
'''simple docstring'''
import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


__snake_case							   =  random.Random()


def     a   (    __a				,      __a=1.0				,      __a=None				,      __a=None     )						->			List[Any]:



					'''simple docstring'''






					if rng is None:
										UpperCamelCase__     :Any							      =				global_rng

					UpperCamelCase__     :List[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
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_=7					,     UpperCamelCase_=400					,     UpperCamelCase_=2000					,     UpperCamelCase_=1					,     UpperCamelCase_=0.0					,     UpperCamelCase_=16000					,     UpperCamelCase_=True					,     UpperCamelCase_=80					,     UpperCamelCase_=16					,     UpperCamelCase_=64					,     UpperCamelCase_="hann_window"					,     UpperCamelCase_=80					,     UpperCamelCase_=7600					,     UpperCamelCase_=1e-10					,     UpperCamelCase_=True					,     ):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				parent
							UpperCamelCase__     :Dict							      =				batch_size
							UpperCamelCase__     :List[Any]							      =				min_seq_length
							UpperCamelCase__     :List[Any]							      =				max_seq_length
							UpperCamelCase__     :Optional[Any]							      =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							UpperCamelCase__     :Optional[Any]							      =				feature_size
							UpperCamelCase__     :Optional[Any]							      =				padding_value
							UpperCamelCase__     :Union[str, Any]							      =				sampling_rate
							UpperCamelCase__     :Optional[Any]							      =				do_normalize
							UpperCamelCase__     :Tuple							      =				num_mel_bins
							UpperCamelCase__     :List[str]							      =				hop_length
							UpperCamelCase__     :Any							      =				win_length
							UpperCamelCase__     :Union[str, Any]							      =				win_function
							UpperCamelCase__     :List[str]							      =				fmin
							UpperCamelCase__     :Union[str, Any]							      =				fmax
							UpperCamelCase__     :List[Any]							      =				mel_floor
							UpperCamelCase__     :Optional[int]							      =				return_attention_mask




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "do_normalize": self.do_normalize,
							    "num_mel_bins": self.num_mel_bins,
							    "hop_length": self.hop_length,
							    "win_length": self.win_length,
							    "win_function": self.win_function,
							    "fmin": self.fmin,
							    "fmax": self.fmax,
							    "mel_floor": self.mel_floor,
							    "return_attention_mask": self.return_attention_mask,
							}




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=False					,     UpperCamelCase_=False							):

							'''simple docstring'''


							def _flatten(UpperCamelCase_							):
												return list(itertools.chain(*__lowerCamelCase							)							)

							if equal_length:
												UpperCamelCase__     :Union[str, Any]							      =				floats_list((self.batch_size, self.max_seq_length)							)
							else:
												# make sure that inputs increase in size
												UpperCamelCase__     :Optional[Any]							      =				[
												    _flatten(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__     :Dict							      =				[np.asarray(__lowerCamelCase							) for x in speech_inputs]

							return speech_inputs




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=False					,     UpperCamelCase_=False							):

							'''simple docstring'''


							if equal_length:
												UpperCamelCase__     :Union[str, Any]							      =				[floats_list((self.max_seq_length, self.num_mel_bins)							) for _ in range(self.batch_size							)]
							else:
												# make sure that inputs increase in size
												UpperCamelCase__     :Optional[Any]							      =				[
												    floats_list((x, self.num_mel_bins)							)
												    for x in range(self.min_seq_length					,     self.max_seq_length					,     self.seq_length_diff							)
												]

							if numpify:
												UpperCamelCase__     :Tuple							      =				[np.asarray(__lowerCamelCase							) for x in speech_inputs]

							return speech_inputs



@require_torch
class      lowercase			(							lowerCamelCase__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       SpeechTaFeatureExtractor




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				SpeechTaFeatureExtractionTester(self							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							# create three inputs of length 800, 1000, and 1200
							UpperCamelCase__     :Union[str, Any]							      =				[floats_list((1, x)							)[0] for x in range(800					,     1400					,     200							)]
							UpperCamelCase__     :Optional[int]							      =				[np.asarray(__lowerCamelCase							) for speech_input in speech_inputs]

							# Test not batched input
							UpperCamelCase__     :Dict							      =				feat_extract(speech_inputs[0]					,     return_tensors='''np'''							).input_values
							UpperCamelCase__     :Any							      =				feat_extract(np_speech_inputs[0]					,     return_tensors='''np'''							).input_values
							self.assertTrue(np.allclose(__lowerCamelCase					,     __lowerCamelCase					,     atol=1e-3							)							)

