

"""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      __a							(unittest.TestCase):






     '''simple docstring'''

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tempfile.mkdtemp()

           # fmt: off
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	["""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
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dict(zip(_a	,    range(len(_a  )  )  )  )
           SCREAMING_SNAKE_CASE__   :      int      		=	["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
           SCREAMING_SNAKE_CASE__   :      int      		=	{"""unk_token""": """<unk>"""}

           SCREAMING_SNAKE_CASE__   :      Dict      		=	os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]  )
           SCREAMING_SNAKE_CASE__   :      str      		=	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(_a  ) + """\n"""  )
           with open(self.merges_file	,    """w"""	,    encoding="""utf-8"""  ) as fp:
                 fp.write("""\n""".join(_a  )  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{
               """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],
           }
           SCREAMING_SNAKE_CASE__   :      str      		=	os.path.join(self.tmpdirname	,    _a  )
           with open(self.image_processor_file	,    """w"""	,    encoding="""utf-8"""  ) as fp:
                 json.dump(_a	,    _a  )

     def 					_a			(						self	,    **_a  )  ->      Any:


           """simple docstring"""


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

     def 					_a			(						self	,    **_a  )  ->      Dict:


           """simple docstring"""


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

     def 					_a			(						self	,    **_a  )  ->      str:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           shutil.rmtree(self.tmpdirname  )

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	[np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta  )]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	[Image.fromarray(np.moveaxis(_a	,    0	,    -1  )  ) for x in image_inputs]

           return image_inputs

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.get_tokenizer()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_rust_tokenizer()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.get_image_processor()

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	CLIPProcessor(tokenizer=_a	,    image_processor=_a  )
           processor_slow.save_pretrained(self.tmpdirname  )
           SCREAMING_SNAKE_CASE__   :      int      		=	CLIPProcessor.from_pretrained(self.tmpdirname	,    use_fast=_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	CLIPProcessor(tokenizer=_a	,    image_processor=_a  )
           processor_fast.save_pretrained(self.tmpdirname  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	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	,    _a  )
           self.assertIsInstance(processor_fast.tokenizer	,    _a  )

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

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	CLIPProcessor(tokenizer=self.get_tokenizer()	,    image_processor=self.get_image_processor()  )
           processor.save_pretrained(self.tmpdirname  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_tokenizer(bos_token="""(BOS)"""	,    eos_token="""(EOS)"""  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	self.get_image_processor(do_normalize=_a	,    padding_value=1.0  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	CLIPProcessor.from_pretrained(
               self.tmpdirname	,    bos_token="""(BOS)"""	,    eos_token="""(EOS)"""	,    do_normalize=_a	,    padding_value=1.0  )

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

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

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Dict      		=	CLIPProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      Any      		=	image_processor(_a	,    return_tensors="""np"""  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	processor(images=_a	,    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  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Any      		=	CLIPProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""lower newer"""

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	processor(text=_a  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	tokenizer(_a  )

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

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	CLIPProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      str      		=	"""lower newer"""
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      int      		=	processor(text=_a	,    images=_a  )

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

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

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	CLIPProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           SCREAMING_SNAKE_CASE__   :      str      		=	processor.batch_decode(_a  )
           SCREAMING_SNAKE_CASE__   :      int      		=	tokenizer.batch_decode(_a  )

           self.assertListEqual(_a	,    _a  )





     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	CLIPProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""lower newer"""
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      int      		=	processor(text=_a	,    images=_a  )

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







"""simple docstring"""


from math import sqrt




def  _lowercase					(    __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(sqrt(__lowerCAmelCase							) + 1							)  ,					6							):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True




def  _lowercase					(    __lowerCAmelCase = 1_0001							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	0
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	1
      while count != nth and number < 3:
            number += 1
            if is_prime(__lowerCAmelCase							):
                  count += 1
      while count != nth:
            number += 2
            if is_prime(__lowerCAmelCase							):
                  count += 1
      return number


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




