





'''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


a     :    str      						=					random.Random()



def    __lowerCamelCase						(      _lowercase			,       _lowercase=1.0			,       _lowercase=None			,       _lowercase=None	)   ->    Optional[Any]:
							if rng is None:
														UpperCAmelCase  :	List[str]							  =	global_rng

							UpperCAmelCase  :	Union[str, 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	,	A	,	A=7	,	A=400	,	A=2000	,	A=1	,	A=0.0	,	A=16000	,	A=True	,	A=True	,	)    ->    Optional[int]:
								UpperCAmelCase  :	Dict							  =	parent
								UpperCAmelCase  :	Dict							  =	batch_size
								UpperCAmelCase  :	str							  =	min_seq_length
								UpperCAmelCase  :	Optional[int]							  =	max_seq_length
								UpperCAmelCase  :	List[str]							  =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								UpperCAmelCase  :	List[Any]							  =	feature_size
								UpperCAmelCase  :	List[str]							  =	padding_value
								UpperCAmelCase  :	Any							  =	sampling_rate
								UpperCAmelCase  :	List[Any]							  =	return_attention_mask
								UpperCAmelCase  :	Union[str, Any]							  =	do_normalize





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								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 				_lowercase(						self	,	A=False	,	A=False					)    ->    Optional[Any]:
								def _flatten(A					):
															return list(itertools.chain(*A					)					)

								if equal_length:
															UpperCAmelCase  :	List[Any]							  =	floats_list((self.batch_size, self.max_seq_length)					)
								else:
															# make sure that inputs increase in size
															UpperCAmelCase  :	Union[str, 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:
															UpperCAmelCase  :	Optional[int]							  =	[np.asarray(A					) for x in speech_inputs]

								return speech_inputs




class     UpperCamelCase_				(							__magic_name__   ,  unittest.TestCase    ):
	lowercase    =				WavaVecaFeatureExtractor





	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	str							  =	WavaVecaFeatureExtractionTester(self					)





	def 				_lowercase(						self	,	A					)    ->    int:
								self.assertTrue(np.all(np.mean(A	,	axis=0					) < 1e-3					)					)
								self.assertTrue(np.all(np.abs(np.var(A	,	axis=0					) - 1					) < 1e-3					)					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								UpperCAmelCase  :	List[str]							  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
								# create three inputs of length 800, 1000, and 1200
								UpperCAmelCase  :	Any							  =	[floats_list((1, x)					)[0] for x in range(800	,	1400	,	200					)]
								UpperCAmelCase  :	Any							  =	[np.asarray(A					) for speech_input in speech_inputs]

								# Test not batched input
								UpperCAmelCase  :	Tuple							  =	feat_extract(speech_inputs[0]	,	return_tensors="""np"""					).input_values
								UpperCAmelCase  :	str							  =	feat_extract(np_speech_inputs[0]	,	return_tensors="""np"""					).input_values
								self.assertTrue(np.allclose(A	,	A	,	atol=1e-3					)					)

								# Test batched
								UpperCAmelCase  :	Optional[int]							  =	feat_extract(A	,	return_tensors="""np"""					).input_values
								UpperCAmelCase  :	List[Any]							  =	feat_extract(A	,	return_tensors="""np"""					).input_values
								for enc_seq_a, enc_seq_a in zip(A	,	A					):
															self.assertTrue(np.allclose(A	,	A	,	atol=1e-3					)					)

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





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Any							  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
								UpperCAmelCase  :	Union[str, Any]							  =	[floats_list((1, x)					)[0] for x in range(800	,	1400	,	200					)]

								UpperCAmelCase  :	Any							  =	["""longest""", """max_length""", """do_not_pad"""]
								UpperCAmelCase  :	List[Any]							  =	[None, 1600, None]
								for max_length, padding in zip(A	,	A					):
															UpperCAmelCase  :	Dict							  =	feat_extract(A	,	padding=A	,	max_length=A	,	return_tensors="""np"""					)
															UpperCAmelCase  :	List[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][:1000]					)
															self.assertTrue(input_values[0][1000:].sum() < 1e-6					)
															self._check_zero_mean_unit_variance(input_values[2][:1200]					)