							# Test batched
							UpperCamelCase__     :int							      =				feat_extract(__lowerCamelCase					,     return_tensors='''np'''							).input_values
							UpperCamelCase__     :str							      =				feat_extract(__lowerCamelCase					,     return_tensors='''np'''							).input_values
							for enc_seq_a, enc_seq_a in zip(__lowerCamelCase					,     __lowerCamelCase							):
												self.assertTrue(np.allclose(__lowerCamelCase					,     __lowerCamelCase					,     atol=1e-3							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							UpperCamelCase__     :Optional[Any]							      =				[floats_list((1, x)							)[0] for x in range(800					,     1400					,     200							)]

							UpperCamelCase__     :List[str]							      =				['''longest''', '''max_length''', '''do_not_pad''']
							UpperCamelCase__     :Optional[Any]							      =				[None, 1600, None]
							for max_length, padding in zip(__lowerCamelCase					,     __lowerCamelCase							):
												UpperCamelCase__     :Any							      =				feat_extract(__lowerCamelCase					,     padding=__lowerCamelCase					,     max_length=__lowerCamelCase					,     return_tensors='''np'''							)
												UpperCamelCase__     :Optional[int]							      =				processed.input_values

												self._check_zero_mean_unit_variance(input_values[0][:800]							)
												self.assertTrue(input_values[0][800:].sum() < 1e-6							)
												self._check_zero_mean_unit_variance(input_values[1][:1000]							)
												self.assertTrue(input_values[0][1000:].sum() < 1e-6							)
												self._check_zero_mean_unit_variance(input_values[2][:1200]							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							UpperCamelCase__     :List[str]							      =				range(800					,     1400					,     200							)
							UpperCamelCase__     :Optional[Any]							      =				[floats_list((1, x)							)[0] for x in lengths]

							UpperCamelCase__     :Union[str, Any]							      =				['''longest''', '''max_length''', '''do_not_pad''']
							UpperCamelCase__     :int							      =				[None, 1600, None]

							for max_length, padding in zip(__lowerCamelCase					,     __lowerCamelCase							):
												UpperCamelCase__     :str							      =				feat_extract(__lowerCamelCase					,     max_length=__lowerCamelCase					,     padding=__lowerCamelCase							)
												UpperCamelCase__     :Tuple							      =				processed.input_values

												self._check_zero_mean_unit_variance(input_values[0][:800]							)
												self._check_zero_mean_unit_variance(input_values[1][:1000]							)
												self._check_zero_mean_unit_variance(input_values[2][:1200]							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							UpperCamelCase__     :Optional[Any]							      =				[floats_list((1, x)							)[0] for x in range(800					,     1400					,     200							)]
							UpperCamelCase__     :Optional[int]							      =				feat_extract(
							    __lowerCamelCase					,     truncation=__lowerCamelCase					,     max_length=1000					,     padding='''max_length'''					,     return_tensors='''np'''							)
							UpperCamelCase__     :List[Any]							      =				processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]							)
							self._check_zero_mean_unit_variance(input_values[1]							)
							self._check_zero_mean_unit_variance(input_values[2]							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							UpperCamelCase__     :List[str]							      =				[floats_list((1, x)							)[0] for x in range(800					,     1400					,     200							)]
							UpperCamelCase__     :Union[str, Any]							      =				feat_extract(
							    __lowerCamelCase					,     truncation=__lowerCamelCase					,     max_length=1000					,     padding='''longest'''					,     return_tensors='''np'''							)
							UpperCamelCase__     :Optional[int]							      =				processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]							)
							self._check_zero_mean_unit_variance(input_values[1, :1000]							)
							self._check_zero_mean_unit_variance(input_values[2]							)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertTrue(input_values.shape == (3, 1000)							)

							UpperCamelCase__     :Tuple							      =				[floats_list((1, x)							)[0] for x in range(800					,     1400					,     200							)]
							UpperCamelCase__     :Optional[int]							      =				feat_extract(
							    __lowerCamelCase					,     truncation=__lowerCamelCase					,     max_length=2000					,     padding='''longest'''					,     return_tensors='''np'''							)
							UpperCamelCase__     :Optional[Any]							      =				processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]							)
							self._check_zero_mean_unit_variance(input_values[1, :1000]							)
							self._check_zero_mean_unit_variance(input_values[2]							)

