


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

a			:			int												=					random.Random()


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=1.0							,      _UpperCamelCase=None							,      _UpperCamelCase=None	):


			'''simple docstring'''







			if rng is None:
						__lowercase			       = global_rng

			__lowercase			       = []
			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 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=7			,							snake_case_=4_0_0			,							snake_case_=2_0_0_0			,							snake_case_=1_0			,							snake_case_=1_6_0			,							snake_case_=8			,							snake_case_=0.0			,							snake_case_=4_0_0_0			,							snake_case_=False			,							snake_case_=True			,							) ->      Any:







								'''simple docstring'''

								__lowercase			       = parent
								__lowercase			       = batch_size
								__lowercase			       = min_seq_length
								__lowercase			       = max_seq_length
								__lowercase			       = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								__lowercase			       = padding_value
								__lowercase			       = sampling_rate
								__lowercase			       = return_attention_mask
								__lowercase			       = do_normalize
								__lowercase			       = feature_size
								__lowercase			       = chunk_length
								__lowercase			       = hop_length






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								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 A   (					self			,							snake_case_=False			,							snake_case_=False    ) ->      Dict:







								'''simple docstring'''

								def _flatten(snake_case_    ):
											return list(itertools.chain(*snake_case_    )    )

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




@require_torch
@require_torchaudio
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					WhisperFeatureExtractor if is_speech_available() else None






					def A   (					self    ) ->      int:







								'''simple docstring'''

								__lowercase			       = WhisperFeatureExtractionTester(self    )






					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = self.feature_extraction_class(**self.feat_extract_dict    )

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

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






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.feature_extraction_class(**self.feat_extract_dict    )

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

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






					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
								# create three inputs of length 800, 1000, and 1200
								__lowercase			       = [floats_list((1, x)    )[0] for x in range(8_0_0			,							1_4_0_0			,							2_0_0    )]
								__lowercase			       = [np.asarray(snake_case_    ) for speech_input in speech_inputs]

								# Test feature size
								__lowercase			       = 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
								__lowercase			       = feature_extractor(speech_inputs[0]			,							return_tensors='''np'''    ).input_features
								__lowercase			       = feature_extractor(np_speech_inputs[0]			,							return_tensors='''np'''    ).input_features
								self.assertTrue(np.allclose(snake_case_			,							snake_case_			,							atol=1e-3    )    )

								# Test batched
								__lowercase			       = feature_extractor(snake_case_			,							return_tensors='''np'''    ).input_features
								__lowercase			       = 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.
								__lowercase			       = [floats_list((1, x)    )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
								__lowercase			       = np.asarray(snake_case_    )
								__lowercase			       = feature_extractor(snake_case_			,							return_tensors='''np'''    ).input_features
								__lowercase			       = 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
								__lowercase			       = [floats_list((1, x)    )[0] for x in range(2_0_0			,							(feature_extractor.n_samples + 5_0_0)			,							2_0_0    )]
								__lowercase			       = [np.asarray(snake_case_    ) for speech_input in speech_inputs]

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

								__lowercase			       = feature_extractor(snake_case_			,							return_tensors='''np'''    ).input_features
								__lowercase			       = 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 A   (					self    ) ->      str:







								'''simple docstring'''

								import torch

								__lowercase			       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
								__lowercase			       = np.random.rand(1_0_0			,							3_2    ).astype(np.floataa    )
								__lowercase			       = np_speech_inputs.tolist()

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






					def A   (					self			,							snake_case_    ) ->      Dict:







								'''simple docstring'''

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

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






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = torch.tensor(
								    [
								        0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
								        0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
								        0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
								        -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
								    ]    )
								# fmt: on

								__lowercase			       = self._load_datasamples(1    )
								__lowercase			       = WhisperFeatureExtractor()
								__lowercase			       = feature_extractor(snake_case_			,							return_tensors='''pt'''    ).input_features
								self.assertEqual(input_features.shape			,							(1, 8_0, 3_0_0_0)    )
								self.assertTrue(torch.allclose(input_features[0, 0, :3_0]			,							snake_case_			,							atol=1e-4    )    )






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
								__lowercase			       = self._load_datasamples(1    )[0]
								__lowercase			       = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
								__lowercase			       = 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    )    )






import doctest
from collections import deque

import numpy as np
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self    ) ->      None:







								'''simple docstring'''

								__lowercase			       = [2, 1, 2, -1]
								__lowercase			       = [1, 2, 3, 4]






					def A   (					self    ) ->      list[float]:







								'''simple docstring'''

								__lowercase			       = len(self.first_signal    )
								__lowercase			       = len(self.second_signal    )

								__lowercase			       = max(snake_case_			,							snake_case_    )

								# create a zero matrix of max_length x max_length
								__lowercase			       = [[0] * max_length for i in range(snake_case_    )]

								# fills the smaller signal with zeros to make both signals of same length
								if length_first_signal < length_second_signal:
											self.first_signal += [0] * (max_length - length_first_signal)
								elif length_first_signal > length_second_signal:
											self.second_signal += [0] * (max_length - length_second_signal)
								for i in range(snake_case_    ):
											__lowercase			       = deque(self.second_signal    )
											rotated_signal.rotate(snake_case_    )
											for j, item in enumerate(snake_case_    ):
														matrix[i][j] += item

        # multiply the matrix with the first signal
								__lowercase			       = np.matmul(np.transpose(snake_case_    )			,							np.transpose(self.first_signal    )    )

								# rounding-off to two decimal places
								return [round(snake_case_			,							2    ) for i in final_signal]


if __name__ == "__main__":
	doctest.testmod()


