

"""simple docstring"""






import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
	from transformers import SpeechaTextFeatureExtractor

snake_case_ : List[str]   =							random.Random()



def 	lowercase_						(     _lowercase							:		Union[str, Any]				,   _lowercase							:		Tuple=1.0				,   _lowercase							:		List[str]=None				,   _lowercase							:		Optional[int]=None						):




					'''simple docstring'''






					if rng is None:
										UpperCAmelCase      : Tuple   =					global_rng

					UpperCAmelCase      : Optional[int]   =					[]
					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    snake_case__	(			unittest.TestCase  ):







			def __init__(      self      :			Optional[int]      ,	lowercase      :			List[str]      ,	lowercase      :			List[Any]=7      ,	lowercase      :			List[str]=4_00      ,	lowercase      :			List[str]=20_00      ,	lowercase      :			Optional[int]=24      ,	lowercase      :			Any=24      ,	lowercase      :			List[Any]=0.0      ,	lowercase      :			List[Any]=1_60_00      ,	lowercase      :			Tuple=True      ,	lowercase      :			Dict=True      ,	):





								'''simple docstring'''



								UpperCAmelCase      : Tuple   =					parent
								UpperCAmelCase      : Tuple   =					batch_size
								UpperCAmelCase      : Tuple   =					min_seq_length
								UpperCAmelCase      : List[Any]   =					max_seq_length
								UpperCAmelCase      : int   =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								UpperCAmelCase      : Tuple   =					feature_size
								UpperCAmelCase      : List[Any]   =					num_mel_bins
								UpperCAmelCase      : Optional[int]   =					padding_value
								UpperCAmelCase      : Optional[int]   =					sampling_rate
								UpperCAmelCase      : str   =					return_attention_mask
								UpperCAmelCase      : Any   =					do_normalize







			def       __lowerCAmelCase					(      self      :			Any	):





								'''simple docstring'''



								return {
								    "feature_size": self.feature_size,
								    "num_mel_bins": self.num_mel_bins,
								    "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]      ,	lowercase      :			Dict=False      ,	lowercase      :			Optional[Any]=False	):





								'''simple docstring'''



								def _flatten(lowercase      :			Optional[Any]	):
													return list(itertools.chain(*__UpperCamelCase	)	)

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







@require_torch
@require_torchaudio
class    snake_case__	(			lowerCAmelCase_       ,	unittest.TestCase  ):
			SCREAMING_SNAKE_CASE__  					=    SpeechaTextFeatureExtractor if is_speech_available() else None







			def       __lowerCAmelCase					(      self      :			List[str]	):





								'''simple docstring'''



								UpperCAmelCase      : Dict   =					SpeechaTextFeatureExtractionTester(self	)







			def       __lowerCAmelCase					(      self      :			List[str]      ,	lowercase      :			Union[str, Any]	):





								'''simple docstring'''



								self.assertTrue(np.all(np.mean(__UpperCamelCase      ,	axis=0	) < 1E-3	)	)
								self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase      ,	axis=0	) - 1	) < 1E-3	)	)







			def       __lowerCAmelCase					(      self      :			Optional[Any]	):





								'''simple docstring'''



								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      : int   =					[floats_list((1, x)	)[0] for x in range(8_00      ,	14_00      ,	2_00	)]
								UpperCAmelCase      : List[Any]   =					[np.asarray(__UpperCamelCase	) for speech_input in speech_inputs]

								# Test feature size
								UpperCAmelCase      : Dict   =					feature_extractor(__UpperCamelCase      ,	padding=__UpperCamelCase      ,	return_tensors="np"	).input_features
								self.assertTrue(input_features.ndim == 3	)
								self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size	)

								# Test not batched input
								UpperCAmelCase      : Any   =					feature_extractor(speech_inputs[0]      ,	return_tensors="np"	).input_features
								UpperCAmelCase      : Tuple   =					feature_extractor(np_speech_inputs[0]      ,	return_tensors="np"	).input_features
								self.assertTrue(np.allclose(__UpperCamelCase      ,	__UpperCamelCase      ,	atol=1E-3	)	)

