_tokenizer()
  
  			__magic_name__ :					Optional[Any]  			=  PixaStructProcessor(tokenizer=_A						,  image_processor=_A    )
  
  			__magic_name__ :					int  			=  'lower newer'
  			__magic_name__ :					Optional[Any]  			=  self.prepare_image_inputs()
  
  			__magic_name__ :					List[Any]  			=  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      __lowerCAmelCase	(      self	:		int    )						->				Union[str, Any]:
  			__magic_name__ :					Dict  			=  self.get_image_processor()
  			__magic_name__ :					Dict  			=  self.get_tokenizer()
  
  			__magic_name__ :					List[Any]  			=  PixaStructProcessor(tokenizer=_A						,  image_processor=_A    )
  
  			__magic_name__ :					str  			=  'lower newer'
  			__magic_name__ :					Dict  			=  self.prepare_image_inputs()
  
  			__magic_name__ :					int  			=  processor(text=_A						,  images=_A    )
  
  			__magic_name__ :					Dict  			=  [512, 1024, 2048, 4096]
  			__magic_name__ :					List[Any]  			=  [770, 770, 770, 770]
  			# with text
  			for i, max_patch in enumerate(_A    ):
  				__magic_name__ :					str  			=  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    ):
  				__magic_name__ :					Dict  			=  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      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				List[Any]:
  			__magic_name__ :					Union[str, Any]  			=  self.get_image_processor()
  			__magic_name__ :					Optional[Any]  			=  self.get_tokenizer()
  
  			__magic_name__ :					Union[str, Any]  			=  PixaStructProcessor(tokenizer=_A						,  image_processor=_A    )
  
  			__magic_name__ :					Tuple  			=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  			__magic_name__ :					Any  			=  processor.batch_decode(_A    )
  			__magic_name__ :					List[Any]  			=  tokenizer.batch_decode(_A    )
  
  			self.assertListEqual(_A						,  _A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int    )						->				Optional[int]:
  			__magic_name__ :					Optional[Any]  			=  self.get_image_processor()
  			__magic_name__ :					Optional[Any]  			=  self.get_tokenizer()
  
  			__magic_name__ :					Optional[int]  			=  PixaStructProcessor(tokenizer=_A						,  image_processor=_A    )
  
  			__magic_name__ :					List[str]  			=  'lower newer'
  			__magic_name__ :					Dict  			=  self.prepare_image_inputs()
  
  			__magic_name__ :					int  			=  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']    )
  
  			__magic_name__ :					Optional[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']    )
style_context: 
  
  
  '''simple docstring'''
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)
  
  lowerCAmelCase   :Dict								=    {
      '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
      # See all YOSO models at https://huggingface.co/models?filter=yoso
  }
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[Any]   					=       """yoso"""
  
  
  
  
  
  
  
  		def __init__(      self	:		Any						,  _A	:		List[Any]=50265						,  _A	:		Any=768						,  _A	:		List[str]=12						,  _A	:		Dict=12						,  _A	:		Union[str, Any]=3072						,  _A	:		List[Any]="gelu"						,  _A	:		Dict=0.1						,  _A	:		Union[str, Any]=0.1						,  _A	:		Union[str, Any]=4096						,  _A	:		Dict=1						,  _A	:		Tuple=0.02						,  _A	:		Any=1E-12						,  _A	:		Optional[int]="absolute"						,  _A	:		Tuple=True						,  _A	:		Any=9						,  _A	:		Tuple=64						,  _A	:		str=None						,  _A	:		Dict=True						,  _A	:		str=True						,  _A	:		int=1						,  _A	:		Optional[int]=0						,  _A	:		Tuple=2						,  **_A	:		Optional[Any]						,  )						->				Any:
  			super().__init__(pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A    )
  
  			__magic_name__ :					List[Any]  			=  vocab_size
  			__magic_name__ :					int  			=  max_position_embeddings
  			__magic_name__ :					int  			=  hidden_size
  			__magic_name__ :					Any  			=  num_hidden_layers
  			__magic_name__ :					List[str]  			=  num_attention_heads
  			__magic_name__ :					str  			=  intermediate_size
  			__magic_name__ :					Any  			=  hidden_act
  			__magic_name__ :					Union[str, Any]  			=  hidden_dropout_prob
  			__magic_name__ :					str  			=  attention_probs_dropout_prob
  			__magic_name__ :					int  			=  initializer_range
  			__magic_name__ :					List[Any]  			=  type_vocab_size
  			__magic_name__ :					Tuple  			=  layer_norm_eps
  			__magic_name__ :					Tuple  			=  position_embedding_type
  			__magic_name__ :					Optional[Any]  			=  use_expectation
  			__magic_name__ :					Union[str, Any]  			=  hash_code_len
  			__magic_name__ :					int  			=  num_hash
  			__magic_name__ :					List[Any]  			=  conv_window
  			__magic_name__ :					Optional[Any]  			=  use_fast_hash
  			__magic_name__ :					int  			=  lsh_backward