
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

lowercase   =	random.Random()



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Tuple=1.0      ,					UpperCAmelCase__					:   List[str]=None      ,					UpperCAmelCase__					:   Optional[int]=None							)	->     Optional[Any]:
							if rng is None:
														lowerCamelCase_					=       global_rng

							lowerCamelCase_					=       []
							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    __A(					unittest.TestCase							):

					def __init__(						self       :  Optional[int] ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  List[Any]=7 ,       __UpperCamelCase       :  List[str]=4_0_0 ,       __UpperCamelCase       :  List[str]=2_0_0_0 ,       __UpperCamelCase       :  Optional[int]=2_4 ,       __UpperCamelCase       :  Any=2_4 ,       __UpperCamelCase       :  List[Any]=0.0 ,       __UpperCamelCase       :  List[Any]=1_6_0_0_0 ,       __UpperCamelCase       :  Tuple=True ,       __UpperCamelCase       :  Dict=True ,       ):
												lowerCamelCase_					=       parent
												lowerCamelCase_					=       batch_size
												lowerCamelCase_					=       min_seq_length
												lowerCamelCase_					=       max_seq_length
												lowerCamelCase_					=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												lowerCamelCase_					=       feature_size
												lowerCamelCase_					=       num_mel_bins
												lowerCamelCase_					=       padding_value
												lowerCamelCase_					=       sampling_rate
												lowerCamelCase_					=       return_attention_mask
												lowerCamelCase_					=       do_normalize

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

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





@require_torch
@require_torchaudio
class    __A(					UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     SpeechaTextFeatureExtractor if is_speech_available() else None

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       SpeechaTextFeatureExtractionTester(self )

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  Union[str, Any] ):
												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  lowercase__   (						self       :  Optional[Any] ):
												# Tests that all call wrap to encode_plus and batch_encode_plus
												lowerCamelCase_					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
												# create three inputs of length 800, 1000, and 1200
												lowerCamelCase_					=       [floats_list((1, x) )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0 )]
												lowerCamelCase_					=       [np.asarray(__UpperCamelCase ) for speech_input in speech_inputs]

												# Test feature size
												lowerCamelCase_					=       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
												lowerCamelCase_					=       feature_extractor(speech_inputs[0] ,       return_tensors="""np""" ).input_features
												lowerCamelCase_					=       feature_extractor(np_speech_inputs[0] ,       return_tensors="""np""" ).input_features
												self.assertTrue(np.allclose(__UpperCamelCase ,       __UpperCamelCase ,       atol=1E-3 ) )

												# Test batched
												lowerCamelCase_					=       feature_extractor(__UpperCamelCase ,       return_tensors="""np""" ).input_features
												lowerCamelCase_					=       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.
												lowerCamelCase_					=       [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
												lowerCamelCase_					=       np.asarray(__UpperCamelCase )
												lowerCamelCase_					=       feature_extractor(__UpperCamelCase ,       return_tensors="""np""" ).input_features
												lowerCamelCase_					=       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  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
												lowerCamelCase_					=       [floats_list((1, x) )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0 )]

												lowerCamelCase_					=       ["""longest""", """max_length""", """do_not_pad"""]
												lowerCamelCase_					=       [None, 1_6, None]
												for max_length, padding in zip(__UpperCamelCase ,       __UpperCamelCase ):
																			lowerCamelCase_					=       feature_extractor(
																			    __UpperCamelCase ,       padding=__UpperCamelCase ,       max_length=__UpperCamelCase ,       return_attention_mask=__UpperCamelCase )
																			lowerCamelCase_					=       inputs.input_features
																			lowerCamelCase_					=       inputs.attention_mask
																			lowerCamelCase_					=       [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  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
												lowerCamelCase_					=       [floats_list((1, x) )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0 )]

												lowerCamelCase_					=       ["""longest""", """max_length""", """do_not_pad"""]
												lowerCamelCase_					=       [None, 1_6, None]
												for max_length, padding in zip(__UpperCamelCase ,       __UpperCamelCase ):
																			lowerCamelCase_					=       feature_extractor(
																			    __UpperCamelCase ,       max_length=__UpperCamelCase ,       padding=__UpperCamelCase ,       return_tensors="""np""" ,       return_attention_mask=__UpperCamelCase )
																			lowerCamelCase_					=       inputs.input_features
																			lowerCamelCase_					=       inputs.attention_mask
																			lowerCamelCase_					=       [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  lowercase__   (						self       :  Any ):
												lowerCamelCase_					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
												lowerCamelCase_					=       [floats_list((1, x) )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0 )]
												lowerCamelCase_					=       feature_extractor(
												    __UpperCamelCase ,       padding="""max_length""" ,       max_length=4 ,       truncation=__UpperCamelCase ,       return_tensors="""np""" ,       return_attention_mask=__UpperCamelCase ,       )
												lowerCamelCase_					=       inputs.input_features
												lowerCamelCase_					=       inputs.attention_mask
												lowerCamelCase_					=       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  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
												lowerCamelCase_					=       [floats_list((1, x) )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0 )]
												lowerCamelCase_					=       feature_extractor(
												    __UpperCamelCase ,       padding="""longest""" ,       max_length=4 ,       truncation=__UpperCamelCase ,       return_tensors="""np""" ,       return_attention_mask=__UpperCamelCase ,       )
												lowerCamelCase_					=       inputs.input_features
												lowerCamelCase_					=       inputs.attention_mask
												lowerCamelCase_					=       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, 2_4) )

												lowerCamelCase_					=       [floats_list((1, x) )[0] for x in range(8_0_0 ,       1_4_0_0 ,       2_0_0 )]
												lowerCamelCase_					=       feature_extractor(
												    __UpperCamelCase ,       padding="""longest""" ,       max_length=1_6 ,       truncation=__UpperCamelCase ,       return_tensors="""np""" ,       return_attention_mask=__UpperCamelCase ,       )
												lowerCamelCase_					=       inputs.input_features
												lowerCamelCase_					=       inputs.attention_mask
												lowerCamelCase_					=       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, 2_4) )

					def  lowercase__   (						self       :  Optional[int] ):
												import torch

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

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

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Optional[int] ):
												from datasets import load_dataset

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

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



					def  lowercase__   (						self       :  Any ):
												# fmt: off
												lowerCamelCase_					=       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

												lowerCamelCase_					=       self._load_datasamples(1 )
												lowerCamelCase_					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
												lowerCamelCase_					=       feature_extractor(__UpperCamelCase ,       return_tensors="""pt""" ).input_features
												self.assertEquals(input_features.shape ,       (1, 5_8_4, 2_4) )
												self.assertTrue(np.allclose(input_features[0, 0, :3_0] ,       __UpperCamelCase ,       atol=1E-4 ) )







lowercase   =	8.314_4598



def   __lowerCAmelCase    (				UpperCAmelCase__					:   float      ,					UpperCAmelCase__					:   float							)	->     float:
							if temperature < 0:
														raise Exception("""Temperature cannot be less than 0 K"""							)
							if molar_mass <= 0:
														raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""							)
							else:
														return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		lowercase   =	3_0_0
		lowercase   =	2_8
		lowercase   =	rms_speed_of_molecule(temperature, molar_mass)
		print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")





