


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 					snake_case__(				unittest.TestCase				):







						'''simple docstring'''




						def     lowercase_			(					self				)       ->     Optional[Any]:
										lowerCAmelCase_			:   Tuple     					=					'''<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(''' '''				)
										lowerCAmelCase_			:   Optional[int]     					=					dict(zip(__lowercase ,    range(len(__lowercase				)				)				)				)

										lowerCAmelCase_			:   List[str]     					=					{
										    '''pad_token''': '''<pad>''',
										    '''unk_token''': '''<unk>''',
										    '''bos_token''': '''<s>''',
										    '''eos_token''': '''</s>''',
										}
										lowerCAmelCase_			:   List[Any]     					=					{
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_6_0_0_0,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}

										lowerCAmelCase_			:   Tuple     					=					tempfile.mkdtemp()
										lowerCAmelCase_			:   str     					=					os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['''vocab_file''']				)
										lowerCAmelCase_			:   Optional[int]     					=					os.path.join(self.tmpdirname ,    __lowercase				)
										with open(self.vocab_file ,    '''w''' ,    encoding='''utf-8'''				) as fp:
														fp.write(json.dumps(__lowercase				) + '''\n'''				)

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




						def     lowercase_			(					self ,    **__lowercase				)       ->     str:
										lowerCAmelCase_			:   int     					=					self.add_kwargs_tokens_map.copy()
										kwargs.update(__lowercase				)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,    **__lowercase				)




						def     lowercase_			(					self ,    **__lowercase				)       ->     Optional[Any]:
										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,    **__lowercase				)




						def     lowercase_			(					self				)       ->     Tuple:
										shutil.rmtree(self.tmpdirname				)




						def     lowercase_			(					self				)       ->     List[str]:
										lowerCAmelCase_			:   Any     					=					self.get_tokenizer()
										lowerCAmelCase_			:   List[str]     					=					self.get_feature_extractor()

										lowerCAmelCase_			:   List[str]     					=					WavaVecaProcessor(tokenizer=__lowercase ,    feature_extractor=__lowercase				)

										processor.save_pretrained(self.tmpdirname				)
										lowerCAmelCase_			:   Any     					=					WavaVecaProcessor.from_pretrained(self.tmpdirname				)

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

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




						def     lowercase_			(					self				)       ->     List[str]:
										lowerCAmelCase_			:   Dict     					=					WavaVecaProcessor(tokenizer=self.get_tokenizer() ,    feature_extractor=self.get_feature_extractor()				)
										processor.save_pretrained(self.tmpdirname				)

										lowerCAmelCase_			:   List[str]     					=					self.get_tokenizer(bos_token='''(BOS)''' ,    eos_token='''(EOS)'''				)
										lowerCAmelCase_			:   int     					=					self.get_feature_extractor(do_normalize=__lowercase ,    padding_value=1.0				)

										lowerCAmelCase_			:   Dict     					=					WavaVecaProcessor.from_pretrained(
										    self.tmpdirname ,    bos_token='''(BOS)''' ,    eos_token='''(EOS)''' ,    do_normalize=__lowercase ,    padding_value=1.0				)

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

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




						def     lowercase_			(					self				)       ->     List[Any]:
										lowerCAmelCase_			:   List[str]     					=					self.get_feature_extractor()
										lowerCAmelCase_			:   Any     					=					self.get_tokenizer()

										lowerCAmelCase_			:   Dict     					=					WavaVecaProcessor(tokenizer=__lowercase ,    feature_extractor=__lowercase				)

										lowerCAmelCase_			:   Tuple     					=					floats_list((3, 1_0_0_0)				)

										lowerCAmelCase_			:   List[Any]     					=					feature_extractor(__lowercase ,    return_tensors='''np'''				)
										lowerCAmelCase_			:   Optional[int]     					=					processor(__lowercase ,    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     lowercase_			(					self				)       ->     Any:
										lowerCAmelCase_			:   Optional[int]     					=					self.get_feature_extractor()
										lowerCAmelCase_			:   Optional[int]     					=					self.get_tokenizer()

										lowerCAmelCase_			:   Tuple     					=					WavaVecaProcessor(tokenizer=__lowercase ,    feature_extractor=__lowercase				)

										lowerCAmelCase_			:   Tuple     					=					'''This is a test string'''

										lowerCAmelCase_			:   int     					=					processor(text=__lowercase				)

										lowerCAmelCase_			:   Union[str, Any]     					=					tokenizer(__lowercase				)

