



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       =random.Random()

def 			a		( _UpperCAmelCase				:   Union[str, Any]					,  _UpperCAmelCase				:   Any=1.0					,  _UpperCAmelCase				:   Tuple=None					,  _UpperCAmelCase				:   int=None		):







		'''simple docstring'''





		if rng is None:
				__UpperCAmelCase       :	str						=      global_rng

		__UpperCAmelCase       :	List[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 						UpperCAmelCase__     (     unittest.TestCase  ):




			'''simple docstring'''






			def __init__(  self				:					int			,			a_				:					List[Any]			,			a_				:					int=7			,			a_				:					Any=4_00			,			a_				:					int=20_00			,			a_				:					Union[str, Any]=1			,			a_				:					Union[str, Any]=0.0			,			a_				:					List[str]=2_40_00			,			a_				:					List[Any]=True			,			):
					'''simple docstring'''





					__UpperCAmelCase       :	Union[str, Any]						=      parent
					__UpperCAmelCase       :	str						=      batch_size
					__UpperCAmelCase       :	List[str]						=      min_seq_length
					__UpperCAmelCase       :	Any						=      max_seq_length
					__UpperCAmelCase       :	Union[str, Any]						=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					__UpperCAmelCase       :	Optional[int]						=      feature_size
					__UpperCAmelCase       :	str						=      padding_value
					__UpperCAmelCase       :	str						=      sampling_rate
					__UpperCAmelCase       :	str						=      return_attention_mask




			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					return {
					    "feature_size": self.feature_size,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					}




			def 					snake_case__	(  self				:					Optional[Any]			,			a_				:					Tuple=False			,			a_				:					Optional[int]=False					):
					'''simple docstring'''





					def _flatten(a_				:					int					):
							return list(itertools.chain(*a_					)					)

					if equal_length:
							__UpperCAmelCase       :	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       :	List[str]						=      [np.asarray(a_					) for x in audio_inputs]

					return audio_inputs

@require_torch
class 						UpperCAmelCase__     (     __UpperCamelCase		,unittest.TestCase  ):




			'''simple docstring'''






			UpperCamelCase     		=			EncodecFeatureExtractor




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Dict						=      EnCodecFeatureExtractionTester(self					)




			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Any						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
					# create three inputs of length 800, 1000, and 1200
					__UpperCAmelCase       :	List[Any]						=      [floats_list((1, x)					)[0] for x in range(8_00			,			14_00			,			2_00					)]
					__UpperCAmelCase       :	str						=      [np.asarray(a_					) for audio_input in audio_inputs]

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

					# Test batched
					__UpperCAmelCase       :	int						=      feat_extract(a_			,			padding=a_			,			return_tensors='''np'''					).input_values
					__UpperCAmelCase       :	Union[str, Any]						=      feat_extract(a_			,			padding=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 					snake_case__	(  self				:					Dict					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[str]						=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
					__UpperCAmelCase       :	List[str]						=      np.random.rand(1_00					).astype(np.floataa					)
					__UpperCAmelCase       :	Optional[int]						=      np_audio_inputs.tolist()

					for inputs in [py_audio_inputs, np_audio_inputs]:
							__UpperCAmelCase       :	List[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					)




			def 					snake_case__	(  self				:					Any			,			a_				:					Tuple					):
					'''simple docstring'''





					from datasets import load_dataset

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

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




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Union[str, Any]						=      torch.tensor(
					    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
					     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
					     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
					     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
					     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
					     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]					)
					# fmt: on
					__UpperCAmelCase       :	Tuple						=      self._load_datasamples(1					)
					__UpperCAmelCase       :	Optional[int]						=      EncodecFeatureExtractor()
					__UpperCAmelCase       :	Union[str, Any]						=      feature_extractor(a_			,			return_tensors='''pt'''					).input_values
					self.assertEquals(input_values.shape			,			(1, 1, 9_36_80)					)
					self.assertTrue(torch.allclose(input_values[0, 0, :30]			,			a_			,			atol=1e-6					)					)




			def 					snake_case__	(  self				:					Any					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      torch.tensor(
					    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
					     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
					     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
					     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
					     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
					     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]					)
					# fmt: on
					__UpperCAmelCase       :	Dict						=      self._load_datasamples(1					)
					__UpperCAmelCase       :	Any						=      [np.tile(input_audio[0][None]			,			reps=(2, 1)					)]
					input_audio[0][1] *= 0.5
					__UpperCAmelCase       :	str						=      EncodecFeatureExtractor(feature_size=2					)
					__UpperCAmelCase       :	str						=      feature_extractor(a_			,			return_tensors='''pt'''					).input_values
					self.assertEquals(input_values.shape			,			(1, 2, 9_36_80)					)
					self.assertTrue(torch.allclose(input_values[0, 0, :30]			,			a_			,			atol=1e-6					)					)
					self.assertTrue(torch.allclose(input_values[0, 1, :30]			,			EXPECTED_INPUT_VALUES * 0.5			,			atol=1e-6					)					)




			def 					snake_case__	(  self				:					Tuple					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      self._load_datasamples(2					)
					# would be easier if the stride was like
					__UpperCAmelCase       :	Optional[Any]						=      EncodecFeatureExtractor(feature_size=1			,			chunk_length_s=1			,			overlap=0.0_1					)

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

					# truncate to chunk
					__UpperCAmelCase       :	int						=      feature_extractor(a_			,			truncation=a_			,			return_tensors='''pt'''					).input_values
					self.assertEquals(truncated_outputs.shape			,			(2, 1, 7_15_20)					)  # 2 chunks

