






"""simple docstring"""







import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_lowerCAmelCase            =	random.Random()


def      UpperCamelCase       ( _A  ,				_A=1.0  ,				_A=None  ,				_A=None )     ->       Optional[int]:
				if rng is None:
								lowercase    :      Any       = global_rng

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

				return values


class      UpperCamelCase		(unittest.TestCase     ):






						def __init__(		self  :Dict			, __magic_name__  :Tuple			, __magic_name__  :int=7			, __magic_name__  :Dict=400			, __magic_name__  :Tuple=2_000			, __magic_name__  :Any=1			, __magic_name__  :Tuple=0.0			, __magic_name__  :Dict=16_000			, __magic_name__  :Dict=True			, __magic_name__  :List[str]=True			, )      ->List[str]:
										lowercase    :      List[str]       = parent
										lowercase    :      Optional[Any]       = batch_size
										lowercase    :      Optional[Any]       = min_seq_length
										lowercase    :      Tuple       = max_seq_length
										lowercase    :      int       = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										lowercase    :      str       = feature_size
										lowercase    :      List[str]       = padding_value
										lowercase    :      Optional[Any]       = sampling_rate
										lowercase    :      int       = return_attention_mask
										lowercase    :      Optional[Any]       = do_normalize






						def      __snake_case      (		self  :Tuple				)      ->int:
										return {
										    "feature_size": self.feature_size,
										    "padding_value": self.padding_value,
										    "sampling_rate": self.sampling_rate,
										    "return_attention_mask": self.return_attention_mask,
										    "do_normalize": self.do_normalize,
										}






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :Tuple=False			, __magic_name__  :Union[str, Any]=False				)      ->str:
										def _flatten(__magic_name__  :int				):
														return list(itertools.chain(*__magic_name__				)				)

										if equal_length:
														lowercase    :      Optional[Any]       = floats_list((self.batch_size, self.max_seq_length)				)
										else:
														# make sure that inputs increase in size
														lowercase    :      Any       = [
														    _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    :      List[Any]       = [np.asarray(__magic_name__				) for x in speech_inputs]

										return speech_inputs


class      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		WavaVecaFeatureExtractor






						def      __snake_case      (		self  :int				)      ->Tuple:
										lowercase    :      Optional[int]       = WavaVecaFeatureExtractionTester(self				)






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :Any				)      ->List[Any]:
										self.assertTrue(np.all(np.mean(__magic_name__			, axis=0				) < 1E-3				)				)
										self.assertTrue(np.all(np.abs(np.var(__magic_name__			, axis=0				) - 1				) < 1E-3				)				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->Tuple:
										# Tests that all call wrap to encode_plus and batch_encode_plus
										lowercase    :      Optional[Any]       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
										# create three inputs of length 800, 1000, and 1200
										lowercase    :      Tuple       = [floats_list((1, x)				)[0] for x in range(800			, 1_400			, 200				)]
										lowercase    :      Union[str, Any]       = [np.asarray(__magic_name__				) for speech_input in speech_inputs]

										# Test not batched input
										lowercase    :      int       = feat_extract(speech_inputs[0]			, return_tensors="""np"""				).input_values
										lowercase    :      Optional[int]       = feat_extract(np_speech_inputs[0]			, return_tensors="""np"""				).input_values
										self.assertTrue(np.allclose(__magic_name__			, __magic_name__			, atol=1E-3				)				)

										# Test batched
										lowercase    :      Union[str, Any]       = feat_extract(__magic_name__			, return_tensors="""np"""				).input_values
										lowercase    :      Tuple       = feat_extract(__magic_name__			, return_tensors="""np"""				).input_values
										for enc_seq_a, enc_seq_a in zip(__magic_name__			, __magic_name__				):
														self.assertTrue(np.allclose(__magic_name__			, __magic_name__			, atol=1E-3				)				)

										# Test 2-D numpy arrays are batched.
										lowercase    :      Optional[Any]       = [floats_list((1, x)				)[0] for x in (800, 800, 800)]
										lowercase    :      List[str]       = np.asarray(__magic_name__				)
										lowercase    :      int       = feat_extract(__magic_name__			, return_tensors="""np"""				).input_values
										lowercase    :      Optional[int]       = feat_extract(__magic_name__			, return_tensors="""np"""				).input_values
										for enc_seq_a, enc_seq_a in zip(__magic_name__			, __magic_name__				):
														self.assertTrue(np.allclose(__magic_name__			, __magic_name__			, atol=1E-3				)				)






						def      __snake_case      (		self  :str				)      ->Union[str, Any]:
										lowercase    :      Any       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
										lowercase    :      Union[str, Any]       = [floats_list((1, x)				)[0] for x in range(800			, 1_400			, 200				)]

										lowercase    :      Tuple       = ["""longest""", """max_length""", """do_not_pad"""]
										lowercase    :      Dict       = [None, 1_600, None]
										for max_length, padding in zip(__magic_name__			, __magic_name__				):
														lowercase    :      Optional[int]       = feat_extract(__magic_name__			, padding=__magic_name__			, max_length=__magic_name__			, return_tensors="""np"""				)
														lowercase    :      Optional[Any]       = processed.input_values

