'''simple docstring'''
import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


_A		:      int						   =random.Random()







def 				__UpperCamelCase (					__UpperCAmelCase  :					List[Any] ,   __UpperCAmelCase  :					List[str]=1.0 ,   __UpperCAmelCase  :					str=None ,   __UpperCAmelCase  :					int=None			)							->	Optional[Any]:
  if rng is None:
    lowercase				:							Optional[int]									=   global_rng

  lowercase				:							str									=   []
  for batch_idx in range(shape[0]			):
    values.append([]			)
    for _ in range(shape[1]			):
      values[-1].append(rng.random() * scale			)

  return values




@require_torch
class _lowerCAmelCase			(	unittest.TestCase					):







      def __init__(  self  ,    lowercase  ,    lowercase=7  ,    lowercase=400  ,    lowercase=2000  ,    lowercase=1  ,    lowercase=0.0  ,    lowercase=2_4000  ,    lowercase=True  ,    ):
        lowercase				:							int									=   parent
        lowercase				:							int									=   batch_size
        lowercase				:							List[Any]									=   min_seq_length
        lowercase				:							Dict									=   max_seq_length
        lowercase				:							List[str]									=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowercase				:							List[str]									=   feature_size
        lowercase				:							List[str]									=   padding_value
        lowercase				:							Tuple									=   sampling_rate
        lowercase				:							List[Any]									=   return_attention_mask







      def      lowerCamelCase__      (  self     ):
        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
        }







      def      lowerCamelCase__      (  self  ,    lowercase=False  ,    lowercase=False     ):
        def _flatten(lowercase     ):
          return list(itertools.chain(*lowercase     )     )

        if equal_length:
          lowercase				:							Tuple									=   floats_list((self.batch_size, self.max_seq_length)     )
        else:
          # make sure that inputs increase in size
          lowercase				:							int									=   [
              _flatten(floats_list((x, self.feature_size)     )     )
              for x in range(self.min_seq_length  ,    self.max_seq_length  ,    self.seq_length_diff     )
          ]

        if numpify:
          lowercase				:							Any									=   [np.asarray(lowercase     ) for x in audio_inputs]

        return audio_inputs





@require_torch
class _lowerCAmelCase			(	snake_case							,   unittest.TestCase					):
      UpperCAmelCase_	:						Optional[int]	 =       EncodecFeatureExtractor







      def      lowerCamelCase__      (  self     ):
        lowercase				:							List[Any]									=   EnCodecFeatureExtractionTester(self     )







      def      lowerCamelCase__      (  self     ):
        # Tests that all call wrap to encode_plus and batch_encode_plus
        lowercase				:							Optional[int]									=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        # create three inputs of length 800, 1000, and 1200
        lowercase				:							List[str]									=   [floats_list((1, x)     )[0] for x in range(800  ,    1400  ,    200     )]
        lowercase				:							Optional[Any]									=   [np.asarray(lowercase     ) for audio_input in audio_inputs]

        # Test not batched input
        lowercase				:							Optional[Any]									=   feat_extract(audio_inputs[0]  ,    return_tensors='''np'''     ).input_values
        lowercase				:							Tuple									=   feat_extract(np_audio_inputs[0]  ,    return_tensors='''np'''     ).input_values
        self.assertTrue(np.allclose(lowercase  ,    lowercase  ,    atol=1e-3     )     )

        # Test batched
        lowercase				:							Dict									=   feat_extract(lowercase  ,    padding=lowercase  ,    return_tensors='''np'''     ).input_values
        lowercase				:							Union[str, Any]									=   feat_extract(lowercase  ,    padding=lowercase  ,    return_tensors='''np'''     ).input_values
        for enc_seq_a, enc_seq_a in zip(lowercase  ,    lowercase     ):
          self.assertTrue(np.allclose(lowercase  ,    lowercase  ,    atol=1e-3     )     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Any									=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        lowercase				:							Any									=   np.random.rand(100     ).astype(np.floataa     )
        lowercase				:							Any									=   np_audio_inputs.tolist()

        for inputs in [py_audio_inputs, np_audio_inputs]:
          lowercase				:							int									=   feature_extractor.pad([{'''input_values''': inputs}]  ,    return_tensors='''np'''     )
          self.assertTrue(np_processed.input_values.dtype == np.floataa     )
          lowercase				:							str									=   feature_extractor.pad([{'''input_values''': inputs}]  ,    return_tensors='''pt'''     )
          self.assertTrue(pt_processed.input_values.dtype == torch.floataa     )







      def      lowerCamelCase__      (  self  ,    lowercase     ):
        from datasets import load_dataset

        lowercase				:							Any									=   load_dataset('''hf-internal-testing/librispeech_asr_dummy'''  ,    '''clean'''  ,    split='''validation'''     )
        # automatic decoding with librispeech
        lowercase				:							Tuple									=   ds.sort('''id'''     ).select(range(lowercase     )     )[:num_samples]['''audio''']

        return [x["array"] for x in audio_samples]