							# make sure that if max_length > longest -> then pad to longest
							self.assertTrue(input_values.shape == (3, 1200)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							# create three inputs of length 800, 1000, and 1200
							UpperCamelCase__     :Any							      =				[floats_list((1, x)							)[0] for x in range(800					,     1400					,     200							)]
							UpperCamelCase__     :Optional[Any]							      =				[np.asarray(__lowerCamelCase							) for speech_input in speech_inputs]

							# Test feature size
							UpperCamelCase__     :List[str]							      =				feature_extractor(audio_target=__lowerCamelCase					,     padding=__lowerCamelCase					,     return_tensors='''np'''							).input_values
							self.assertTrue(input_values.ndim == 3							)
							self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins							)

							# Test not batched input
							UpperCamelCase__     :List[str]							      =				feature_extractor(speech_inputs[0]					,     return_tensors='''np'''							).input_values
							UpperCamelCase__     :Optional[int]							      =				feature_extractor(np_speech_inputs[0]					,     return_tensors='''np'''							).input_values
							self.assertTrue(np.allclose(__lowerCamelCase					,     __lowerCamelCase					,     atol=1e-3							)							)

							# Test batched
							UpperCamelCase__     :Dict							      =				feature_extractor(__lowerCamelCase					,     return_tensors='''np'''							).input_values
							UpperCamelCase__     :List[Any]							      =				feature_extractor(__lowerCamelCase					,     return_tensors='''np'''							).input_values
							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.
							UpperCamelCase__     :Tuple							      =				[floats_list((1, x)							)[0] for x in (800, 800, 800)]
							UpperCamelCase__     :Optional[int]							      =				np.asarray(__lowerCamelCase							)
							UpperCamelCase__     :Dict							      =				feature_extractor(__lowerCamelCase					,     return_tensors='''np'''							).input_values
							UpperCamelCase__     :Dict							      =				feature_extractor(__lowerCamelCase					,     return_tensors='''np'''							).input_values
							for enc_seq_a, enc_seq_a in zip(__lowerCamelCase					,     __lowerCamelCase							):
												self.assertTrue(np.allclose(__lowerCamelCase					,     __lowerCamelCase					,     atol=1e-3							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self.feat_extract_tester.prepare_inputs_for_target()
							UpperCamelCase__     :Union[str, Any]							      =				self.feature_extraction_class(**self.feat_extract_dict							)
							UpperCamelCase__     :Optional[int]							      =				feat_extract.model_input_names[0]

							UpperCamelCase__     :Dict							      =				BatchFeature({input_name: speech_inputs}							)

							self.assertTrue(all(len(__lowerCamelCase							) == len(__lowerCamelCase							) for x, y in zip(__lowerCamelCase					,     processed_features[input_name]							)							)							)

							UpperCamelCase__     :str							      =				self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowerCamelCase							)
							UpperCamelCase__     :List[Any]							      =				BatchFeature({input_name: speech_inputs}					,     tensor_type='''np'''							)

							UpperCamelCase__     :int							      =				processed_features[input_name]

							if len(batch_features_input.shape							) < 3:
												UpperCamelCase__     :List[str]							      =				batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]							), self.feat_extract_tester.num_mel_bins)							)




		@require_torch
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.feat_extract_tester.prepare_inputs_for_target(equal_length=__lowerCamelCase							)
							UpperCamelCase__     :List[Any]							      =				self.feature_extraction_class(**self.feat_extract_dict							)
							UpperCamelCase__     :int							      =				feat_extract.model_input_names[0]

							UpperCamelCase__     :Union[str, Any]							      =				BatchFeature({input_name: speech_inputs}					,     tensor_type='''pt'''							)

							UpperCamelCase__     :Optional[int]							      =				processed_features[input_name]

							if len(batch_features_input.shape							) < 3:
												UpperCamelCase__     :Optional[int]							      =				batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]							), self.feat_extract_tester.num_mel_bins)							)




		@require_torch
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				self.feature_extraction_class(**self.feat_extract_dict							)
							UpperCamelCase__     :List[str]							      =				self.feat_extract_tester.prepare_inputs_for_target()
							UpperCamelCase__     :Optional[int]							      =				feat_extract.model_input_names[0]

							UpperCamelCase__     :Union[str, Any]							      =				BatchFeature({input_name: speech_inputs}							)

							UpperCamelCase__     :Optional[int]							      =				feat_extract.num_mel_bins  # hack!

