

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 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	'laion/clap-htsat-unfused'
         lowercase__   :       List[Any]				=	tempfile.mkdtemp()





    def 				snake_case_    (				self						,       **a):
         return RobertaTokenizer.from_pretrained(self.checkpoint						,       **a)





    def 				snake_case_    (				self						,       **a):
         return ClapFeatureExtractor.from_pretrained(self.checkpoint						,       **a)





    def 				snake_case_    (				self):
         shutil.rmtree(self.tmpdirname)





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	self.get_tokenizer()
         lowercase__   :       Optional[int]				=	self.get_feature_extractor()

         lowercase__   :       Optional[Any]				=	ClapProcessor(tokenizer=a						,       feature_extractor=a)

         processor.save_pretrained(self.tmpdirname)
         lowercase__   :       Union[str, Any]				=	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):
         lowercase__   :       Tuple				=	ClapProcessor(tokenizer=self.get_tokenizer()						,       feature_extractor=self.get_feature_extractor())
         processor.save_pretrained(self.tmpdirname)

         lowercase__   :       Dict				=	self.get_tokenizer(bos_token='(BOS)'						,       eos_token='(EOS)')
         lowercase__   :       str				=	self.get_feature_extractor(do_normalize=a						,       padding_value=1.0)

         lowercase__   :       Optional[int]				=	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):
         lowercase__   :       Any				=	self.get_feature_extractor()
         lowercase__   :       int				=	self.get_tokenizer()

         lowercase__   :       Optional[int]				=	ClapProcessor(tokenizer=a						,       feature_extractor=a)

         lowercase__   :       Dict				=	floats_list((3, 1000))

         lowercase__   :       Union[str, Any]				=	feature_extractor(a						,       return_tensors='np')
         lowercase__   :       str				=	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):
         lowercase__   :       Dict				=	self.get_feature_extractor()
         lowercase__   :       Tuple				=	self.get_tokenizer()

         lowercase__   :       str				=	ClapProcessor(tokenizer=a						,       feature_extractor=a)

         lowercase__   :       Dict				=	'This is a test string'

         lowercase__   :       Optional[Any]				=	processor(text=a)

         lowercase__   :       List[Any]				=	tokenizer(a)

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





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

         lowercase__   :       Dict				=	ClapProcessor(tokenizer=a						,       feature_extractor=a)

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

         lowercase__   :       int				=	processor.batch_decode(a)
         lowercase__   :       int				=	tokenizer.batch_decode(a)

         self.assertListEqual(a						,       a)





    def 				snake_case_    (				self):
         lowercase__   :       Optional[Any]				=	self.get_feature_extractor()
         lowercase__   :       Dict				=	self.get_tokenizer()

         lowercase__   :       List[Any]				=	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'						,       )




from __future__ import annotations

from collections.abc import Callable

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Callable[[int | float], int | float]     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int = 100     ,					):



     '''simple docstring'''





     lowercase__   :       Tuple				=	x_start
     lowercase__   :       Tuple				=	fnc(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[Any]				=	0.0
     for _ in range(SCREAMING_SNAKE_CASE_      ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          lowercase__   :       Any				=	(x_end - x_start) / steps + xa
          lowercase__   :       Optional[Any]				=	fnc(SCREAMING_SNAKE_CASE_      )
          area += abs(fxa + fxa      ) * (xa - xa) / 2
          # Increment step
          lowercase__   :       Any				=	xa
          lowercase__   :       str				=	fxa
     return area


if __name__ == "__main__":

       def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple      ):



            '''simple docstring'''





            return x**3 + x**2

       print('''f(x) = x^3 + x^2''')
       print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
       snake_case_        =			10
       while i <= 100_000:
              print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
              i *= 10

