

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

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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=1.0				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None    ):
						if rng is None:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       global_rng

						SCREAMING_SNAKE_CASE_: 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
@require_torchaudio
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def __init__( self  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     str=7   ,						lowerCAmelCase__  :     Optional[int]=400   ,						lowerCAmelCase__  :     Union[str, Any]=2000   ,						lowerCAmelCase__  :     List[str]=24   ,						lowerCAmelCase__  :     Any=24   ,						lowerCAmelCase__  :     Union[str, Any]=0.0   ,						lowerCAmelCase__  :     List[str]=1_6000   ,						lowerCAmelCase__  :     Optional[Any]=True   ,						lowerCAmelCase__  :     Optional[Any]=True   ,						):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       parent
									SCREAMING_SNAKE_CASE_: Dict  		=       batch_size
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       min_seq_length
									SCREAMING_SNAKE_CASE_: List[str]  		=       max_seq_length
									SCREAMING_SNAKE_CASE_: str  		=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       feature_size
									SCREAMING_SNAKE_CASE_: Tuple  		=       num_mel_bins
									SCREAMING_SNAKE_CASE_: Dict  		=       padding_value
									SCREAMING_SNAKE_CASE_: List[str]  		=       sampling_rate
									SCREAMING_SNAKE_CASE_: Dict  		=       return_attention_mask
									SCREAMING_SNAKE_CASE_: List[Any]  		=       do_normalize



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									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        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Union[str, Any]=False   ,						lowerCAmelCase__  :     str=False):
									def _flatten(lowerCAmelCase__  :     Optional[int]):
															return list(itertools.chain(*lowerCAmelCase__))

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







@require_torch
@require_torchaudio
class   __lowercase				( UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							int										=     SpeechaTextFeatureExtractor if is_speech_available() else None



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       SpeechaTextFeatureExtractionTester(self)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     str):
									self.assertTrue(np.all(np.mean(lowerCAmelCase__   ,						axis=0) < 1E-3))
									self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase__   ,						axis=0) - 1) < 1E-3))



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

									# Test feature size
									SCREAMING_SNAKE_CASE_: Dict  		=       feature_extractor(lowerCAmelCase__   ,						padding=lowerCAmelCase__   ,						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
									SCREAMING_SNAKE_CASE_: int  		=       feature_extractor(speech_inputs[0]   ,						return_tensors="np").input_features
									SCREAMING_SNAKE_CASE_: int  		=       feature_extractor(np_speech_inputs[0]   ,						return_tensors="np").input_features
									self.assertTrue(np.allclose(lowerCAmelCase__   ,						lowerCAmelCase__   ,						atol=1E-3))

									# Test batched
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       feature_extractor(lowerCAmelCase__   ,						return_tensors="np").input_features
									SCREAMING_SNAKE_CASE_: str  		=       feature_extractor(lowerCAmelCase__   ,						return_tensors="np").input_features
									for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															self.assertTrue(np.allclose(lowerCAmelCase__   ,						lowerCAmelCase__   ,						atol=1E-3))

									# Test 2-D numpy arrays are batched.
									SCREAMING_SNAKE_CASE_: Any  		=       [floats_list((1, x))[0] for x in (800, 800, 800)]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       np.asarray(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       feature_extractor(lowerCAmelCase__   ,						return_tensors="np").input_features
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       feature_extractor(lowerCAmelCase__   ,						return_tensors="np").input_features
									for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															self.assertTrue(np.allclose(lowerCAmelCase__   ,						lowerCAmelCase__   ,						atol=1E-3))



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: int  		=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									SCREAMING_SNAKE_CASE_: int  		=       [floats_list((1, x))[0] for x in range(800   ,						1400   ,						200)]

