






'''simple docstring'''







import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

__lowerCAmelCase					   =       0.25 + 0.0J
__lowerCAmelCase					   =       -0.4 + 0.6J
__lowerCAmelCase					   =       -0.1 + 0.651J
__lowerCAmelCase					   =       -2.0
__lowerCAmelCase					   =       56
__lowerCAmelCase					   =       2.0
__lowerCAmelCase					   =       6_66


def 			__UpperCamelCase				(			lowercase_					:     complex      ,					lowercase_					:     numpy.ndarray      ):
       """simple docstring"""




       return numpy.exp(lowercase_      ) + c_parameter


def 			__UpperCamelCase				(			lowercase_					:     complex      ,					lowercase_					:     numpy.ndarray      ):
       """simple docstring"""




       return z_values * z_values + c_parameter


def 			__UpperCamelCase				(			lowercase_					:     float      ,					lowercase_					:     int      ):
       """simple docstring"""




       a_	    = numpy.linspace(-window_size      ,					lowercase_      ,					lowercase_      )
       a_	    = x.reshape((nb_pixels, 1)      )
       a_	    = numpy.linspace(-window_size      ,					lowercase_      ,					lowercase_      )
       a_	    = y.reshape((1, nb_pixels)      )
       return x + 1.0J * y


def 			__UpperCamelCase				(			lowercase_					:     Callable[[Any, numpy.ndarray], numpy.ndarray]      ,					lowercase_					:     Any      ,					lowercase_					:     int      ,					lowercase_					:     numpy.ndarray      ,					lowercase_					:     float | None = None      ,					):
       """simple docstring"""




       a_	    = z_a.astype('complex64'      )
       for _ in range(lowercase_      ):
              a_	    = eval_function(lowercase_      ,					lowercase_      )
              if infinity is not None:
                     numpy.nan_to_num(lowercase_      ,					copy=lowercase_      ,					nan=lowercase_      )
                     a_	    = infinity
       return z_n


def 			__UpperCamelCase				(			lowercase_					:     str      ,					lowercase_					:     Any      ,					lowercase_					:     float      ,					lowercase_					:     numpy.ndarray      ,					):
       """simple docstring"""




       a_	    = (abs(lowercase_      )).transpose()
       a_	    = abs_z_final[::-1, :]
       pyplot.matshow(abs_z_final < escape_radius      )
       pyplot.title(F'Julia set of ${function_label}$, $c={function_params}$'      )
       pyplot.show()


def 			__UpperCamelCase				(			):
       """simple docstring"""




       warnings.filterwarnings(
           'ignore'      ,					category=lowercase_      ,					message='overflow encountered in multiply'      )
       warnings.filterwarnings(
           'ignore'      ,					category=lowercase_      ,					message='invalid value encountered in multiply'      ,					)
       warnings.filterwarnings(
           'ignore'      ,					category=lowercase_      ,					message='overflow encountered in absolute'      )
       warnings.filterwarnings(
           'ignore'      ,					category=lowercase_      ,					message='overflow encountered in exp'      )


if __name__ == "__main__":
  __lowerCAmelCase					   =       prepare_grid(window_size, nb_pixels)

  ignore_overflow_warnings()  # See file header for explanations

  __lowerCAmelCase					   =       24
  __lowerCAmelCase					   =       2 * abs(c_cauliflower) + 1
  __lowerCAmelCase					   =       iterate_function(
      eval_quadratic_polynomial,
      c_cauliflower,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_cauliflower, escape_radius, z_final)

  __lowerCAmelCase					   =       64
  __lowerCAmelCase					   =       2 * abs(c_polynomial_a) + 1
  __lowerCAmelCase					   =       iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

  __lowerCAmelCase					   =       1_61
  __lowerCAmelCase					   =       2 * abs(c_polynomial_a) + 1
  __lowerCAmelCase					   =       iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

  __lowerCAmelCase					   =       12
  __lowerCAmelCase					   =       1_0000.0
  __lowerCAmelCase					   =       iterate_function(
      eval_exponential,
      c_exponential,
      nb_iterations,
      z_a + 2,
      infinity=1.0e10,
  )
  show_results("e^z+c", c_exponential, escape_radius, z_final)












'''simple docstring'''







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      __SCREAMING_SNAKE_CASE					(unittest.TestCase					):


  """simple docstring"""


  def       _a					( self  ):






         """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(UpperCamelCase__      ,  range(len(UpperCamelCase__  )  )  )  )

         a_	    = {
             'pad_token': '<pad>',
             'unk_token': '<unk>',
             'bos_token': '<s>',
             'eos_token': '</s>',
         }
         a_	    = {
             'feature_size': 1,
             'padding_value': 0.0,
             'sampling_rate': 16_000,
             '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      ,  UpperCamelCase__  )
         with open(self.vocab_file      ,  'w'      ,  encoding='utf-8'  ) as fp:
                fp.write(json.dumps(UpperCamelCase__  ) + '\n'  )

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







  def       _a					( self      ,  **UpperCamelCase__  ):






         """simple docstring"""


         a_	    = self.add_kwargs_tokens_map.copy()
         kwargs.update(UpperCamelCase__  )
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,  **UpperCamelCase__  )







  def       _a					( self      ,  **UpperCamelCase__  ):






         """simple docstring"""


         return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,  **UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         shutil.rmtree(self.tmpdirname  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_tokenizer()
         a_	    = self.get_feature_extractor()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         processor.save_pretrained(self.tmpdirname  )
         a_	    = WavaVecaProcessor.from_pretrained(self.tmpdirname  )

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

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







  def       _a					( self  ):






         """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=UpperCamelCase__      ,  padding_value=1.0  )

         a_	    = WavaVecaProcessor.from_pretrained(
             self.tmpdirname      ,  bos_token='(BOS)'      ,  eos_token='(EOS)'      ,  do_normalize=UpperCamelCase__      ,  padding_value=1.0  )

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

         self.assertEqual(processor.feature_extractor.to_json_string()      ,  feature_extractor_add_kwargs.to_json_string()  )
         self.assertIsInstance(processor.feature_extractor      ,  UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         a_	    = floats_list((3, 1_000)  )

         a_	    = feature_extractor(UpperCamelCase__      ,  return_tensors='np'  )
         a_	    = processor(UpperCamelCase__      ,  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       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

         a_	    = 'This is a test string'

         a_	    = processor(text=UpperCamelCase__  )

         a_	    = tokenizer(UpperCamelCase__  )

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







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

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

         a_	    = processor.batch_decode(UpperCamelCase__  )
         a_	    = tokenizer.batch_decode(UpperCamelCase__  )

         self.assertListEqual(UpperCamelCase__      ,  UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.get_feature_extractor()
         a_	    = self.get_tokenizer()

         a_	    = WavaVecaProcessor(tokenizer=UpperCamelCase__      ,  feature_extractor=UpperCamelCase__  )

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