					# force truncate to max_length
					__UpperCAmelCase       :	List[str]						=      feature_extractor(
					    a_			,			truncation=a_			,			max_length=4_80_00			,			return_tensors='''pt'''					).input_values
					self.assertEquals(truncated_outputs.shape			,			(2, 1, 4_80_00)					)

					# pad to chunk
					__UpperCAmelCase       :	Optional[Any]						=      feature_extractor(a_			,			padding=a_			,			return_tensors='''pt'''					).input_values
					self.assertEquals(padded_outputs.shape			,			(2, 1, 9_52_80)					)

					# pad to chunk
					__UpperCAmelCase       :	Dict						=      feature_extractor(a_			,			return_tensors='''pt'''					).input_values
					self.assertEquals(truncated_outputs.shape			,			(2, 1, 9_52_80)					)

					# force pad to max length
					__UpperCAmelCase       :	Tuple						=      feature_extractor(
					    a_			,			padding='''max_length'''			,			max_length=10_00_00			,			return_tensors='''pt'''					).input_values
					self.assertEquals(truncated_outputs.shape			,			(2, 1, 10_00_00)					)

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

					__UpperCAmelCase       :	int						=      feature_extractor(input_audio[0]			,			padding=a_			,			return_tensors='''pt'''					).input_values
					self.assertEquals(truncated_outputs.shape			,			(1, 1, 9_36_80)					)

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

					__UpperCAmelCase       :	str						=      feature_extractor(input_audio[0]			,			padding=a_			,			return_tensors='''pt'''					).input_values
					self.assertEquals(truncated_outputs.shape			,			(1, 1, 9_36_80)					)

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

					__UpperCAmelCase       :	Union[str, Any]						=      feature_extractor(input_audio[0]			,			padding=a_			,			return_tensors='''pt'''					).input_values
					self.assertEquals(truncated_outputs.shape			,			(1, 1, 9_36_80)					)










import numpy as np

def 			a		( _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   List[Any]="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	Union[str, Any]						=      bin(int.from_bytes(text.encode(_UpperCAmelCase					,  _UpperCAmelCase		)					,  '''big'''		)		)[2:]
		return bits.zfill(8 * ((len(_UpperCAmelCase		) + 7) // 8)		)

def 			a		( _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   str="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[str]						=      int(_UpperCAmelCase					,  2		)
		return n.to_bytes((n.bit_length() + 7) // 8					,  '''big'''		).decode(_UpperCAmelCase					,  _UpperCAmelCase		) or "\0"

def 			a		( _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   List[str]		):







		'''simple docstring'''





		if size_par + len(_UpperCAmelCase		) <= 2**size_par - (len(_UpperCAmelCase		) - 1):
				raise ValueError('''size of parity don\'t match with size of data'''		)

		__UpperCAmelCase       :	str						=      []
		__UpperCAmelCase       :	Any						=      []
		__UpperCAmelCase       :	Any						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		# sorted information data for the size of the output data
		__UpperCAmelCase       :	Optional[Any]						=      []
		# data position template + parity
		__UpperCAmelCase       :	Any						=      []
		# parity bit counter
		__UpperCAmelCase       :	Tuple						=      0
		# counter position of data bits
		__UpperCAmelCase       :	Optional[int]						=      0

		for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
								data_out_gab.append('''P'''		)
								__UpperCAmelCase       :	Union[str, Any]						=      qtd_bp + 1
						else:
								data_out_gab.append('''D'''		)
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data[cont_data]		)
						cont_data += 1
				else:
						data_ord.append(_UpperCAmelCase		)

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a given parity
				__UpperCAmelCase       :	List[str]						=      0
				# counter to control the loop reading
				__UpperCAmelCase       :	str						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	Tuple						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	List[str]						=      '''0'''
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(cont_bo % 2		)

				qtd_bp += 1

		# Mount the message
		__UpperCAmelCase       :	int						=      0  # parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]		)		)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]		)

		return data_out

def 			a		( _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		__UpperCAmelCase       :	str						=      []
		# Parity bit counter
		__UpperCAmelCase       :	List[str]						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	int						=      0
		# list of parity received
		__UpperCAmelCase       :	int						=      []
		__UpperCAmelCase       :	List[Any]						=      []

		for x in range(1					,  len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
						data_out_gab.append('''P'''		)
						__UpperCAmelCase       :	Union[str, Any]						=      qtd_bp + 1
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_output.append(data[cont_data]		)
				else:
						parity_received.append(data[cont_data]		)
				cont_data += 1

		# -----------calculates the parity with the data
		__UpperCAmelCase       :	Optional[int]						=      []
		__UpperCAmelCase       :	Dict						=      []
		__UpperCAmelCase       :	Union[str, Any]						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		#  sorted information data for the size of the output data
		__UpperCAmelCase       :	str						=      []
		# Data position feedback + parity
		__UpperCAmelCase       :	Any						=      []
		# Parity bit counter
		__UpperCAmelCase       :	int						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	List[Any]						=      0

		for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		):
				# Performs a template position of bits - who should be given,
				# and who should be parity
				if qtd_bp < size_par and (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
						data_out_gab.append('''P'''		)
						__UpperCAmelCase       :	Dict						=      qtd_bp + 1
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data_output[cont_data]		)
						cont_data += 1
				else:
						data_ord.append(_UpperCAmelCase		)

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a certain parity
				__UpperCAmelCase       :	Union[str, Any]						=      0
				# Counter to control loop reading
				__UpperCAmelCase       :	Optional[Any]						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	int						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	int						=      '''0'''
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(str(cont_bo % 2		)		)

				qtd_bp += 1

		# Mount the message
		__UpperCAmelCase       :	Any						=      0  # Parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]		)		)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]		)

		__UpperCAmelCase       :	Optional[Any]						=      parity_received == parity
		return data_output, ack