									SCREAMING_SNAKE_CASE_: Any  		=       ["longest", "max_length", "do_not_pad"]
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [None, 16, None]
									for max_length, padding in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Any  		=       feature_extractor(
															    lowerCAmelCase__   ,						padding=lowerCAmelCase__   ,						max_length=lowerCAmelCase__   ,						return_attention_mask=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       inputs.input_features
															SCREAMING_SNAKE_CASE_: Tuple  		=       inputs.attention_mask
															SCREAMING_SNAKE_CASE_: int  		=       [np.sum(lowerCAmelCase__) 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        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									SCREAMING_SNAKE_CASE_: List[str]  		=       [floats_list((1, x))[0] for x in range(800   ,						1400   ,						200)]

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ["longest", "max_length", "do_not_pad"]
									SCREAMING_SNAKE_CASE_: str  		=       [None, 16, None]
									for max_length, padding in zip(lowerCAmelCase__   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: List[str]  		=       feature_extractor(
															    lowerCAmelCase__   ,						max_length=lowerCAmelCase__   ,						padding=lowerCAmelCase__   ,						return_tensors="np"   ,						return_attention_mask=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       inputs.input_features
															SCREAMING_SNAKE_CASE_: Tuple  		=       inputs.attention_mask
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       [np.sum(lowerCAmelCase__) 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        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       [floats_list((1, x))[0] for x in range(800   ,						1400   ,						200)]
									SCREAMING_SNAKE_CASE_: Any  		=       feature_extractor(
									    lowerCAmelCase__   ,						padding="max_length"   ,						max_length=4   ,						truncation=lowerCAmelCase__   ,						return_tensors="np"   ,						return_attention_mask=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       inputs.input_features
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       inputs.attention_mask
									SCREAMING_SNAKE_CASE_: 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])
									self._check_zero_mean_unit_variance(input_features[2])



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: str  		=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       [floats_list((1, x))[0] for x in range(800   ,						1400   ,						200)]
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       feature_extractor(
									    lowerCAmelCase__   ,						padding="longest"   ,						max_length=4   ,						truncation=lowerCAmelCase__   ,						return_tensors="np"   ,						return_attention_mask=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Dict  		=       inputs.input_features
									SCREAMING_SNAKE_CASE_: Any  		=       inputs.attention_mask
									SCREAMING_SNAKE_CASE_: 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))

									SCREAMING_SNAKE_CASE_: Any  		=       [floats_list((1, x))[0] for x in range(800   ,						1400   ,						200)]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       feature_extractor(
									    lowerCAmelCase__   ,						padding="longest"   ,						max_length=16   ,						truncation=lowerCAmelCase__   ,						return_tensors="np"   ,						return_attention_mask=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: int  		=       inputs.input_features
									SCREAMING_SNAKE_CASE_: int  		=       inputs.attention_mask
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									import torch

									SCREAMING_SNAKE_CASE_: Dict  		=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									SCREAMING_SNAKE_CASE_: List[Any]  		=       np.random.rand(100   ,						32).astype(np.floataa)
									SCREAMING_SNAKE_CASE_: int  		=       np_speech_inputs.tolist()

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



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     Any):
									from datasets import load_dataset

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

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




			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									# fmt: off
									SCREAMING_SNAKE_CASE_: List[str]  		=       np.array([
									    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
									    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
									    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
									])
									# fmt: on

									SCREAMING_SNAKE_CASE_: Dict  		=       self._load_datasamples(1)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									SCREAMING_SNAKE_CASE_: Any  		=       feature_extractor(lowerCAmelCase__   ,						return_tensors="pt").input_features
									self.assertEquals(input_features.shape   ,						(1, 584, 24))
									self.assertTrue(np.allclose(input_features[0, 0, :30]   ,						lowerCAmelCase__   ,						atol=1E-4))









def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = False    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: str  		=       f"Expected string as input, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )
						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       f"Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Tuple  		=       input_str.split("_"    )

						SCREAMING_SNAKE_CASE_: str  		=       0 if use_pascal else 1

						SCREAMING_SNAKE_CASE_: int  		=       words[start_index:]

						SCREAMING_SNAKE_CASE_: List[str]  		=       [word[0].upper() + word[1:] for word in words_to_capitalize]

						SCREAMING_SNAKE_CASE_: List[Any]  		=       "" if use_pascal else words[0]

						return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
						from doctest import testmod

						testmod()






