




"""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 CLIPSegProcessor, ViTImageProcessor




@require_vision
class      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	List[str]				)				->							Any:




     '''simple docstring'''

     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(snake_case_     ,			range(len(snake_case_				)				)				)				)
     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(snake_case_				) + "\n"				)
     with open(self.merges_file     ,			"w"     ,			encoding="utf-8"				) as fp:
      fp.write("\n".join(snake_case_				)				)

     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     ,			snake_case_				)
     with open(self.image_processor_file     ,			"w"     ,			encoding="utf-8"				) as fp:
      json.dump(snake_case_     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Dict     ,			**snake_case_						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     return CLIPTokenizer.from_pretrained(self.tmpdirname     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	str     ,			**snake_case_						:	Dict				)				->							str:




     '''simple docstring'''

     return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	List[Any]     ,			**snake_case_						:	Optional[int]				)				->							Any:




     '''simple docstring'''

     return ViTImageProcessor.from_pretrained(self.tmpdirname     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	List[Any]				)				->							Union[str, Any]:




     '''simple docstring'''

     shutil.rmtree(self.tmpdirname				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							str:




     '''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 							__magic_name__							(   self						:	List[Any]				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    self.get_tokenizer()
     A__	=    self.get_rust_tokenizer()
     A__	=    self.get_image_processor()

     A__	=    CLIPSegProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)
     processor_slow.save_pretrained(self.tmpdirname				)
     A__	=    CLIPSegProcessor.from_pretrained(self.tmpdirname     ,			use_fast=snake_case_				)

     A__	=    CLIPSegProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)
     processor_fast.save_pretrained(self.tmpdirname				)
     A__	=    CLIPSegProcessor.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     ,			snake_case_				)
     self.assertIsInstance(processor_fast.tokenizer     ,			snake_case_				)

     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     ,			snake_case_				)
     self.assertIsInstance(processor_fast.image_processor     ,			snake_case_				)




    def 							__magic_name__							(   self						:	int				)				->							List[str]:




     '''simple docstring'''

     A__	=    CLIPSegProcessor(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__	=    CLIPSegProcessor.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 							__magic_name__							(   self						:	Union[str, Any]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    CLIPSegProcessor(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 							__magic_name__							(   self						:	List[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    CLIPSegProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

     A__	=    "lower newer"

     A__	=    processor(text=snake_case_				)

     A__	=    tokenizer(snake_case_				)

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




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Dict:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    CLIPSegProcessor(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 							__magic_name__							(   self						:	Any				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    CLIPSegProcessor(tokenizer=snake_case_     ,			image_processor=snake_case_				)

     A__	=    self.prepare_image_inputs()
     A__	=    self.prepare_image_inputs()

     A__	=    processor(images=snake_case_     ,			visual_prompt=snake_case_				)

     self.assertListEqual(list(inputs.keys()				)     ,			["pixel_values", "conditional_pixel_values"]				)

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




    def 							__magic_name__							(   self						:	Optional[int]				)				->							Any:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_tokenizer()

     A__	=    CLIPSegProcessor(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_				)











"""simple docstring"""



import random

from .binary_exp_mod import bin_exp_mod







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=10_00						)						->		Optional[Any]:
 if n < 2:
  return False

 if n % 2 == 0:
  return n == 2

 # this means n is odd
 A__	=    n - 1
 A__	=    0
 while d % 2 == 0:
  d /= 2
  exp += 1

 # n - 1=d*(2**exp)
 A__	=    0
 while count < prec:
  A__	=    random.randint(2      ,			n - 1						)
  A__	=    bin_exp_mod(lowercase_      ,			lowercase_      ,			lowercase_						)
  if b != 1:
   A__	=    True
   for _ in range(lowercase_						):
    if b == n - 1:
     A__	=    False
     break
    A__	=    b * b
    b %= n
   if flag:
    return False
   count += 1
 return True


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	abs(int(input("Enter bound : ").strip()))
      print("Here's the list of primes:")
      print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))