								# Test batched
								UpperCAmelCase      : List[Any]   =					feature_extractor(__UpperCamelCase      ,	return_tensors="np"	).input_features
								UpperCAmelCase      : Optional[Any]   =					feature_extractor(__UpperCamelCase      ,	return_tensors="np"	).input_features
								for enc_seq_a, enc_seq_a in zip(__UpperCamelCase      ,	__UpperCamelCase	):
													self.assertTrue(np.allclose(__UpperCamelCase      ,	__UpperCamelCase      ,	atol=1E-3	)	)

								# Test 2-D numpy arrays are batched.
								UpperCAmelCase      : List[str]   =					[floats_list((1, x)	)[0] for x in (8_00, 8_00, 8_00)]
								UpperCAmelCase      : Union[str, Any]   =					np.asarray(__UpperCamelCase	)
								UpperCAmelCase      : Optional[int]   =					feature_extractor(__UpperCamelCase      ,	return_tensors="np"	).input_features
								UpperCAmelCase      : Tuple   =					feature_extractor(__UpperCamelCase      ,	return_tensors="np"	).input_features
								for enc_seq_a, enc_seq_a in zip(__UpperCamelCase      ,	__UpperCamelCase	):
													self.assertTrue(np.allclose(__UpperCamelCase      ,	__UpperCamelCase      ,	atol=1E-3	)	)







			def       __lowerCAmelCase					(      self      :			Optional[Any]	):





								'''simple docstring'''



								UpperCAmelCase      : str   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								UpperCAmelCase      : Optional[int]   =					[floats_list((1, x)	)[0] for x in range(8_00      ,	14_00      ,	2_00	)]

								UpperCAmelCase      : Dict   =					["longest", "max_length", "do_not_pad"]
								UpperCAmelCase      : Any   =					[None, 16, None]
								for max_length, padding in zip(__UpperCamelCase      ,	__UpperCamelCase	):
													UpperCAmelCase      : List[Any]   =					feature_extractor(
													    __UpperCamelCase      ,	padding=__UpperCamelCase      ,	max_length=__UpperCamelCase      ,	return_attention_mask=__UpperCamelCase	)
													UpperCAmelCase      : Dict   =					inputs.input_features
													UpperCAmelCase      : Any   =					inputs.attention_mask
													UpperCAmelCase      : int   =					[np.sum(__UpperCamelCase	) for x in attention_mask]

													self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
													self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
													self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)







			def       __lowerCAmelCase					(      self      :			Union[str, Any]	):





								'''simple docstring'''



								UpperCAmelCase      : Any   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								UpperCAmelCase      : Tuple   =					[floats_list((1, x)	)[0] for x in range(8_00      ,	14_00      ,	2_00	)]

								UpperCAmelCase      : Union[str, Any]   =					["longest", "max_length", "do_not_pad"]
								UpperCAmelCase      : Any   =					[None, 16, None]
								for max_length, padding in zip(__UpperCamelCase      ,	__UpperCamelCase	):
													UpperCAmelCase      : str   =					feature_extractor(
													    __UpperCamelCase      ,	max_length=__UpperCamelCase      ,	padding=__UpperCamelCase      ,	return_tensors="np"      ,	return_attention_mask=__UpperCamelCase	)
													UpperCAmelCase      : Optional[Any]   =					inputs.input_features
													UpperCAmelCase      : Union[str, Any]   =					inputs.attention_mask
													UpperCAmelCase      : Dict   =					[np.sum(__UpperCamelCase	) for x in attention_mask]

													self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
													self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6	)
													self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
													self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6	)
													self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)







			def       __lowerCAmelCase					(      self      :			Any	):





								'''simple docstring'''



								UpperCAmelCase      : List[str]   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								UpperCAmelCase      : int   =					[floats_list((1, x)	)[0] for x in range(8_00      ,	14_00      ,	2_00	)]
								UpperCAmelCase      : Optional[Any]   =					feature_extractor(
								    __UpperCamelCase      ,	padding="max_length"      ,	max_length=4      ,	truncation=__UpperCamelCase      ,	return_tensors="np"      ,	return_attention_mask=__UpperCamelCase      ,	)
								UpperCAmelCase      : Dict   =					inputs.input_features
								UpperCAmelCase      : Union[str, Any]   =					inputs.attention_mask
								UpperCAmelCase      : Dict   =					np.sum(attention_mask == 1      ,	axis=1	)

								self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
								self._check_zero_mean_unit_variance(input_features[1]	)
								self._check_zero_mean_unit_variance(input_features[2]	)







			def       __lowerCAmelCase					(      self      :			Tuple	):