      def      lowerCamelCase__      (  self     ):
        # fmt: off
        lowercase				:							str									=   torch.tensor(
            [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
             3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
             2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
             4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
             7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
             4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03]     )
        # fmt: on
        lowercase				:							List[str]									=   self._load_datasamples(1     )
        lowercase				:							Any									=   EncodecFeatureExtractor()
        lowercase				:							Union[str, Any]									=   feature_extractor(lowercase  ,    return_tensors='''pt'''     ).input_values
        self.assertEquals(input_values.shape  ,    (1, 1, 9_3680)     )
        self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,    lowercase  ,    atol=1e-6     )     )







      def      lowerCamelCase__      (  self     ):
        # fmt: off
        lowercase				:							str									=   torch.tensor(
            [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
             3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
             2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
             4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
             7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
             4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03]     )
        # fmt: on
        lowercase				:							Optional[Any]									=   self._load_datasamples(1     )
        lowercase				:							str									=   [np.tile(input_audio[0][None]  ,    reps=(2, 1)     )]
        input_audio[0][1] *= 0.5
        lowercase				:							List[Any]									=   EncodecFeatureExtractor(feature_size=2     )
        lowercase				:							Union[str, Any]									=   feature_extractor(lowercase  ,    return_tensors='''pt'''     ).input_values
        self.assertEquals(input_values.shape  ,    (1, 2, 9_3680)     )
        self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,    lowercase  ,    atol=1e-6     )     )
        self.assertTrue(torch.allclose(input_values[0, 1, :30]  ,    EXPECTED_INPUT_VALUES * 0.5  ,    atol=1e-6     )     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Any									=   self._load_datasamples(2     )
        # would be easier if the stride was like
        lowercase				:							Any									=   EncodecFeatureExtractor(feature_size=1  ,    chunk_length_s=1  ,    overlap=0.01     )

        # pad and trunc raise an error ?
        with self.assertRaisesRegex(
            lowercase  ,    '''^Both padding and truncation were set. Make sure you only set one.$'''  ,    ):
          lowercase				:							str									=   feature_extractor(
              lowercase  ,    padding='''max_length'''  ,    truncation=lowercase  ,    return_tensors='''pt'''     ).input_values

        # truncate to chunk
        lowercase				:							List[str]									=   feature_extractor(lowercase  ,    truncation=lowercase  ,    return_tensors='''pt'''     ).input_values
        self.assertEquals(truncated_outputs.shape  ,    (2, 1, 7_1520)     )  # 2 chunks

        # force truncate to max_length
        lowercase				:							Dict									=   feature_extractor(
            lowercase  ,    truncation=lowercase  ,    max_length=4_8000  ,    return_tensors='''pt'''     ).input_values
        self.assertEquals(truncated_outputs.shape  ,    (2, 1, 4_8000)     )

        # pad to chunk
        lowercase				:							Union[str, Any]									=   feature_extractor(lowercase  ,    padding=lowercase  ,    return_tensors='''pt'''     ).input_values
        self.assertEquals(padded_outputs.shape  ,    (2, 1, 9_5280)     )

        # pad to chunk
        lowercase				:							Optional[Any]									=   feature_extractor(lowercase  ,    return_tensors='''pt'''     ).input_values
        self.assertEquals(truncated_outputs.shape  ,    (2, 1, 9_5280)     )

        # force pad to max length
        lowercase				:							Tuple									=   feature_extractor(
            lowercase  ,    padding='''max_length'''  ,    max_length=10_0000  ,    return_tensors='''pt'''     ).input_values
        self.assertEquals(truncated_outputs.shape  ,    (2, 1, 10_0000)     )

        # force no pad
        with self.assertRaisesRegex(
            lowercase  ,    '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''  ,    ):
          lowercase				:							Optional[int]									=   feature_extractor(lowercase  ,    padding=lowercase  ,    return_tensors='''pt'''     ).input_values

        lowercase				:							Union[str, Any]									=   feature_extractor(input_audio[0]  ,    padding=lowercase  ,    return_tensors='''pt'''     ).input_values
        self.assertEquals(truncated_outputs.shape  ,    (1, 1, 9_3680)     )

        # no pad if no chunk_length_s
        lowercase				:							int									=   None
        with self.assertRaisesRegex(
            lowercase  ,    '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''  ,    ):
          lowercase				:							Optional[Any]									=   feature_extractor(lowercase  ,    padding=lowercase  ,    return_tensors='''pt'''     ).input_values

        lowercase				:							int									=   feature_extractor(input_audio[0]  ,    padding=lowercase  ,    return_tensors='''pt'''     ).input_values
        self.assertEquals(truncated_outputs.shape  ,    (1, 1, 9_3680)     )

        # no pad if no overlap
        lowercase				:							List[str]									=   2
        lowercase				:							Any									=   None
        with self.assertRaisesRegex(
            lowercase  ,    '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''  ,    ):
          lowercase				:							int									=   feature_extractor(lowercase  ,    padding=lowercase  ,    return_tensors='''pt'''     ).input_values

        lowercase				:							Union[str, Any]									=   feature_extractor(input_audio[0]  ,    padding=lowercase  ,    return_tensors='''pt'''     ).input_values
        self.assertEquals(truncated_outputs.shape  ,    (1, 1, 9_3680)     )
'''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()