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    ):

      def        __a		(	self				:      Any   )							->		Any:


         '''simple docstring'''

         lowercase							=		tempfile.mkdtemp()

         # fmt: off
         lowercase							=		["""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
         lowercase							=		dict(zip(lowerCamelCase_							,							range(len(lowerCamelCase_   )   )   )   )
         lowercase							=		["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
         lowercase							=		{"""unk_token""": """<unk>"""}

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

         lowercase							=		{
             """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],
         }
         lowercase							=		os.path.join(self.tmpdirname							,							lowerCamelCase_   )
         with open(self.image_processor_file							,							'''w'''							,							encoding='''utf-8'''   ) as fp:
            json.dump(lowerCamelCase_							,							lowerCamelCase_   )

      def        __a		(	self				:      Optional[int]							,							**__lowerCamelCase				:      str   )							->		List[Any]:


         '''simple docstring'''

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

      def        __a		(	self				:      Any							,							**__lowerCamelCase				:      Optional[int]   )							->		Optional[int]:


         '''simple docstring'''

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

      def        __a		(	self				:      Union[str, Any]							,							**__lowerCamelCase				:      Union[str, Any]   )							->		str:


         '''simple docstring'''

         return CLIPImageProcessor.from_pretrained(self.tmpdirname							,							**lowerCamelCase_   )

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


         '''simple docstring'''

         shutil.rmtree(self.tmpdirname   )

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


         '''simple docstring'''

         lowercase							=		[np.random.randint(2_55							,							size=(3, 30, 4_00)							,							dtype=np.uinta   )]

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

         return image_inputs

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


         '''simple docstring'''

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

         lowercase							=		CLIPProcessor(tokenizer=lowerCamelCase_							,							image_processor=lowerCamelCase_   )
         processor_slow.save_pretrained(self.tmpdirname   )
         lowercase							=		CLIPProcessor.from_pretrained(self.tmpdirname							,							use_fast=lowerCamelCase_   )

         lowercase							=		CLIPProcessor(tokenizer=lowerCamelCase_							,							image_processor=lowerCamelCase_   )
         processor_fast.save_pretrained(self.tmpdirname   )
         lowercase							=		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							,							lowerCamelCase_   )
         self.assertIsInstance(processor_fast.tokenizer							,							lowerCamelCase_   )

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

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


         '''simple docstring'''

         lowercase							=		CLIPProcessor(tokenizer=self.get_tokenizer()							,							image_processor=self.get_image_processor()   )
         processor.save_pretrained(self.tmpdirname   )

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

         lowercase							=		CLIPProcessor.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        __a		(	self				:      Optional[Any]   )							->		Optional[Any]:


         '''simple docstring'''

         lowercase							=		self.get_image_processor()
         lowercase							=		self.get_tokenizer()

         lowercase							=		CLIPProcessor(tokenizer=lowerCamelCase_							,							image_processor=lowerCamelCase_   )

         lowercase							=		self.prepare_image_inputs()

         lowercase							=		image_processor(lowerCamelCase_							,							return_tensors='''np'''   )
         lowercase							=		processor(images=lowerCamelCase_							,							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        __a		(	self				:      Union[str, Any]   )							->		Tuple:


         '''simple docstring'''

         lowercase							=		self.get_image_processor()
         lowercase							=		self.get_tokenizer()

         lowercase							=		CLIPProcessor(tokenizer=lowerCamelCase_							,							image_processor=lowerCamelCase_   )

         lowercase							=		"""lower newer"""

         lowercase							=		processor(text=lowerCamelCase_   )

         lowercase							=		tokenizer(lowerCamelCase_   )

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

      def        __a		(	self				:      Dict   )							->		str:


         '''simple docstring'''

         lowercase							=		self.get_image_processor()
         lowercase							=		self.get_tokenizer()

         lowercase							=		CLIPProcessor(tokenizer=lowerCamelCase_							,							image_processor=lowerCamelCase_   )

         lowercase							=		"""lower newer"""
         lowercase							=		self.prepare_image_inputs()

         lowercase							=		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        __a		(	self				:      Dict   )							->		int:


         '''simple docstring'''

         lowercase							=		self.get_image_processor()
         lowercase							=		self.get_tokenizer()

         lowercase							=		CLIPProcessor(tokenizer=lowerCamelCase_							,							image_processor=lowerCamelCase_   )

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

         lowercase							=		processor.batch_decode(lowerCamelCase_   )
         lowercase							=		tokenizer.batch_decode(lowerCamelCase_   )

         self.assertListEqual(lowerCamelCase_							,							lowerCamelCase_   )



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


         '''simple docstring'''

         lowercase							=		self.get_image_processor()
         lowercase							=		self.get_tokenizer()

         lowercase							=		CLIPProcessor(tokenizer=lowerCamelCase_							,							image_processor=lowerCamelCase_   )

         lowercase							=		"""lower newer"""
         lowercase							=		self.prepare_image_inputs()

         lowercase							=		processor(text=lowerCamelCase_							,							images=lowerCamelCase_   )

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






from __future__ import annotations

import math






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



			"""simple docstring"""







			if num <= 0:
						lowercase							=		f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(UpperCAmelCase       )

			lowercase							=		[True] * (num + 1)
			lowercase							=		[]
			lowercase							=		2
			lowercase							=		int(math.sqrt(UpperCAmelCase       )       )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(UpperCAmelCase       )

									# Set multiples of start be False
									for i in range(start * start,		num + 1,		UpperCAmelCase       ):
												if sieve[i] is True:
															lowercase							=		False

						start += 1

			for j in range(end + 1,		num + 1       ):
						if sieve[j] is True:
									prime.append(UpperCAmelCase       )

			return prime


if __name__ == "__main__":
						print(prime_sieve(int(input("Enter a positive integer: ").strip())))





