code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import unittest
  
  from transformers import load_tool
  
  from .test_tools_common import ToolTesterMixin
  
  
  a__				: Optional[int]									=    '''
  Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
  
  In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
  
  On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
  '''
  
  
  
  
  
  
  class 							__snake_case    (				unittest.TestCase					,					a_       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[Any]:
  		snake_case__  					=      load_tool('summarization'		)
  		self.tool.setup()
  		snake_case__  					=      load_tool('summarization'			,					remote=UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		int:
  		snake_case__  					=      self.tool(UpperCamelCase_		)
  		self.assertEqual(
  		    UpperCamelCase_			,					'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,					)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      self.remote_tool(UpperCamelCase_		)
  		self.assertEqual(
  		    UpperCamelCase_			,					'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,					)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      self.tool(text=UpperCamelCase_		)
  		self.assertEqual(
  		    UpperCamelCase_			,					'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,					)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      self.remote_tool(text=UpperCamelCase_		)
  		self.assertEqual(
  		    UpperCamelCase_			,					'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,					)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import json
  import os
  import random
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers import BertTokenizer, BertTokenizerFast
  from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_vision
  from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
  
  
  if is_vision_available():
  			from PIL import Image
  
  			from transformers import FlavaImageProcessor, FlavaProcessor
  			from transformers.models.flava.image_processing_flava import (
  			    FLAVA_CODEBOOK_MEAN,
  			    FLAVA_CODEBOOK_STD,
  			    FLAVA_IMAGE_MEAN,
  			    FLAVA_IMAGE_STD,
  			)
  
  
  
  
  
  
  @require_vision
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[Any]:
  		snake_case__  					=      tempfile.mkdtemp()
  
  		# fmt: off
  		snake_case__  					=      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
  		# fmt: on
  		snake_case__  					=      os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['vocab_file']		)
  
  		with open(self.vocab_file			,					'w'			,					encoding='utf-8'		) as fp:
  			fp.write(''.join([x + '\n' for x in vocab_tokens]		)		)
  
  		snake_case__  					=      {
  		    'image_mean': FLAVA_IMAGE_MEAN,
  		    'image_std': FLAVA_IMAGE_STD,
  		    'do_normalize': True,
  		    'do_resize': True,
  		    'size': 224,
  		    'do_center_crop': True,
  		    'crop_size': 224,
  		    'input_size_patches': 14,
  		    'total_mask_patches': 75,
  		    'mask_group_max_patches': None,
  		    'mask_group_min_patches': 16,
  		    'mask_group_min_aspect_ratio': 0.3,
  		    'mask_group_max_aspect_ratio': None,
  		    'codebook_do_resize': True,
  		    'codebook_size': 112,
  		    'codebook_do_center_crop': True,
  		    'codebook_crop_size': 112,
  		    'codebook_do_map_pixels': True,
  		    'codebook_do_normalize': True,
  		    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
  		    'codebook_image_std': FLAVA_CODEBOOK_STD,
  		}
  
  		snake_case__  					=      os.path.join(self.tmpdirname			,					UpperCamelCase_		)
  		with open(self.image_processor_file			,					'w'			,					encoding='utf-8'		) as fp:
  			json.dump(UpperCamelCase_			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		List[str]:
  		return BertTokenizer.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Optional[Any]:
  		return BertTokenizerFast.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Any:
  		return FlavaImageProcessor.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		shutil.rmtree(self.tmpdirname		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      [np.random.randint(255			,					size=(3, 30, 400)			,					dtype=np.uinta		)]
  
  		snake_case__  					=      [Image.fromarray(np.moveaxis(UpperCamelCase_			,					0			,					-1		)		) for x in image_inputs]
  
  		return image_inputs
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      self.get_tokenizer()
  		snake_case__  					=      self.get_rust_tokenizer()
  		snake_case__  					=      self.get_image_processor()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  		processor_slow.save_pretrained(self.tmpdirname		)
  		snake_case__  					=      FlavaProcessor.from_pretrained(self.tmpdirname			,					use_fast=UpperCamelCase_		)
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  		processor_fast.save_pretrained(self.tmpdirname		)
  		snake_case__  					=      FlavaProcessor.from_pretrained(self.tmpdirname		)
  
  		self.assertEqual(processor_slow.tokenizer.get_vocab()			,					tokenizer_slow.get_vocab()		)
  		self.assertEqual(processor_fast.tokenizer.get_vocab()			,					tokenizer_fast.get_vocab()		)
  		self.assertEqual(tokenizer_slow.get_vocab()			,					tokenizer_fast.get_vocab()		)
  		self.assertIsInstance(processor_slow.tokenizer			,					UpperCamelCase_		)
  		self.assertIsInstance(processor_fast.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor_slow.image_processor.to_json_string()			,					image_processor.to_json_string()		)
  		self.assertEqual(processor_fast.image_processor.to_json_string()			,					image_processor.to_json_string()		)
  		self.assertIsInstance(processor_slow.image_processor			,					UpperCamelCase_		)
  		self.assertIsInstance(processor_fast.image_processor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      FlavaProcessor(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=UpperCamelCase_			,					padding_value=1.0		)
  
  		snake_case__  					=      FlavaProcessor.from_pretrained(
  		    self.tmpdirname			,					bos_token='(BOS)'			,					eos_token='(EOS)'			,					do_normalize=UpperCamelCase_			,					padding_value=1.0		)
  
  		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer_add_kwargs.get_vocab()		)
  		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor.image_processor.to_json_string()			,					image_processor_add_kwargs.to_json_string()		)
  		self.assertIsInstance(processor.image_processor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      self.prepare_image_inputs()
  
  		snake_case__  					=      image_processor(UpperCamelCase_			,					return_tensors='np'		)
  		snake_case__  					=      processor(images=UpperCamelCase_			,					return_tensors='np'		)
  
  		for key in input_feat_extract.keys():
  			self.assertAlmostEqual(input_feat_extract[key].sum()			,					input_processor[key].sum()			,					delta=1E-2		)
  
  		# With rest of the args
  		random.seed(1234		)
  		snake_case__  					=      image_processor(
  		    UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_tensors='np'		)
  		random.seed(1234		)
  		snake_case__  					=      processor(
  		    images=UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					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      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      'lower newer'
  
  		snake_case__  					=      processor(text=UpperCamelCase_		)
  
  		snake_case__  					=      tokenizer(UpperCamelCase_		)
  
  		for key in encoded_tok.keys():
  			self.assertListEqual(encoded_tok[key]			,					encoded_processor[key]		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      'lower newer'
  		snake_case__  					=      self.prepare_image_inputs()
  
  		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)
  
  		self.assertListEqual(list(inputs.keys()		)			,					['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']		)
  
  		# add extra args
  		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_image_mask=UpperCamelCase_		)
  
  		self.assertListEqual(
  		    list(inputs.keys()		)			,					[
  		        'input_ids',
  		        'token_type_ids',
  		        'attention_mask',
  		        'pixel_values',
  		        'codebook_pixel_values',
  		        'bool_masked_pos',
  		    ]			,					)
  
  		# test if it raises when no input is passed
  		with pytest.raises(UpperCamelCase_		):
  			processor()
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  		snake_case__  					=      processor.batch_decode(UpperCamelCase_		)
  		snake_case__  					=      tokenizer.batch_decode(UpperCamelCase_		)
  
  		self.assertListEqual(UpperCamelCase_			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      'lower newer'
  		snake_case__  					=      self.prepare_image_inputs()
  
  		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)
  
  		self.assertListEqual(list(inputs.keys()		)			,					processor.model_input_names		)
  
  
  
  