	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Tuple							  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
								UpperCAmelCase  :	str							  =	range(800	,	1400	,	200					)
								UpperCAmelCase  :	Optional[int]							  =	[floats_list((1, x)					)[0] for x in lengths]

								UpperCAmelCase  :	Optional[int]							  =	["""longest""", """max_length""", """do_not_pad"""]
								UpperCAmelCase  :	Dict							  =	[None, 1600, None]

								for max_length, padding in zip(A	,	A					):
															UpperCAmelCase  :	List[Any]							  =	feat_extract(A	,	max_length=A	,	padding=A					)
															UpperCAmelCase  :	Optional[Any]							  =	processed.input_values

															self._check_zero_mean_unit_variance(input_values[0][:800]					)
															self._check_zero_mean_unit_variance(input_values[1][:1000]					)
															self._check_zero_mean_unit_variance(input_values[2][:1200]					)





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	List[Any]							  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
								UpperCAmelCase  :	Union[str, Any]							  =	[floats_list((1, x)					)[0] for x in range(800	,	1400	,	200					)]
								UpperCAmelCase  :	Dict							  =	feat_extract(
								    A	,	truncation=A	,	max_length=1000	,	padding="""max_length"""	,	return_tensors="""np"""					)
								UpperCAmelCase  :	int							  =	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 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Optional[int]							  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
								UpperCAmelCase  :	List[str]							  =	[floats_list((1, x)					)[0] for x in range(800	,	1400	,	200					)]
								UpperCAmelCase  :	Union[str, Any]							  =	feat_extract(
								    A	,	truncation=A	,	max_length=1000	,	padding="""longest"""	,	return_tensors="""np"""					)
								UpperCAmelCase  :	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, :1000]					)
								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, 1000)					)

								UpperCAmelCase  :	List[Any]							  =	[floats_list((1, x)					)[0] for x in range(800	,	1400	,	200					)]
								UpperCAmelCase  :	List[str]							  =	feat_extract(
								    A	,	truncation=A	,	max_length=2000	,	padding="""longest"""	,	return_tensors="""np"""					)
								UpperCAmelCase  :	Optional[int]							  =	processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]					)
								self._check_zero_mean_unit_variance(input_values[1, :1000]					)
								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, 1200)					)





	@require_torch
	def 				_lowercase(						self					)    ->    Tuple:
								import torch

								UpperCAmelCase  :	str							  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
								UpperCAmelCase  :	Tuple							  =	np.random.rand(100					).astype(np.floataa					)
								UpperCAmelCase  :	str							  =	np_speech_inputs.tolist()

								for inputs in [py_speech_inputs, np_speech_inputs]:
															UpperCAmelCase  :	Union[str, Any]							  =	feature_extractor.pad([{"""input_values""": inputs}]	,	return_tensors="""np"""					)
															self.assertTrue(np_processed.input_values.dtype == np.floataa					)
															UpperCAmelCase  :	Tuple							  =	feature_extractor.pad([{"""input_values""": inputs}]	,	return_tensors="""pt"""					)
															self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)






	@slow
	@require_torch
	def 				_lowercase(						self					)    ->    Dict:
								# 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:
															UpperCAmelCase  :	Optional[Any]							  =	WavaVecaConfig.from_pretrained(A					)
															UpperCAmelCase  :	Optional[Any]							  =	WavaVecaFeatureExtractor.from_pretrained(A					)

															# 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'''




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str | Literal[False]:
							UpperCAmelCase  :	Optional[int]							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	list(_lowercase	)
							UpperCAmelCase  :	str							  =	0
							for i in range(len(_lowercase	)	):
														if lista[i] != lista[i]:
																					count += 1
																					UpperCAmelCase  :	Optional[Any]							  =	"""_"""
							if count > 1:
														return False
							else:
														return "".join(_lowercase	)



