
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     __A(				unittest.TestCase    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''simple docstring'''




								__a    							=					tempfile.mkdtemp()

								__a    							=					CLIPImageProcessor()
								__a    							=					BertTokenizer.from_pretrained(
								    '''hf-internal-testing/tiny-random-BertModel'''	,  model_input_names=['''input_ids''', '''attention_mask''']       )

								__a    							=					GitProcessor(_snake_case	,  _snake_case       )

								processor.save_pretrained(self.tmpdirname       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  **_snake_case       )					->   List[Any]:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  **_snake_case       )					->   Tuple:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''simple docstring'''




								shutil.rmtree(self.tmpdirname       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




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

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

								return image_inputs




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a    							=					GitProcessor(tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor()       )
								processor.save_pretrained(self.tmpdirname       )

								__a    							=					self.get_tokenizer(bos_token='''(BOS)'''	,  eos_token='''(EOS)'''       )
								__a    							=					self.get_image_processor(do_normalize=_snake_case	,  padding_value=1.0       )

								__a    							=					GitProcessor.from_pretrained(
								    self.tmpdirname	,  bos_token='''(BOS)'''	,  eos_token='''(EOS)'''	,  do_normalize=_snake_case	,  padding_value=1.0       )

								self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()       )
								self.assertIsInstance(processor.tokenizer	,  _snake_case       )

								self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string()       )
								self.assertIsInstance(processor.image_processor	,  _snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Any:


								'''simple docstring'''




								__a    							=					self.get_image_processor()
								__a    							=					self.get_tokenizer()

								__a    							=					GitProcessor(tokenizer=_snake_case	,  image_processor=_snake_case       )

								__a    							=					self.prepare_image_inputs()

								__a    							=					image_processor(_snake_case	,  return_tensors='''np'''       )
								__a    							=					processor(images=_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       )					->   Dict:


								'''simple docstring'''




								__a    							=					self.get_image_processor()
								__a    							=					self.get_tokenizer()

								__a    							=					GitProcessor(tokenizer=_snake_case	,  image_processor=_snake_case       )

								__a    							=					'''lower newer'''

								__a    							=					processor(text=_snake_case       )

								__a    							=					tokenizer(_snake_case	,  return_token_type_ids=_snake_case       )

								for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Any:


								'''simple docstring'''




								__a    							=					self.get_image_processor()
								__a    							=					self.get_tokenizer()

								__a    							=					GitProcessor(tokenizer=_snake_case	,  image_processor=_snake_case       )

								__a    							=					'''lower newer'''
								__a    							=					self.prepare_image_inputs()

								__a    							=					processor(text=_snake_case	,  images=_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(_snake_case       ):
												processor()




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict:


								'''simple docstring'''




								__a    							=					self.get_image_processor()
								__a    							=					self.get_tokenizer()

								__a    							=					GitProcessor(tokenizer=_snake_case	,  image_processor=_snake_case       )

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

								__a    							=					processor.batch_decode(_snake_case       )
								__a    							=					tokenizer.batch_decode(_snake_case       )

								self.assertListEqual(_snake_case	,  _snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a    							=					self.get_image_processor()
								__a    							=					self.get_tokenizer()

								__a    							=					GitProcessor(tokenizer=_snake_case	,  image_processor=_snake_case       )

								__a    							=					'''lower newer'''
								__a    							=					self.prepare_image_inputs()

								__a    							=					processor(text=_snake_case	,  images=_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''']       )

def 					__lowerCAmelCase    (    a__ = 99_8001						) ->						int:
				__a    							=					0
				for i in range(999		,					99		,					-1						):  # 3 digit numbers range from 999 down to 100
								for j in range(999		,					99		,					-1						):
												__a    							=					str(i * j						)
												if product_string == product_string[::-1] and i * j < n:
																__a    							=					max(a__		,					i * j						)
				return answer


if __name__ == "__main__":
					print(F"{solution() = }")