
"""simple docstring"""







import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
		from transformers import WhisperFeatureExtractor

if is_torch_available():
		import torch

_SCREAMING_SNAKE_CASE	       =      random.Random()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=1.0							,    SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE=None )  ->     Union[str, Any]:







							"""simple docstring"""


							if rng is None:
														__snake_case     			=       global_rng

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

							return values







@require_torch
@require_torchaudio
class 							__magic_name__     (		unittest.TestCase   ):





					def __init__(				self   :							int							,						snake_case_   :							str							,						snake_case_   :							Optional[Any]=7							,						snake_case_   :							List[str]=400							,						snake_case_   :							Optional[int]=2000							,						snake_case_   :							List[Any]=10							,						snake_case_   :							Dict=160							,						snake_case_   :							Any=8							,						snake_case_   :							Union[str, Any]=0.0							,						snake_case_   :							List[str]=4000							,						snake_case_   :							Tuple=False							,						snake_case_   :							List[str]=True							,						):
												__snake_case     			=       parent
												__snake_case     			=       batch_size
												__snake_case     			=       min_seq_length
												__snake_case     			=       max_seq_length
												__snake_case     			=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												__snake_case     			=       padding_value
												__snake_case     			=       sampling_rate
												__snake_case     			=       return_attention_mask
												__snake_case     			=       do_normalize
												__snake_case     			=       feature_size
												__snake_case     			=       chunk_length
												__snake_case     			=       hop_length





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												return {
												    "feature_size": self.feature_size,
												    "hop_length": self.hop_length,
												    "chunk_length": self.chunk_length,
												    "padding_value": self.padding_value,
												    "sampling_rate": self.sampling_rate,
												    "return_attention_mask": self.return_attention_mask,
												    "do_normalize": self.do_normalize,
												}





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]=False							,						snake_case_   :							Optional[Any]=False    ):
												def _flatten(snake_case_   :							Union[str, Any]    ):
																			return list(itertools.chain(*snake_case_    )    )

												if equal_length:
																			__snake_case     			=       [floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
												else:
																			# make sure that inputs increase in size
																			__snake_case     			=       [
																			    floats_list((x, self.feature_size)    )
																			    for x in range(self.min_seq_length							,						self.max_seq_length							,						self.seq_length_diff    )
																			]
												if numpify:
																			__snake_case     			=       [np.asarray(snake_case_    ) for x in speech_inputs]
												return speech_inputs







@require_torch
@require_torchaudio
class 							__magic_name__     (		lowercase__      ,		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   WhisperFeatureExtractor if is_speech_available() else None





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       WhisperFeatureExtractionTester(self    )





					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       self.feature_extraction_class(**self.feat_extract_dict    )

												with tempfile.TemporaryDirectory() as tmpdirname:
																			__snake_case     			=       feat_extract_first.save_pretrained(snake_case_    )[0]
																			check_json_file_has_correct_format(snake_case_    )
																			__snake_case     			=       self.feature_extraction_class.from_pretrained(snake_case_    )

												__snake_case     			=       feat_extract_first.to_dict()
												__snake_case     			=       feat_extract_second.to_dict()
												__snake_case     			=       feat_extract_first.mel_filters
												__snake_case     			=       feat_extract_second.mel_filters
												self.assertTrue(np.allclose(snake_case_							,						snake_case_    )    )
												self.assertEqual(snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       self.feature_extraction_class(**self.feat_extract_dict    )

												with tempfile.TemporaryDirectory() as tmpdirname:
																			__snake_case     			=       os.path.join(snake_case_							,						"feat_extract.json"    )
																			feat_extract_first.to_json_file(snake_case_    )
																			__snake_case     			=       self.feature_extraction_class.from_json_file(snake_case_    )

												__snake_case     			=       feat_extract_first.to_dict()
												__snake_case     			=       feat_extract_second.to_dict()
												__snake_case     			=       feat_extract_first.mel_filters
												__snake_case     			=       feat_extract_second.mel_filters
												self.assertTrue(np.allclose(snake_case_							,						snake_case_    )    )
												self.assertEqual(snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict    ):
												# Tests that all call wrap to encode_plus and batch_encode_plus
												__snake_case     			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
												# create three inputs of length 800, 1000, and 1200
												__snake_case     			=       [floats_list((1, x)    )[0] for x in range(800							,						1400							,						200    )]
												__snake_case     			=       [np.asarray(snake_case_    ) for speech_input in speech_inputs]

