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

__lowercase										= random.Random()



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=1.0  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE=None     ):
    '''simple docstring'''







    if rng is None:
        __UpperCamelCase					:List[Any]        =		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      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase=7			,			__lowercase=400			,			__lowercase=2_000			,			__lowercase=10			,			__lowercase=160			,			__lowercase=8			,			__lowercase=0.0			,			__lowercase=4_000			,			__lowercase=False			,			__lowercase=True			,			)	->     List[str]:
        __UpperCamelCase					:Optional[Any]        =		parent
        __UpperCamelCase					:List[Any]        =		batch_size
        __UpperCamelCase					:Optional[Any]        =		min_seq_length
        __UpperCamelCase					:Any        =		max_seq_length
        __UpperCamelCase					:Optional[Any]        =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        __UpperCamelCase					:List[str]        =		padding_value
        __UpperCamelCase					:Union[str, Any]        =		sampling_rate
        __UpperCamelCase					:int        =		return_attention_mask
        __UpperCamelCase					:Dict        =		do_normalize
        __UpperCamelCase					:Tuple        =		feature_size
        __UpperCamelCase					:List[Any]        =		chunk_length
        __UpperCamelCase					:str        =		hop_length







    def       UpperCamelCase__       (     self)	->     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       UpperCamelCase__       (     self			,			__lowercase=False			,			__lowercase=False)	->     Optional[int]:
        def _flatten(__lowercase):
            return list(itertools.chain(*__lowercase))

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


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






    '''simple docstring'''






    a__     :						Optional[Any]      =				WhisperFeatureExtractor if is_speech_available() else None







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:int        =		WhisperFeatureExtractionTester(self)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:str        =		self.feature_extraction_class(**self.feat_extract_dict)

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

        __UpperCamelCase					:str        =		feat_extract_first.to_dict()
        __UpperCamelCase					:Optional[int]        =		feat_extract_second.to_dict()
        __UpperCamelCase					:Any        =		feat_extract_first.mel_filters
        __UpperCamelCase					:int        =		feat_extract_second.mel_filters
        self.assertTrue(np.allclose(__lowercase			,			__lowercase))
        self.assertEqual(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Union[str, Any]        =		self.feature_extraction_class(**self.feat_extract_dict)

        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:Optional[int]        =		os.path.join(__lowercase			,			'''feat_extract.json''')
            feat_extract_first.to_json_file(__lowercase)
            __UpperCamelCase					:Optional[int]        =		self.feature_extraction_class.from_json_file(__lowercase)

        __UpperCamelCase					:Union[str, Any]        =		feat_extract_first.to_dict()
        __UpperCamelCase					:Optional[Any]        =		feat_extract_second.to_dict()
        __UpperCamelCase					:Dict        =		feat_extract_first.mel_filters
        __UpperCamelCase					:str        =		feat_extract_second.mel_filters
        self.assertTrue(np.allclose(__lowercase			,			__lowercase))
        self.assertEqual(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        __UpperCamelCase					:Any        =		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(800			,			1_400			,			200)]
        __UpperCamelCase					:Union[str, Any]        =		[np.asarray(__lowercase) for speech_input in speech_inputs]

        # Test feature size
        __UpperCamelCase					:Union[str, Any]        =		feature_extractor(__lowercase			,			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
        __UpperCamelCase					:Optional[int]        =		feature_extractor(speech_inputs[0]			,			return_tensors='''np''').input_features
        __UpperCamelCase					:int        =		feature_extractor(np_speech_inputs[0]			,			return_tensors='''np''').input_features
        self.assertTrue(np.allclose(__lowercase			,			__lowercase			,			atol=1E-3))

        # Test batched
        __UpperCamelCase					:Dict        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
        __UpperCamelCase					:str        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
        for enc_seq_a, enc_seq_a in zip(__lowercase			,			__lowercase):
            self.assertTrue(np.allclose(__lowercase			,			__lowercase			,			atol=1E-3))

        # Test 2-D numpy arrays are batched.
        __UpperCamelCase					:Any        =		[floats_list((1, x))[0] for x in (800, 800, 800)]
        __UpperCamelCase					:str        =		np.asarray(__lowercase)
        __UpperCamelCase					:List[Any]        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
        __UpperCamelCase					:int        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
        for enc_seq_a, enc_seq_a in zip(__lowercase			,			__lowercase):
            self.assertTrue(np.allclose(__lowercase			,			__lowercase			,			atol=1E-3))

        # Test truncation required
        __UpperCamelCase					:Optional[Any]        =		[floats_list((1, x))[0] for x in range(200			,			(feature_extractor.n_samples + 500)			,			200)]
        __UpperCamelCase					:Tuple        =		[np.asarray(__lowercase) for speech_input in speech_inputs]

        __UpperCamelCase					:Optional[int]        =		[x[: feature_extractor.n_samples] for x in speech_inputs]
        __UpperCamelCase					:Any        =		[np.asarray(__lowercase) for speech_input in speech_inputs_truncated]

        __UpperCamelCase					:str        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
        __UpperCamelCase					:Union[str, Any]        =		feature_extractor(__lowercase			,			return_tensors='''np''').input_features
        for enc_seq_a, enc_seq_a in zip(__lowercase			,			__lowercase):
            self.assertTrue(np.allclose(__lowercase			,			__lowercase			,			atol=1E-3))







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        import torch

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

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







    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[str]:
        __UpperCamelCase					:Optional[Any]        =		load_dataset('''hf-internal-testing/librispeech_asr_dummy'''			,			'''clean'''			,			split='''validation''')
        # automatic decoding with librispeech
        __UpperCamelCase					:Optional[Any]        =		ds.sort('''id''').select(range(__lowercase))[:num_samples]['''audio''']

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







    def       UpperCamelCase__       (     self)	->     Any:
        # fmt: off
        __UpperCamelCase					:str        =		torch.tensor(
            [
                0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
                0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
                0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
                -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
            ])
        # fmt: on

        __UpperCamelCase					:Optional[Any]        =		self._load_datasamples(1)
        __UpperCamelCase					:Optional[Any]        =		WhisperFeatureExtractor()
        __UpperCamelCase					:Tuple        =		feature_extractor(__lowercase			,			return_tensors='''pt''').input_features
        self.assertEqual(input_features.shape			,			(1, 80, 3_000))
        self.assertTrue(torch.allclose(input_features[0, 0, :30]			,			__lowercase			,			atol=1E-4))







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:int        =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        __UpperCamelCase					:int        =		self._load_datasamples(1)[0]
        __UpperCamelCase					:List[str]        =		((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
        __UpperCamelCase					:Any        =		feat_extract.zero_mean_unit_var_norm([audio]			,			attention_mask=__lowercase)[0]

        self.assertTrue(np.all(np.mean(__lowercase) < 1E-3))
        self.assertTrue(np.all(np.abs(np.var(__lowercase) - 1) < 1E-3))






from __future__ import annotations

from math import pi



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if (inductance, frequency, reactance).count(0     ) != 1:
        raise ValueError('''One and only one argument must be 0'''     )
    if inductance < 0:
        raise ValueError('''Inductance cannot be negative'''     )
    if frequency < 0:
        raise ValueError('''Frequency cannot be negative'''     )
    if reactance < 0:
        raise ValueError('''Inductive reactance cannot be negative'''     )
    if inductance == 0:
        return {"inductance": reactance / (2 * pi * frequency)}
    elif frequency == 0:
        return {"frequency": reactance / (2 * pi * inductance)}
    elif reactance == 0:
        return {"reactance": 2 * pi * frequency * inductance}
    else:
        raise ValueError('''Exactly one argument must be 0'''     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