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




						def     lowercase_			(					self				)       ->     str:
										lowerCAmelCase_			:   Any     					=					self.get_feature_extractor()
										lowerCAmelCase_			:   Tuple     					=					self.get_tokenizer()

										lowerCAmelCase_			:   Union[str, Any]     					=					WavaVecaProcessor(tokenizer=__lowercase ,    feature_extractor=__lowercase				)

										lowerCAmelCase_			:   str     					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowerCAmelCase_			:   Any     					=					processor.batch_decode(__lowercase				)
										lowerCAmelCase_			:   List[str]     					=					tokenizer.batch_decode(__lowercase				)

										self.assertListEqual(__lowercase ,    __lowercase				)




						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   Optional[Any]     					=					self.get_feature_extractor()
										lowerCAmelCase_			:   int     					=					self.get_tokenizer()

										lowerCAmelCase_			:   Any     					=					WavaVecaProcessor(tokenizer=__lowercase ,    feature_extractor=__lowercase				)

										self.assertListEqual(
										    processor.model_input_names ,    feature_extractor.model_input_names ,    msg='''`processor` and `feature_extractor` model input names do not match''' ,    )
'''simple docstring'''
from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar







def 				__UpperCamelCase (					__UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					int			)							->	list:
  seed(1			)
  return [gauss(__UpperCAmelCase ,   __UpperCAmelCase			) for _ in range(__UpperCAmelCase			)]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					list			)							->	list:
  return [k for k in range(__UpperCAmelCase			) for _ in range(instance_count[k]			)]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					list			)							->	float:
  return sum(__UpperCAmelCase			) / instance_count







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	float:
  return instance_count / total_count







def 				__UpperCamelCase (					__UpperCAmelCase  :					list ,   __UpperCAmelCase  :					list ,   __UpperCAmelCase  :					int			)							->	float:
  lowercase				:							List[Any]									=   []  # An empty list to store all squared differences
  # iterate over number of elements in items
  for i in range(len(__UpperCAmelCase			)			):
    # for loop iterates over number of elements in inner layer of items
    for j in range(len(items[i]			)			):
      # appending squared differences to 'squared_diff' list
      squared_diff.append((items[i][j] - means[i]) ** 2			)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
  lowercase				:							List[str]									=   len(__UpperCAmelCase			)  # Number of classes in dataset
  return 1 / (total_count - n_classes) * sum(__UpperCAmelCase			)







def 				__UpperCamelCase (					__UpperCAmelCase  :					list ,   __UpperCAmelCase  :					list ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					list			)							->	list:
  lowercase				:							Union[str, Any]									=   []
  # for loop iterates over number of elements in list
  for i in range(len(__UpperCAmelCase			)			):
    # for loop iterates over number of inner items of each element
    for j in range(len(x_items[i]			)			):
      lowercase				:							Any									=   []  # to store all discriminant values of each item as a list
      # for loop iterates over number of classes we have in our dataset
      for k in range(len(__UpperCAmelCase			)			):
        # appending values of discriminants for each class to 'temp' list
        temp.append(
            x_items[i][j] * (means[k] / variance)
            - (means[k] ** 2 / (2 * variance))
            + log(probabilities[k]			)			)
      # appending discriminant values of each item to 'results' list
      results.append(__UpperCAmelCase			)

  return [result.index(max(__UpperCAmelCase			)			) for result in results]







def 				__UpperCamelCase (					__UpperCAmelCase  :					list ,   __UpperCAmelCase  :					list			)							->	float:
  lowercase				:							List[str]									=   sum(1 for i, j in zip(__UpperCAmelCase ,   __UpperCAmelCase			) if i == j			)
  # percentage of accuracy equals to number of correct predictions divided by number
  # of all data and multiplied by 100
  return (correct / len(__UpperCAmelCase			)) * 100


_A		:      Dict						   =TypeVar('''num''')







def 				__UpperCamelCase (					__UpperCAmelCase  :					Callable[[object], num] ,   __UpperCAmelCase  :					str ,   __UpperCAmelCase  :					str ,   __UpperCAmelCase  :					Callable[[num], bool] = lambda __UpperCAmelCase			: True ,   __UpperCAmelCase  :					str | None = None ,   )							->	num:
  while True:
    try:
      lowercase				:							int									=   input_type(input(__UpperCAmelCase			).strip() or default			)
      if condition(__UpperCAmelCase			):
        return user_input
      else:
        print(f"""{user_input}: {err_msg}"""			)
        continue
    except ValueError:
      print(
          f"""{user_input}: Incorrect input type, expected {input_type.__name__!r}"""			)







def 				__UpperCamelCase (					)							->	Optional[int]:
  while True:
    print(''' Linear Discriminant Analysis '''.center(50 ,   '''*'''			)			)
    print('''*''' * 50 ,   '''\n'''			)
    print('''First of all we should specify the number of classes that'''			)
    print('''we want to generate as training dataset'''			)
    # Trying to get number of classes
    lowercase				:							List[str]									=   valid_input(
        input_type=__UpperCAmelCase ,   condition=lambda __UpperCAmelCase			: x > 0 ,   input_msg='''Enter the number of classes (Data Groupings): ''' ,   err_msg='''Number of classes should be positive!''' ,   )

    print('''-''' * 100			)

