




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list







class 		__UpperCAmelCase(		unittest.TestCase			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Any:






         """simple docstring"""
         a__  			=							"""<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """     )
         a__  			=							dict(zip(A    ,					range(len(A     )     )     )     )

         a__  			=							{
             """pad_token""": """<pad>""",
             """unk_token""": """<unk>""",
             """bos_token""": """<s>""",
             """eos_token""": """</s>""",
         }
         a__  			=							{
             """feature_size""": 1,
             """padding_value""": 0.0,
             """sampling_rate""": 1_60_00,
             """return_attention_mask""": False,
             """do_normalize""": True,
         }

         a__  			=							tempfile.mkdtemp()
         a__  			=							os.path.join(self.tmpdirname    ,					VOCAB_FILES_NAMES["""vocab_file"""]     )
         a__  			=							os.path.join(self.tmpdirname    ,					A     )
         with open(self.vocab_file    ,					"""w"""    ,					encoding="""utf-8"""     ) as fp:
             fp.write(json.dumps(A     ) + """\n"""     )

         with open(self.feature_extraction_file    ,					"""w"""    ,					encoding="""utf-8"""     ) as fp:
             fp.write(json.dumps(A     ) + """\n"""     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					**A			:							str     )      ->      Dict:






         """simple docstring"""
         a__  			=							self.add_kwargs_tokens_map.copy()
         kwargs.update(A     )
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname    ,					**A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					**A			:							str     )      ->      Any:






         """simple docstring"""
         return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname    ,					**A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]     )      ->      Optional[int]:






         """simple docstring"""
         shutil.rmtree(self.tmpdirname     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      int:






         """simple docstring"""
         a__  			=							self.get_tokenizer()
         a__  			=							self.get_feature_extractor()

         a__  			=							WavaVecaProcessor(tokenizer=A    ,					feature_extractor=A     )

         processor.save_pretrained(self.tmpdirname     )
         a__  			=							WavaVecaProcessor.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 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							WavaVecaProcessor(tokenizer=self.get_tokenizer()    ,					feature_extractor=self.get_feature_extractor()     )
         processor.save_pretrained(self.tmpdirname     )

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

         a__  			=							WavaVecaProcessor.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 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      str:






         """simple docstring"""
         a__  			=							self.get_feature_extractor()
         a__  			=							self.get_tokenizer()

         a__  			=							WavaVecaProcessor(tokenizer=A    ,					feature_extractor=A     )

         a__  			=							floats_list((3, 10_00)     )

         a__  			=							feature_extractor(A    ,					return_tensors="""np"""     )
         a__  			=							processor(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 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]     )      ->      str:






         """simple docstring"""
         a__  			=							self.get_feature_extractor()
         a__  			=							self.get_tokenizer()

         a__  			=							WavaVecaProcessor(tokenizer=A    ,					feature_extractor=A     )

         a__  			=							"""This is a test string"""

         a__  			=							processor(text=A     )

         a__  			=							tokenizer(A     )

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

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






         """simple docstring"""
         a__  			=							self.get_feature_extractor()
         a__  			=							self.get_tokenizer()

         a__  			=							WavaVecaProcessor(tokenizer=A    ,					feature_extractor=A     )

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

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

         self.assertListEqual(A    ,					A     )






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






         """simple docstring"""
         a__  			=							self.get_feature_extractor()
         a__  			=							self.get_tokenizer()

         a__  			=							WavaVecaProcessor(tokenizer=A    ,					feature_extractor=A     )

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












from itertools import count
from math import asin, pi, sqrt
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    return (
        (1 - 2 * point) * sqrt(point - point**2		) + 2 * point + asin(sqrt(1 - point		)		)
    ) / 4
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    a__  			=							(circles_number + 1 - sqrt(2 * circles_number		)) / (
        2 * (circles_number**2 + 1)
    )
    a__  			=							circles_number * intersection_y

    a__  			=							intersection_x * intersection_y / 2
    a__  			=							circle_bottom_arc_integral(
        1 / 2		) - circle_bottom_arc_integral(UpperCAmelCase_		)

    return triangle_area + concave_region_area
def 							__lowerCAmelCase			(UpperCAmelCase_ = 1 / 1_000		):
    a__  			=							(1 - pi / 4) / 4

    for n in count(1		):
        if concave_triangle_area(UpperCAmelCase_		) / l_section_area < fraction:
            return n

    return -1


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