def    __lowerCamelCase						(      _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	List[str]							  =	[]
							while True:
														UpperCAmelCase  :	Optional[int]							  =	["""$"""] * len(_lowercase	)
														UpperCAmelCase  :	int							  =	[]
														for i in range(len(_lowercase	)	):
																					for j in range(i + 1			,       len(_lowercase	)	):
																												UpperCAmelCase  :	str							  =	compare_string(binary[i]			,       binary[j]	)
																												if k is False:
																																			UpperCAmelCase  :	Union[str, Any]							  =	"""*"""
																																			UpperCAmelCase  :	Optional[Any]							  =	"""*"""
																																			temp.append("""X"""	)
														for i in range(len(_lowercase	)	):
																					if checka[i] == "$":
																												pi.append(binary[i]	)
														if len(_lowercase	) == 0:
																					return pi
														UpperCAmelCase  :	List[Any]							  =	list(set(_lowercase	)	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	Dict							  =	[]
							for minterm in minterms:
														UpperCAmelCase  :	List[str]							  =	""""""
														for _ in range(_lowercase	):
																					UpperCAmelCase  :	Dict							  =	str(minterm % 2	) + string
																					minterm //= 2
														temp.append(_lowercase	)
							return temp



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    bool:
							UpperCAmelCase  :	Optional[int]							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	list(_lowercase	)
							UpperCAmelCase  :	Dict							  =	0
							for i in range(len(_lowercase	)	):
														if lista[i] != lista[i]:
																					count_n += 1
							return count_n == count



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[str]:
							UpperCAmelCase  :	Tuple							  =	[]
							UpperCAmelCase  :	Optional[int]							  =	[0] * len(_lowercase	)
							for i in range(len(chart[0]	)	):
														UpperCAmelCase  :	Any							  =	0
														UpperCAmelCase  :	Optional[Any]							  =	-1
														for j in range(len(_lowercase	)	):
																					if chart[j][i] == 1:
																												count += 1
																												UpperCAmelCase  :	str							  =	j
														if count == 1:
																					UpperCAmelCase  :	Optional[int]							  =	1
							for i in range(len(_lowercase	)	):
														if select[i] == 1:
																					for j in range(len(chart[0]	)	):
																												if chart[i][j] == 1:
																																			for k in range(len(_lowercase	)	):
																																										UpperCAmelCase  :	List[str]							  =	0
																					temp.append(prime_implicants[i]	)
							while True:
														UpperCAmelCase  :	int							  =	0
														UpperCAmelCase  :	Tuple							  =	-1
														UpperCAmelCase  :	Union[str, Any]							  =	0
														for i in range(len(_lowercase	)	):
																					UpperCAmelCase  :	Optional[Any]							  =	chart[i].count(1	)
																					if count_n > max_n:
																												UpperCAmelCase  :	Union[str, Any]							  =	count_n
																												UpperCAmelCase  :	Optional[Any]							  =	i

														if max_n == 0:
																					return temp

														temp.append(prime_implicants[rem]	)

														for i in range(len(chart[0]	)	):
																					if chart[rem][i] == 1:
																												for j in range(len(_lowercase	)	):
																																			UpperCAmelCase  :	Optional[Any]							  =	0



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    list[list[int]]:
							UpperCAmelCase  :	Optional[int]							  =	[[0 for x in range(len(_lowercase	)	)] for x in range(len(_lowercase	)	)]
							for i in range(len(_lowercase	)	):
														UpperCAmelCase  :	Tuple							  =	prime_implicants[i].count("""_"""	)
														for j in range(len(_lowercase	)	):
																					if is_for_table(prime_implicants[i]			,       binary[j]			,       _lowercase	):
																												UpperCAmelCase  :	List[Any]							  =	1

							return chart



def    __lowerCamelCase						(      )   ->    None:
							UpperCAmelCase  :	str							  =	int(input("""Enter the no. of variables\n"""	)	)
							UpperCAmelCase  :	List[Any]							  =	[
							    float(_lowercase	)
							    for x in input(
							        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""	).split()
							]
							UpperCAmelCase  :	str							  =	decimal_to_binary(_lowercase			,       _lowercase	)

							UpperCAmelCase  :	Tuple							  =	check(_lowercase	)
							print("""Prime Implicants are:"""	)
							print(_lowercase	)
							UpperCAmelCase  :	Union[str, Any]							  =	prime_implicant_chart(_lowercase			,       _lowercase	)

							UpperCAmelCase  :	Tuple							  =	selection(_lowercase			,       _lowercase	)
							print("""Essential Prime Implicants are:"""	)
							print(_lowercase	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