								'''simple docstring'''



								UpperCAmelCase      : Optional[Any]   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								UpperCAmelCase      : List[Any]   =					[floats_list((1, x)	)[0] for x in range(8_00      ,	14_00      ,	2_00	)]
								UpperCAmelCase      : Union[str, Any]   =					feature_extractor(
								    __UpperCamelCase      ,	padding="longest"      ,	max_length=4      ,	truncation=__UpperCamelCase      ,	return_tensors="np"      ,	return_attention_mask=__UpperCamelCase      ,	)
								UpperCAmelCase      : int   =					inputs.input_features
								UpperCAmelCase      : Tuple   =					inputs.attention_mask
								UpperCAmelCase      : Optional[int]   =					np.sum(attention_mask == 1      ,	axis=1	)

								self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
								self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
								self._check_zero_mean_unit_variance(input_features[2]	)

								# make sure that if max_length < longest -> then pad to max_length
								self.assertEqual(input_features.shape      ,	(3, 4, 24)	)

								UpperCAmelCase      : Tuple   =					[floats_list((1, x)	)[0] for x in range(8_00      ,	14_00      ,	2_00	)]
								UpperCAmelCase      : Any   =					feature_extractor(
								    __UpperCamelCase      ,	padding="longest"      ,	max_length=16      ,	truncation=__UpperCamelCase      ,	return_tensors="np"      ,	return_attention_mask=__UpperCamelCase      ,	)
								UpperCAmelCase      : List[Any]   =					inputs.input_features
								UpperCAmelCase      : List[str]   =					inputs.attention_mask
								UpperCAmelCase      : Dict   =					np.sum(attention_mask == 1      ,	axis=1	)

								self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
								self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
								self._check_zero_mean_unit_variance(input_features[2]	)

								# make sure that if max_length < longest -> then pad to max_length
								self.assertEqual(input_features.shape      ,	(3, 6, 24)	)







			def       __lowerCAmelCase					(      self      :			Optional[int]	):





								'''simple docstring'''



								import torch

								UpperCAmelCase      : Tuple   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								UpperCAmelCase      : List[str]   =					np.random.rand(1_00      ,	32	).astype(np.floataa	)
								UpperCAmelCase      : str   =					np_speech_inputs.tolist()

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







			def       __lowerCAmelCase					(      self      :			Optional[int]      ,	lowercase      :			Optional[int]	):





								'''simple docstring'''



								from datasets import load_dataset

								UpperCAmelCase      : Any   =					load_dataset("hf-internal-testing/librispeech_asr_dummy"      ,	"clean"      ,	split="validation"	)
								# automatic decoding with librispeech
								UpperCAmelCase      : int   =					ds.sort("id"	).select(range(__UpperCamelCase	)	)[:num_samples]["audio"]

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







			def       __lowerCAmelCase					(      self      :			Any	):





								'''simple docstring'''



								UpperCAmelCase      : Union[str, Any]   =					np.array([
								    -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
								    -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
								    -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
								]	)
								# fmt: on

								UpperCAmelCase      : Union[str, Any]   =					self._load_datasamples(1	)
								UpperCAmelCase      : Dict   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								UpperCAmelCase      : int   =					feature_extractor(__UpperCamelCase      ,	return_tensors="pt"	).input_features
								self.assertEquals(input_features.shape      ,	(1, 5_84, 24)	)
								self.assertTrue(np.allclose(input_features[0, 0, :30]      ,	__UpperCamelCase      ,	atol=1E-4	)	)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowercase   =	{}


try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''MLukeTokenizer''']

if TYPE_CHECKING:
		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mluke import MLukeTokenizer


else:
		import sys

		lowercase   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