    # Trying to get the value of standard deviation
    lowercase				:							Dict									=   valid_input(
        input_type=__UpperCAmelCase ,   condition=lambda __UpperCAmelCase			: x >= 0 ,   input_msg=(
            '''Enter the value of standard deviation'''
            '''(Default value is 1.0 for all classes): '''
        ) ,   err_msg='''Standard deviation should not be negative!''' ,   default='''1.0''' ,   )

    print('''-''' * 100			)

    # Trying to get number of instances in classes and theirs means to generate
    # dataset
    lowercase				:							str									=   []  # An empty list to store instance counts of classes in dataset
    for i in range(__UpperCAmelCase			):
      lowercase				:							List[Any]									=   valid_input(
          input_type=__UpperCAmelCase ,   condition=lambda __UpperCAmelCase			: x > 0 ,   input_msg=(f"""Enter The number of instances for class_{i+1}: """) ,   err_msg='''Number of instances should be positive!''' ,   )
      counts.append(__UpperCAmelCase			)
    print('''-''' * 100			)

    # An empty list to store values of user-entered means of classes
    lowercase				:							int									=   []
    for a in range(__UpperCAmelCase			):
      lowercase				:							int									=   valid_input(
          input_type=__UpperCAmelCase ,   input_msg=(f"""Enter the value of mean for class_{a+1}: """) ,   err_msg='''This is an invalid value.''' ,   )
      user_means.append(__UpperCAmelCase			)
    print('''-''' * 100			)

    print('''Standard deviation: ''' ,   __UpperCAmelCase			)
    # print out the number of instances in classes in separated line
    for i, count in enumerate(__UpperCAmelCase ,   1			):
      print(f"""Number of instances in class_{i} is: {count}"""			)
    print('''-''' * 100			)

    # print out mean values of classes separated line
    for i, user_mean in enumerate(__UpperCAmelCase ,   1			):
      print(f"""Mean of class_{i} is: {user_mean}"""			)
    print('''-''' * 100			)

    # Generating training dataset drawn from gaussian distribution
    lowercase				:							Union[str, Any]									=   [
        gaussian_distribution(user_means[j] ,   __UpperCAmelCase ,   counts[j]			)
        for j in range(__UpperCAmelCase			)
    ]
    print('''Generated Normal Distribution: \n''' ,   __UpperCAmelCase			)
    print('''-''' * 100			)

    # Generating Ys to detecting corresponding classes
    lowercase				:							List[str]									=   y_generator(__UpperCAmelCase ,   __UpperCAmelCase			)
    print('''Generated Corresponding Ys: \n''' ,   __UpperCAmelCase			)
    print('''-''' * 100			)

    # Calculating the value of actual mean for each class
    lowercase				:							int									=   [calculate_mean(counts[k] ,   x[k]			) for k in range(__UpperCAmelCase			)]
    # for loop iterates over number of elements in 'actual_means' list and print
    # out them in separated line
    for i, actual_mean in enumerate(__UpperCAmelCase ,   1			):
      print(f"""Actual(Real) mean of class_{i} is: {actual_mean}"""			)
    print('''-''' * 100			)

    # Calculating the value of probabilities for each class
    lowercase				:							Optional[int]									=   [
        calculate_probabilities(counts[i] ,   sum(__UpperCAmelCase			)			) for i in range(__UpperCAmelCase			)
    ]

    # for loop iterates over number of elements in 'probabilities' list and print
    # out them in separated line
    for i, probability in enumerate(__UpperCAmelCase ,   1			):
      print(f"""Probability of class_{i} is: {probability}"""			)
    print('''-''' * 100			)

    # Calculating the values of variance for each class
    lowercase				:							Tuple									=   calculate_variance(__UpperCAmelCase ,   __UpperCAmelCase ,   sum(__UpperCAmelCase			)			)
    print('''Variance: ''' ,   __UpperCAmelCase			)
    print('''-''' * 100			)

    # Predicting Y values
    # storing predicted Y values in 'pre_indexes' variable
    lowercase				:							Any									=   predict_y_values(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
    print('''-''' * 100			)

    # Calculating Accuracy of the model
    print(f"""Accuracy: {accuracy(__UpperCAmelCase ,   __UpperCAmelCase			)}"""			)
    print('''-''' * 100			)
    print(''' DONE '''.center(100 ,   '''+'''			)			)

    if input('''Press any key to restart or \'q\' for quit: '''			).strip().lower() == "q":
      print('''\n''' + '''GoodBye!'''.center(100 ,   '''-'''			) + '''\n'''			)
      break
    system('''cls''' if name == '''nt''' else '''clear'''			)  # noqa: S605


if __name__ == "__main__":
 main()