




'''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 		lowerCAmelCase_     (    unittest.TestCase				):







  '''simple docstring'''

  def  _snake_case			(      self							:				Tuple     )       ->						Any:







       '''simple docstring'''



       A:						Optional[int]						=						tempfile.mkdtemp()

       # fmt: off
       A:						Tuple						=						["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:						Tuple						=						dict(zip(_lowerCAmelCase ,   range(len(_lowerCAmelCase     )     )     )     )
       A:						Optional[Any]						=						["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
       A:						List[Any]						=						{"unk_token": "<unk>"}

       A:						Optional[int]						=						os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''vocab_file''']     )
       A:						Optional[int]						=						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     )     )

       A:						List[str]						=						{
           "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:						Tuple						=						os.path.join(self.tmpdirname ,   _lowerCAmelCase     )
       with open(self.image_processor_file ,   '''w''' ,   encoding='''utf-8'''     ) as fp:
            json.dump(_lowerCAmelCase ,   _lowerCAmelCase     )



  def  _snake_case			(      self							:				List[Any] ,   **SCREAMING_SNAKE_CASE_							:				Optional[int]     )       ->						Optional[Any]:







       '''simple docstring'''



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



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







       '''simple docstring'''



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



  def  _snake_case			(      self							:				int ,   **SCREAMING_SNAKE_CASE_							:				int     )       ->						int:







       '''simple docstring'''



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



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







       '''simple docstring'''



       shutil.rmtree(self.tmpdirname     )



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







       '''simple docstring'''



       A:						Optional[int]						=						[np.random.randint(2_55 ,   size=(3, 30, 4_00) ,   dtype=np.uinta     )]

       A:						List[str]						=						[Image.fromarray(np.moveaxis(_lowerCAmelCase ,   0 ,   -1     )     ) for x in image_inputs]

       return image_inputs



  def  _snake_case			(      self							:				List[str]     )       ->						Dict:







       '''simple docstring'''



       A:						Union[str, Any]						=						self.get_tokenizer()
       A:						Union[str, Any]						=						self.get_rust_tokenizer()
       A:						Dict						=						self.get_image_processor()

       A:						int						=						CLIPSegProcessor(tokenizer=_lowerCAmelCase ,   image_processor=_lowerCAmelCase     )
       processor_slow.save_pretrained(self.tmpdirname     )
       A:						Dict						=						CLIPSegProcessor.from_pretrained(self.tmpdirname ,   use_fast=_lowerCAmelCase     )

       A:						List[Any]						=						CLIPSegProcessor(tokenizer=_lowerCAmelCase ,   image_processor=_lowerCAmelCase     )
       processor_fast.save_pretrained(self.tmpdirname     )
       A:						Dict						=						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 ,   _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  _snake_case			(      self							:				str     )       ->						int:







       '''simple docstring'''



       A:						Any						=						CLIPSegProcessor(tokenizer=self.get_tokenizer() ,   image_processor=self.get_image_processor()     )
       processor.save_pretrained(self.tmpdirname     )

       A:						str						=						self.get_tokenizer(bos_token='''(BOS)''' ,   eos_token='''(EOS)'''     )
       A:						int						=						self.get_image_processor(do_normalize=_lowerCAmelCase ,   padding_value=1.0     )

       A:						Optional[Any]						=						CLIPSegProcessor.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  _snake_case			(      self							:				Optional[int]     )       ->						int:







       '''simple docstring'''



       A:						List[Any]						=						self.get_image_processor()
       A:						Tuple						=						self.get_tokenizer()

       A:						Any						=						CLIPSegProcessor(tokenizer=_lowerCAmelCase ,   image_processor=_lowerCAmelCase     )

       A:						int						=						self.prepare_image_inputs()

       A:						Optional[int]						=						image_processor(_lowerCAmelCase ,   return_tensors='''np'''     )
       A:						str						=						processor(images=_lowerCAmelCase ,   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  _snake_case			(      self							:				Optional[int]     )       ->						Tuple:







       '''simple docstring'''



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

       A:						Optional[Any]						=						CLIPSegProcessor(tokenizer=_lowerCAmelCase ,   image_processor=_lowerCAmelCase     )

       A:						str						=						"lower newer"

       A:						List[str]						=						processor(text=_lowerCAmelCase     )

       A:						Tuple						=						tokenizer(_lowerCAmelCase     )

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



  def  _snake_case			(      self							:				Tuple     )       ->						Optional[int]:







       '''simple docstring'''



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

       A:						Optional[int]						=						CLIPSegProcessor(tokenizer=_lowerCAmelCase ,   image_processor=_lowerCAmelCase     )

       A:						Optional[int]						=						"lower newer"
       A:						List[Any]						=						self.prepare_image_inputs()

       A:						str						=						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  _snake_case			(      self							:				Union[str, Any]     )       ->						Optional[int]:







       '''simple docstring'''



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

       A:						List[Any]						=						CLIPSegProcessor(tokenizer=_lowerCAmelCase ,   image_processor=_lowerCAmelCase     )

       A:						Dict						=						self.prepare_image_inputs()
       A:						Dict						=						self.prepare_image_inputs()

       A:						Dict						=						processor(images=_lowerCAmelCase ,   visual_prompt=_lowerCAmelCase     )

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

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




  def  _snake_case			(      self							:				str     )       ->						List[str]:







       '''simple docstring'''



       A:						Dict						=						self.get_image_processor()
       A:						Any						=						self.get_tokenizer()

       A:						Tuple						=						CLIPSegProcessor(tokenizer=_lowerCAmelCase ,   image_processor=_lowerCAmelCase     )

       A:						Optional[Any]						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       A:						Optional[Any]						=						processor.batch_decode(_lowerCAmelCase     )
       A:						List[Any]						=						tokenizer.batch_decode(_lowerCAmelCase     )

       self.assertListEqual(_lowerCAmelCase ,   _lowerCAmelCase     )












import os
from math import logaa



def 					UpperCAmelCase__     (   lowerCamelCase = "base_exp.txt"      ):
  lowercase			:float							=  0
  lowercase			:str							=  0
  for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase      ),	lowerCamelCase      )      )      ):
    lowercase		,			lowercase			:str							=  list(map(lowerCamelCase,	line.split(","      )      )      )
    if x * logaa(lowerCamelCase      ) > largest:
      lowercase			:Optional[Any]							=  x * logaa(lowerCamelCase      )
      lowercase			:Any							=  i + 1
  return result


if __name__ == "__main__":
     print(solution())



