





'''simple docstring'''

import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list


@require_torchaudio
@require_sentencepiece
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			"""laion/clap-htsat-unfused"""
        _SCREAMING_SNAKE_CASE									=			tempfile.mkdtemp()


    def 			snake_case_(		self ,		**A       )		->   int:
        return RobertaTokenizer.from_pretrained(self.checkpoint ,		**A       )


    def 			snake_case_(		self ,		**A       )		->   Optional[int]:
        return ClapFeatureExtractor.from_pretrained(self.checkpoint ,		**A       )


    def 			snake_case_(		self       )		->   Dict:
        shutil.rmtree(self.tmpdirname       )


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor()

        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=A ,		feature_extractor=A       )

        processor.save_pretrained(self.tmpdirname       )
        _SCREAMING_SNAKE_CASE									=			ClapProcessor.from_pretrained(self.tmpdirname       )

        self.assertEqual(processor.tokenizer.get_vocab() ,		tokenizer.get_vocab()       )
        self.assertIsInstance(processor.tokenizer ,		A       )

        self.assertEqual(processor.feature_extractor.to_json_string() ,		feature_extractor.to_json_string()       )
        self.assertIsInstance(processor.feature_extractor ,		A       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=self.get_tokenizer() ,		feature_extractor=self.get_feature_extractor()       )
        processor.save_pretrained(self.tmpdirname       )

        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer(bos_token="""(BOS)""" ,		eos_token="""(EOS)"""       )
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor(do_normalize=A ,		padding_value=1.0       )

        _SCREAMING_SNAKE_CASE									=			ClapProcessor.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.feature_extractor.to_json_string() ,		feature_extractor_add_kwargs.to_json_string()       )
        self.assertIsInstance(processor.feature_extractor ,		A       )


    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=A ,		feature_extractor=A       )

        _SCREAMING_SNAKE_CASE									=			floats_list((3, 1000)       )

        _SCREAMING_SNAKE_CASE									=			feature_extractor(A ,		return_tensors="""np"""       )
        _SCREAMING_SNAKE_CASE									=			processor(audios=A ,		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       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=A ,		feature_extractor=A       )

        _SCREAMING_SNAKE_CASE									=			"""This is a test string"""

        _SCREAMING_SNAKE_CASE									=			processor(text=A       )

        _SCREAMING_SNAKE_CASE									=			tokenizer(A       )

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


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=A ,		feature_extractor=A       )

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

        _SCREAMING_SNAKE_CASE									=			processor.batch_decode(A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.batch_decode(A       )

        self.assertListEqual(A ,		A       )





    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=A ,		feature_extractor=A       )

        self.assertListEqual(
            processor.model_input_names[2:] ,		feature_extractor.model_input_names ,		msg="""`processor` and `feature_extractor` model input names do not match""" ,		)





import colorsys

from PIL import Image  # type: ignore




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				int  ):
 __a :  Any						=  x
 __a :  List[Any]						=  y
 for step in range(lowerCAmelCase__  ):  # noqa: B007
  __a :  List[Any]						=  a * a - b * b + x
  __a :  Tuple						=  2 * a * b + y
  __a :  Optional[int]						=  a_new

  # divergence happens for all complex number with an absolute value
  # greater than 4
  if a * a + b * b > 4:
   break
 return step / (max_step - 1)




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return (2_5_5, 2_5_5, 2_5_5)




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return tuple(round(i * 2_5_5  ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__  ,   1  ,   1  )  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				int = 8_0_0  ,   lowerCAmelCase__							:				int = 6_0_0  ,   lowerCAmelCase__							:				float = -0.6  ,   lowerCAmelCase__							:				float = 0  ,   lowerCAmelCase__							:				float = 3.2  ,   lowerCAmelCase__							:				int = 5_0  ,   lowerCAmelCase__							:				bool = True  ,   ):
 __a :  int						=  Image.new('''RGB'''  ,   (image_width, image_height)  )
 __a :  Dict						=  img.load()

 # loop through the image-coordinates
 for image_x in range(lowerCAmelCase__  ):
  for image_y in range(lowerCAmelCase__  ):
   # determine the figure-coordinates based on the image-coordinates
   __a :  Optional[Any]						=  figure_width / image_width * image_height
   __a :  str						=  figure_center_x + (image_x / image_width - 0.5) * figure_width
   __a :  str						=  figure_center_y + (image_y / image_height - 0.5) * figure_height

   __a :  Tuple						=  get_distance(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )

   # color the corresponding pixel based on the selected coloring-function
   if use_distance_color_coding:
    __a :  Optional[int]						=  get_color_coded_rgb(lowerCAmelCase__  )
   else:
    __a :  Optional[Any]						=  get_black_and_white_rgb(lowerCAmelCase__  )

 return img


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # colored version, full figure
      lowercase__       =get_image()

      # uncomment for colored version, different section, zoomed in
      # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
      # figure_width = 0.8)

      # uncomment for black and white version, full figure
      # img = get_image(use_distance_color_coding = False)

      # uncomment to save the image
      # img.save("mandelbrot.png")

      img.show()
