'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPImageProcessor, CLIPProcessor







@require_vision
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			int:
        _A    		=  tempfile.mkdtemp()

        # fmt: off
        _A    		=  ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
        # fmt: on
        _A    		=  dict(zip(UpperCamelCase__, range(len(UpperCamelCase__     )     )     )     )
        _A    		=  ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
        _A    		=  {'unk_token': '<unk>'}

        _A    		=  os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file']     )
        _A    		=  os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file']     )
        with open(self.vocab_file, 'w', encoding='utf-8'     ) as fp:
             fp.write(json.dumps(UpperCamelCase__     ) + '\n'     )
        with open(self.merges_file, 'w', encoding='utf-8'     ) as fp:
             fp.write('\n'.join(UpperCamelCase__     )     )

        _A    		=  {
            'do_resize': True,
            'size': 20,
            'do_center_crop': True,
            'crop_size': 18,
            'do_normalize': True,
            'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
            'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
        }
        _A    		=  os.path.join(self.tmpdirname, UpperCamelCase__     )
        with open(self.image_processor_file, 'w', encoding='utf-8'     ) as fp:
             json.dump(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], **UpperCamelCase__      :			List[Any]     )  ->			int:
        return CLIPTokenizer.from_pretrained(self.tmpdirname, **UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Dict, **UpperCamelCase__      :			List[str]     )  ->			Any:
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int], **UpperCamelCase__      :			List[Any]     )  ->			str:
        return CLIPImageProcessor.from_pretrained(self.tmpdirname, **UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			int:
        shutil.rmtree(self.tmpdirname     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Union[str, Any]:
        _A    		=  [np.random.randint(2_55, size=(3, 30, 4_00), dtype=np.uinta     )]

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

        return image_inputs
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Union[str, Any]:
        _A    		=  self.get_tokenizer()
        _A    		=  self.get_rust_tokenizer()
        _A    		=  self.get_image_processor()

        _A    		=  CLIPProcessor(tokenizer=UpperCamelCase__, image_processor=UpperCamelCase__     )
        processor_slow.save_pretrained(self.tmpdirname     )
        _A    		=  CLIPProcessor.from_pretrained(self.tmpdirname, use_fast=UpperCamelCase__     )

        _A    		=  CLIPProcessor(tokenizer=UpperCamelCase__, image_processor=UpperCamelCase__     )
        processor_fast.save_pretrained(self.tmpdirname     )
        _A    		=  CLIPProcessor.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 	__UpperCAmelCase   (      self      :			Any     )  ->			Dict:
        _A    		=  CLIPProcessor(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    		=  CLIPProcessor.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 	__UpperCAmelCase   (      self      :			str     )  ->			List[Any]:
        _A    		=  self.get_image_processor()
        _A    		=  self.get_tokenizer()

        _A    		=  CLIPProcessor(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 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Tuple:
        _A    		=  self.get_image_processor()
        _A    		=  self.get_tokenizer()

        _A    		=  CLIPProcessor(tokenizer=UpperCamelCase__, image_processor=UpperCamelCase__     )

        _A    		=  'lower newer'

        _A    		=  processor(text=UpperCamelCase__     )

        _A    		=  tokenizer(UpperCamelCase__     )

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key], encoded_processor[key]     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        _A    		=  self.get_image_processor()
        _A    		=  self.get_tokenizer()

        _A    		=  CLIPProcessor(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', 'attention_mask', 'pixel_values']     )

        # test if it raises when no input is passed
        with pytest.raises(UpperCamelCase__     ):
             processor()
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Dict:
        _A    		=  self.get_image_processor()
        _A    		=  self.get_tokenizer()

        _A    		=  CLIPProcessor(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 	__UpperCAmelCase   (      self      :			Dict     )  ->			Dict:
        _A    		=  self.get_image_processor()
        _A    		=  self.get_tokenizer()

        _A    		=  CLIPProcessor(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     )







from __future__ import annotations

import math
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->bool:

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5	,    int(math.sqrt(lowerCAmelCase_ ) + 1 )	,    6 ):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True


__a	 =      [num for num in range(3, 10_0001, 2) if not is_prime(num)]
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->list[int]:
   if not isinstance(lowerCAmelCase_	,    lowerCAmelCase_ ):
      raise ValueError("""n must be an integer""" )
   if n <= 0:
      raise ValueError("""n must be >= 0""" )

   UpperCAmelCase      					=  []
   for num in range(len(lowerCAmelCase_ ) ):
      UpperCAmelCase      					=  0
      while 2 * i * i <= odd_composites[num]:
         UpperCAmelCase      					=  odd_composites[num] - 2 * i * i
         if is_prime(lowerCAmelCase_ ):
            break
         i += 1
      else:
         list_nums.append(odd_composites[num] )
         if len(lowerCAmelCase_ ) == n:
            return list_nums

   return []
def 	_UpperCamelCase					(    )    ->int:
   return compute_nums(1 )[0]


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



