code: 
  
  
  
  
  """simple docstring"""
  
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
  							from PIL import Image
  
  							from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  @require_vision
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				tempfile.mkdtemp()
  
  					snake_case		:						Tuple								=				CLIPImageProcessor()
  					snake_case		:						Tuple								=				BertTokenizer.from_pretrained(
  					    "hf-internal-testing/tiny-random-BertModel"				, model_input_names=["input_ids", "attention_mask"]							)
  
  					snake_case		:						Optional[int]								=				GitProcessor(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  					processor.save_pretrained(self.tmpdirname							)
  
  
  			def 							lowerCamelCase_				(     self				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return AutoProcessor.from_pretrained(self.tmpdirname				, **SCREAMING_SNAKE_CASE							).tokenizer
  
  
  			def 							lowerCamelCase_				(     self				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return AutoProcessor.from_pretrained(self.tmpdirname				, **SCREAMING_SNAKE_CASE							).image_processor
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					shutil.rmtree(self.tmpdirname							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				[np.random.randint(255				, size=(3, 30, 400)				, dtype=np.uinta							)]
  
  					snake_case		:						Dict								=				[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE				, 0				, -1							)							) for x in image_inputs]
  
  					return image_inputs
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				GitProcessor(tokenizer=self.get_tokenizer()				, image_processor=self.get_image_processor()							)
  					processor.save_pretrained(self.tmpdirname							)
  
  					snake_case		:						Union[str, Any]								=				self.get_tokenizer(bos_token="(BOS)"				, eos_token="(EOS)"							)
  					snake_case		:						str								=				self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE				, padding_value=1.0							)
  
  					snake_case		:						int								=				GitProcessor.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 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				self.get_image_processor()
  					snake_case		:						Optional[int]								=				self.get_tokenizer()
  
  					snake_case		:						List[Any]								=				GitProcessor(tokenizer=SCREAMING_SNAKE_CASE				, image_processor=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				self.prepare_image_inputs()
  
  					snake_case		:						Any								=				image_processor(SCREAMING_SNAKE_CASE				, return_tensors="np"							)
  					snake_case		:						Optional[int]								=				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 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				self.get_image_processor()
  					snake_case		:						Optional[Any]								=				self.get_tokenizer()
  
  					snake_case		:						Tuple								=				GitProcessor(tokenizer=SCREAMING_SNAKE_CASE				, image_processor=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Optional[int]								=				"lower newer"
  
  					snake_case		:						Union[str, Any]								=				processor(text=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Optional[int]								=				tokenizer(SCREAMING_SNAKE_CASE				, return_token_type_ids=SCREAMING_SNAKE_CASE							)
  
  					for key in encoded_tok.keys():
  							self.assertListEqual(encoded_tok[key]				, encoded_processor[key]							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				self.get_image_processor()
  					snake_case		:						int								=				self.get_tokenizer()
  
  					snake_case		:						List[str]								=				GitProcessor(tokenizer=SCREAMING_SNAKE_CASE				, image_processor=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Any								=				"lower newer"
  					snake_case		:						Optional[int]								=				self.prepare_image_inputs()
  
  					snake_case		:						List[Any]								=				processor(text=SCREAMING_SNAKE_CASE				, images=SCREAMING_SNAKE_CASE							)
  
  					self.assertListEqual(list(inputs.keys()							)				, ["input_ids", "attention_mask", "pixel_values"]							)
  
  					# test if it raises when no input is passed
  					with pytest.raises(SCREAMING_SNAKE_CASE							):
  							processor()
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Any								=				self.get_image_processor()
  					snake_case		:						str								=				self.get_tokenizer()
  
  					snake_case		:						Optional[int]								=				GitProcessor(tokenizer=SCREAMING_SNAKE_CASE				, image_processor=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Optional[Any]								=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  					snake_case		:						Optional[int]								=				processor.batch_decode(SCREAMING_SNAKE_CASE							)
  					snake_case		:						str								=				tokenizer.batch_decode(SCREAMING_SNAKE_CASE							)
  
  					self.assertListEqual(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				self.get_image_processor()
  					snake_case		:						Union[str, Any]								=				self.get_tokenizer()
  
  					snake_case		:						Tuple								=				GitProcessor(tokenizer=SCREAMING_SNAKE_CASE				, image_processor=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Dict								=				"lower newer"
  					snake_case		:						int								=				self.prepare_image_inputs()
  
  					snake_case		:						str								=				processor(text=SCREAMING_SNAKE_CASE				, images=SCREAMING_SNAKE_CASE							)
  
  					# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
  					self.assertListEqual(list(inputs.keys()							)				, ["input_ids", "attention_mask", "pixel_values"]							)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  def   UpperCamelCase__     (		lowercase__  :     int   ):
  		if not isinstance(lowercase__       ,     lowercase__   ):
  				snake_case		:						Optional[Any]								=				F'''Input value of [number={number}] must be an integer'''
  				raise TypeError(lowercase__   )
  		if number < 0 or number % 2 == 1:
  				return False
  		snake_case		:						str								=				int(number**0.5   )
  		return number == number_sqrt * (number_sqrt + 1)
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  