






"""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    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''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(_lowerCamelCase,			_lowerCamelCase    )

     processor.save_pretrained(self.tmpdirname    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			**_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     return AutoProcessor.from_pretrained(self.tmpdirname,			**_lowerCamelCase    ).tokenizer


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			**_lowerCamelCase					:	str    ):

     '''simple docstring'''
     return AutoProcessor.from_pretrained(self.tmpdirname,			**_lowerCamelCase    ).image_processor


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     shutil.rmtree(self.tmpdirname    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					[np.random.randint(2_55,			size=(3, 30, 4_00),			dtype=np.uinta    )]

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

     return image_inputs


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''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=_lowerCamelCase,			padding_value=1.0    )

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

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

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					self.get_image_processor()
     __A		     =					self.get_tokenizer()

     __A		     =					GitProcessor(tokenizer=_lowerCamelCase,			image_processor=_lowerCamelCase    )

     __A		     =					self.prepare_image_inputs()

     __A		     =					image_processor(_lowerCamelCase,			return_tensors='''np'''    )
     __A		     =					processor(images=_lowerCamelCase,			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					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					self.get_image_processor()
     __A		     =					self.get_tokenizer()

     __A		     =					GitProcessor(tokenizer=_lowerCamelCase,			image_processor=_lowerCamelCase    )

     __A		     =					'''lower newer'''

     __A		     =					processor(text=_lowerCamelCase    )

     __A		     =					tokenizer(_lowerCamelCase,			return_token_type_ids=_lowerCamelCase    )

     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=_lowerCamelCase,			image_processor=_lowerCamelCase    )

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

     __A		     =					processor(text=_lowerCamelCase,			images=_lowerCamelCase    )

     self.assertListEqual(list(inputs.keys()    ),			['''input_ids''', '''attention_mask''', '''pixel_values''']    )

     # test if it raises when no input is passed
     with pytest.raises(_lowerCamelCase    ):
       processor()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					self.get_image_processor()
     __A		     =					self.get_tokenizer()

     __A		     =					GitProcessor(tokenizer=_lowerCamelCase,			image_processor=_lowerCamelCase    )

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

     __A		     =					processor.batch_decode(_lowerCamelCase    )
     __A		     =					tokenizer.batch_decode(_lowerCamelCase    )

     self.assertListEqual(_lowerCamelCase,			_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.get_image_processor()
     __A		     =					self.get_tokenizer()

     __A		     =					GitProcessor(tokenizer=_lowerCamelCase,			image_processor=_lowerCamelCase    )

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

     __A		     =					processor(text=_lowerCamelCase,			images=_lowerCamelCase    )

     # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
     self.assertListEqual(list(inputs.keys()    ),			['''input_ids''', '''attention_mask''', '''pixel_values''']    )












from __future__ import annotations






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		list[int]:







							"""simple docstring"""





							_a  :    Optional[Any]			=						[0] * max_one

							for i in range(0    ,	UpperCAmelCase     ):
														_a  :    int			=						i

							for i in range(2    ,	UpperCAmelCase     ):
														if totients[i] == i:
																					for j in range(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     ):
																												totients[j] -= totients[j] // i

							return totients






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		bool:







							"""simple docstring"""





							return sorted(str(UpperCAmelCase     )     ) == sorted(str(UpperCAmelCase     )     )






def    UpperCamelCase__  (   UpperCAmelCase = 10000000     )     ->		int:







							"""simple docstring"""





							_a  :    List[Any]			=						1  # i
							_a  :    Any			=						0  # φ(i)
							_a  :    Tuple			=						get_totients(max_n + 1     )

							for i in range(2    ,	max_n + 1     ):
														_a  :    List[str]			=						totients[i]

														if i * min_denominator < min_numerator * t and has_same_digits(UpperCAmelCase    ,	UpperCAmelCase     ):
																					_a  :    Any			=						i
																					_a  :    Union[str, Any]			=						t

							return min_numerator


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