														self._check_zero_mean_unit_variance(input_values[0][:800]				)
														self.assertTrue(input_values[0][800:].sum() < 1E-6				)
														self._check_zero_mean_unit_variance(input_values[1][:1_000]				)
														self.assertTrue(input_values[0][1_000:].sum() < 1E-6				)
														self._check_zero_mean_unit_variance(input_values[2][:1_200]				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[Any]:
										lowercase    :      str       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
										lowercase    :      str       = range(800			, 1_400			, 200				)
										lowercase    :      Any       = [floats_list((1, x)				)[0] for x in lengths]

										lowercase    :      int       = ["""longest""", """max_length""", """do_not_pad"""]
										lowercase    :      Union[str, Any]       = [None, 1_600, None]

										for max_length, padding in zip(__magic_name__			, __magic_name__				):
														lowercase    :      Dict       = feat_extract(__magic_name__			, max_length=__magic_name__			, padding=__magic_name__				)
														lowercase    :      Union[str, Any]       = processed.input_values

														self._check_zero_mean_unit_variance(input_values[0][:800]				)
														self._check_zero_mean_unit_variance(input_values[1][:1_000]				)
														self._check_zero_mean_unit_variance(input_values[2][:1_200]				)






						def      __snake_case      (		self  :Dict				)      ->str:
										lowercase    :      Optional[Any]       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
										lowercase    :      Any       = [floats_list((1, x)				)[0] for x in range(800			, 1_400			, 200				)]
										lowercase    :      Any       = feat_extract(
										    __magic_name__			, truncation=__magic_name__			, max_length=1_000			, padding="""max_length"""			, return_tensors="""np"""				)
										lowercase    :      Union[str, Any]       = processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :800]				)
										self._check_zero_mean_unit_variance(input_values[1]				)
										self._check_zero_mean_unit_variance(input_values[2]				)






						def      __snake_case      (		self  :Any				)      ->Optional[Any]:
										lowercase    :      str       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
										lowercase    :      Optional[int]       = [floats_list((1, x)				)[0] for x in range(800			, 1_400			, 200				)]
										lowercase    :      Union[str, Any]       = feat_extract(
										    __magic_name__			, truncation=__magic_name__			, max_length=1_000			, padding="""longest"""			, return_tensors="""np"""				)
										lowercase    :      Optional[Any]       = processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :800]				)
										self._check_zero_mean_unit_variance(input_values[1, :1_000]				)
										self._check_zero_mean_unit_variance(input_values[2]				)

										# make sure that if max_length < longest -> then pad to max_length
										self.assertTrue(input_values.shape == (3, 1_000)				)

										lowercase    :      Dict       = [floats_list((1, x)				)[0] for x in range(800			, 1_400			, 200				)]
										lowercase    :      Optional[Any]       = feat_extract(
										    __magic_name__			, truncation=__magic_name__			, max_length=2_000			, padding="""longest"""			, return_tensors="""np"""				)
										lowercase    :      int       = processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :800]				)
										self._check_zero_mean_unit_variance(input_values[1, :1_000]				)
										self._check_zero_mean_unit_variance(input_values[2]				)

										# make sure that if max_length > longest -> then pad to longest
										self.assertTrue(input_values.shape == (3, 1_200)				)






						@require_torch
						def      __snake_case      (		self  :int				)      ->List[str]:
										import torch

										lowercase    :      List[str]       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
										lowercase    :      List[str]       = np.random.rand(100				).astype(np.floataa				)
										lowercase    :      Optional[int]       = np_speech_inputs.tolist()

										for inputs in [py_speech_inputs, np_speech_inputs]:
														lowercase    :      Dict       = feature_extractor.pad([{"""input_values""": inputs}]			, return_tensors="""np"""				)
														self.assertTrue(np_processed.input_values.dtype == np.floataa				)
														lowercase    :      Optional[int]       = feature_extractor.pad([{"""input_values""": inputs}]			, return_tensors="""pt"""				)
														self.assertTrue(pt_processed.input_values.dtype == torch.floataa				)






						@slow
						@require_torch
						def      __snake_case      (		self  :Optional[Any]				)      ->Optional[Any]:
										# this test makes sure that models that are using
										# group norm don't have their feature extractor return the
										# attention_mask
										for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
														lowercase    :      Union[str, Any]       = WavaVecaConfig.from_pretrained(__magic_name__				)
														lowercase    :      int       = WavaVecaFeatureExtractor.from_pretrained(__magic_name__				)

														# only "layer" feature extraction norm should make use of
														# attention_mask
														self.assertEqual(feat_extract.return_attention_mask			, config.feat_extract_norm == """layer"""				)








"""simple docstring"""







import math
import random


def      UpperCamelCase       ( _A  ,				_A = False )     ->       float:
				if deriv:
								return value * (1 - value)
				return 1 / (1 + math.exp(-value ))


# Initial Value
_lowerCAmelCase            =	0.02


def      UpperCamelCase       ( _A  ,				_A )     ->       float:
				lowercase    :      Any       = float(2 * (random.randint(1  ,				100 )) - 1 )

				for _ in range(_A ):
								# Forward propagation
								lowercase    :      Dict       = sigmoid_function(INITIAL_VALUE * weight )
								# How much did we miss?
								lowercase    :      Any       = (expected / 100) - layer_a
								# Error delta
								lowercase    :      Dict       = layer_1_error * sigmoid_function(_A  ,				_A )
								# Update weight
								weight += INITIAL_VALUE * layer_1_delta

				return layer_a * 100


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	int(input('Expected value: '))
				_lowerCAmelCase            =	int(input('Number of propagations: '))
				print(forward_propagation(expected, number_propagations))
