




"""simple docstring"""

import json
import os
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 AlignProcessor, EfficientNetImageProcessor





@require_vision
class     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       tempfile.mkdtemp()

       _A        =       [
           '[UNK]',
           '[CLS]',
           '[SEP]',
           '[PAD]',
           '[MASK]',
           'want',
           '##want',
           '##ed',
           'wa',
           'un',
           'runn',
           '##ing',
           ',',
           'low',
           'lowest',
       ]
       _A        =       os.path.join(self.tmpdirname   ,							VOCAB_FILES_NAMES['vocab_file']		)
       with open(self.vocab_file   ,							'w'   ,							encoding='utf-8'		) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)

       _A        =       {
           'do_resize': True,
           'size': 20,
           'do_center_crop': True,
           'crop_size': 18,
           'do_normalize': True,
           'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
           'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
       }
       _A        =       os.path.join(self.tmpdirname   ,							_UpperCAmelCase		)
       with open(self.image_processor_file   ,							'w'   ,							encoding='utf-8'		) as fp:
             json.dump(_UpperCAmelCase   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							**_UpperCAmelCase	:	Any		):
       return BertTokenizer.from_pretrained(self.tmpdirname   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Tuple   ,							**_UpperCAmelCase	:	Dict		):
       return BertTokenizerFast.from_pretrained(self.tmpdirname   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							**_UpperCAmelCase	:	Union[str, Any]		):
       return EfficientNetImageProcessor.from_pretrained(self.tmpdirname   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str		):
       shutil.rmtree(self.tmpdirname		)

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       [np.random.randint(255   ,							size=(3, 30, 400)   ,							dtype=np.uinta		)]
       _A        =       [Image.fromarray(np.moveaxis(_UpperCAmelCase   ,							0   ,							-1		)		) for x in image_inputs]
       return image_inputs

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       self.get_tokenizer()
       _A        =       self.get_rust_tokenizer()
       _A        =       self.get_image_processor()

       _A        =       AlignProcessor(tokenizer=_UpperCAmelCase   ,							image_processor=_UpperCAmelCase		)
       processor_slow.save_pretrained(self.tmpdirname		)
       _A        =       AlignProcessor.from_pretrained(self.tmpdirname   ,							use_fast=_UpperCAmelCase		)

       _A        =       AlignProcessor(tokenizer=_UpperCAmelCase   ,							image_processor=_UpperCAmelCase		)
       processor_fast.save_pretrained(self.tmpdirname		)
       _A        =       AlignProcessor.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       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       AlignProcessor(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=_UpperCAmelCase   ,							padding_value=1.0		)

       _A        =       AlignProcessor.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       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       self.get_image_processor()
       _A        =       self.get_tokenizer()

       _A        =       AlignProcessor(tokenizer=_UpperCAmelCase   ,							image_processor=_UpperCAmelCase		)

       _A        =       self.prepare_image_inputs()

       _A        =       image_processor(_UpperCAmelCase   ,							return_tensors='np'		)
       _A        =       processor(images=_UpperCAmelCase   ,							return_tensors='np'		)

       for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum()   ,							input_processor[key].sum()   ,							delta=1E-2		)

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       self.get_image_processor()
       _A        =       self.get_tokenizer()

       _A        =       AlignProcessor(tokenizer=_UpperCAmelCase   ,							image_processor=_UpperCAmelCase		)

       _A        =       'lower newer'

       _A        =       processor(text=_UpperCAmelCase		)

       _A        =       tokenizer(_UpperCAmelCase   ,							padding='max_length'   ,							max_length=64		)

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

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       self.get_image_processor()
       _A        =       self.get_tokenizer()

       _A        =       AlignProcessor(tokenizer=_UpperCAmelCase   ,							image_processor=_UpperCAmelCase		)

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

       _A        =       processor(text=_UpperCAmelCase   ,							images=_UpperCAmelCase		)

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

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

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A        =       self.get_image_processor()
       _A        =       self.get_tokenizer()

       _A        =       AlignProcessor(tokenizer=_UpperCAmelCase   ,							image_processor=_UpperCAmelCase		)

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

       _A        =       processor.batch_decode(_UpperCAmelCase		)
       _A        =       tokenizer.batch_decode(_UpperCAmelCase		)

       self.assertListEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       self.get_image_processor()
       _A        =       self.get_tokenizer()

       _A        =       AlignProcessor(tokenizer=_UpperCAmelCase   ,							image_processor=_UpperCAmelCase		)

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

       _A        =       processor(text=_UpperCAmelCase   ,							images=_UpperCAmelCase		)

       self.assertListEqual(list(inputs.keys()		)   ,							processor.model_input_names		)







"""simple docstring"""

def 						_snake_case					( _snake_case							: int							)   -> list:






      '''simple docstring'''


      _A        =       int(_snake_case							)
      if n_element < 1:
            _A        =       ValueError('a should be a positive number'							)
            raise my_error

      _A        =       [1]
      _A       ,   _A       ,   _A        =       (0, 0, 0)
      _A        =       1
      while index < n_element:
            while hamming_list[i] * 2 <= hamming_list[-1]:
                  i += 1
            while hamming_list[j] * 3 <= hamming_list[-1]:
                  j += 1
            while hamming_list[k] * 5 <= hamming_list[-1]:
                  k += 1
            hamming_list.append(
                min(hamming_list[i] * 2    ,  hamming_list[j] * 3    ,  hamming_list[k] * 5							)							)
            index += 1
      return hamming_list


if __name__ == "__main__":
       a         =      input('''Enter the last number (nth term) of the Hamming Number Series: ''')
       print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
       a         =      hamming(int(n))
       print('''-----------------------------------------------------''')
       print(F'''The list with nth numbers is: {hamming_numbers}''')
       print('''-----------------------------------------------------''')