												# Test feature size
												__snake_case     			=       feature_extractor(snake_case_							,						padding="max_length"							,						return_tensors="np"    ).input_features
												self.assertTrue(input_features.ndim == 3    )
												self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames    )
												self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size    )

												# Test not batched input
												__snake_case     			=       feature_extractor(speech_inputs[0]							,						return_tensors="np"    ).input_features
												__snake_case     			=       feature_extractor(np_speech_inputs[0]							,						return_tensors="np"    ).input_features
												self.assertTrue(np.allclose(snake_case_							,						snake_case_							,						atol=1e-3    )    )

												# Test batched
												__snake_case     			=       feature_extractor(snake_case_							,						return_tensors="np"    ).input_features
												__snake_case     			=       feature_extractor(snake_case_							,						return_tensors="np"    ).input_features
												for enc_seq_a, enc_seq_a in zip(snake_case_							,						snake_case_    ):
																			self.assertTrue(np.allclose(snake_case_							,						snake_case_							,						atol=1e-3    )    )

												# Test 2-D numpy arrays are batched.
												__snake_case     			=       [floats_list((1, x)    )[0] for x in (800, 800, 800)]
												__snake_case     			=       np.asarray(snake_case_    )
												__snake_case     			=       feature_extractor(snake_case_							,						return_tensors="np"    ).input_features
												__snake_case     			=       feature_extractor(snake_case_							,						return_tensors="np"    ).input_features
												for enc_seq_a, enc_seq_a in zip(snake_case_							,						snake_case_    ):
																			self.assertTrue(np.allclose(snake_case_							,						snake_case_							,						atol=1e-3    )    )

												# Test truncation required
												__snake_case     			=       [floats_list((1, x)    )[0] for x in range(200							,						(feature_extractor.n_samples + 500)							,						200    )]
												__snake_case     			=       [np.asarray(snake_case_    ) for speech_input in speech_inputs]

												__snake_case     			=       [x[: feature_extractor.n_samples] for x in speech_inputs]
												__snake_case     			=       [np.asarray(snake_case_    ) for speech_input in speech_inputs_truncated]

												__snake_case     			=       feature_extractor(snake_case_							,						return_tensors="np"    ).input_features
												__snake_case     			=       feature_extractor(snake_case_							,						return_tensors="np"    ).input_features
												for enc_seq_a, enc_seq_a in zip(snake_case_							,						snake_case_    ):
																			self.assertTrue(np.allclose(snake_case_							,						snake_case_							,						atol=1e-3    )    )





					def 							lowerCAmelCase      (				self   :							List[str]    ):
												import torch

												__snake_case     			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
												__snake_case     			=       np.random.rand(100							,						32    ).astype(np.floataa    )
												__snake_case     			=       np_speech_inputs.tolist()

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





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							int    ):
												__snake_case     			=       load_dataset("hf-internal-testing/librispeech_asr_dummy"							,						"clean"							,						split="validation"    )
												# automatic decoding with librispeech
												__snake_case     			=       ds.sort("id"    ).select(range(snake_case_    )    )[:num_samples]["audio"]

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





					def 							lowerCAmelCase      (				self   :							Any    ):
												# fmt: off
												__snake_case     			=       torch.tensor(
												    [
												        0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
												        0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
												        0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
												        -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
												    ]    )
												# fmt: on

												__snake_case     			=       self._load_datasamples(1    )
												__snake_case     			=       WhisperFeatureExtractor()
												__snake_case     			=       feature_extractor(snake_case_							,						return_tensors="pt"    ).input_features
												self.assertEqual(input_features.shape							,						(1, 80, 3000)    )
												self.assertTrue(torch.allclose(input_features[0, 0, :30]							,						snake_case_							,						atol=1e-4    )    )





					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
												__snake_case     			=       self._load_datasamples(1    )[0]
												__snake_case     			=       ((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
												__snake_case     			=       feat_extract.zero_mean_unit_var_norm([audio]							,						attention_mask=snake_case_    )[0]

												self.assertTrue(np.all(np.mean(snake_case_    ) < 1e-3    )    )
												self.assertTrue(np.all(np.abs(np.var(snake_case_    ) - 1    ) < 1e-3    )    )


"""simple docstring"""







import baseaa
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     bytes:







							"""simple docstring"""


							return baseaa.baaencode(string.encode("utf-8" ) )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							return baseaa.baadecode(SCREAMING_SNAKE_CASE ).decode("utf-8" )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      """Hello World!"""
		_SCREAMING_SNAKE_CASE	       =      baseaa_encode(test)
		print(encoded)

		_SCREAMING_SNAKE_CASE	       =      baseaa_decode(encoded)
		print(decoded)