							UpperCamelCase__     :List[str]							      =				feat_extract.pad(__lowerCamelCase					,     padding='''longest'''					,     return_tensors='''np'''							)[input_name]
							UpperCamelCase__     :Any							      =				feat_extract.pad(__lowerCamelCase					,     padding='''longest'''					,     return_tensors='''pt'''							)[input_name]

							self.assertTrue(abs(input_np.astype(np.floataa							).sum() - input_pt.numpy().astype(np.floataa							).sum()							) < 1e-2							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.feat_extract_dict
							UpperCamelCase__     :Optional[Any]							      =				True
							UpperCamelCase__     :Optional[Any]							      =				self.feature_extraction_class(**__lowerCamelCase							)
							UpperCamelCase__     :int							      =				self.feat_extract_tester.prepare_inputs_for_target()
							UpperCamelCase__     :Optional[int]							      =				[len(__lowerCamelCase							) for x in speech_inputs]
							UpperCamelCase__     :Union[str, Any]							      =				feat_extract.model_input_names[0]

							UpperCamelCase__     :Tuple							      =				BatchFeature({input_name: speech_inputs}							)

							UpperCamelCase__     :str							      =				feat_extract.num_mel_bins  # hack!

							UpperCamelCase__     :List[Any]							      =				feat_extract.pad(__lowerCamelCase					,     padding='''longest'''					,     return_tensors='''np'''							)
							self.assertIn('''attention_mask'''					,     __lowerCamelCase							)
							self.assertListEqual(list(processed.attention_mask.shape							)					,     list(processed[input_name].shape[:2]							)							)
							self.assertListEqual(processed.attention_mask.sum(-1							).tolist()					,     __lowerCamelCase							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.feat_extract_dict
							UpperCamelCase__     :Any							      =				True
							UpperCamelCase__     :List[str]							      =				self.feature_extraction_class(**__lowerCamelCase							)
							UpperCamelCase__     :Any							      =				self.feat_extract_tester.prepare_inputs_for_target()
							UpperCamelCase__     :Tuple							      =				[len(__lowerCamelCase							) for x in speech_inputs]
							UpperCamelCase__     :str							      =				feat_extract.model_input_names[0]

							UpperCamelCase__     :int							      =				BatchFeature({input_name: speech_inputs}							)
							UpperCamelCase__     :Optional[int]							      =				min(__lowerCamelCase							)

							UpperCamelCase__     :Dict							      =				feat_extract.num_mel_bins  # hack!

							UpperCamelCase__     :Union[str, Any]							      =				feat_extract.pad(
							    __lowerCamelCase					,     padding='''max_length'''					,     max_length=__lowerCamelCase					,     truncation=__lowerCamelCase					,     return_tensors='''np'''							)
							self.assertIn('''attention_mask'''					,     __lowerCamelCase							)
							self.assertListEqual(
							    list(processed_pad.attention_mask.shape							)					,     [processed_pad[input_name].shape[0], max_length]							)
							self.assertListEqual(
							    processed_pad.attention_mask[:, :max_length].sum(-1							).tolist()					,     [max_length for x in speech_inputs]							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							from datasets import load_dataset

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

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				torch.tensor(
							    [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
							     3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
							     2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
							     4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
							     7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
							     4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]							)
							# fmt: on

							UpperCamelCase__     :Dict							      =				self._load_datasamples(1							)
							UpperCamelCase__     :Any							      =				SpeechTaFeatureExtractor()
							UpperCamelCase__     :Dict							      =				feature_extractor(__lowerCamelCase					,     return_tensors='''pt'''							).input_values
							self.assertEquals(input_values.shape					,     (1, 93680)							)
							self.assertTrue(torch.allclose(input_values[0, :30]					,     __lowerCamelCase					,     atol=1e-6							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.tensor(
							    [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
							     -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
							     -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
							     -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]							)
							# fmt: on

							UpperCamelCase__     :Tuple							      =				self._load_datasamples(1							)
							UpperCamelCase__     :Optional[Any]							      =				SpeechTaFeatureExtractor()
							UpperCamelCase__     :Any							      =				feature_extractor(audio_target=__lowerCamelCase					,     return_tensors='''pt'''							).input_values
							self.assertEquals(input_values.shape					,     (1, 366, 80)							)
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,     __lowerCamelCase					,     atol=1e-4							)							)



'''simple docstring'''
from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__snake_case							   =  logging.get_logger(__name__)
class      lowercase			:

		"""simple docstring"""

		_a									=       42
		_a									=       None




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if not self.is_available():
												raise RuntimeError(
												    F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''							)




		@classmethod
		def 					lowerCAmelCase__       (	cls							):

							'''simple docstring'''


							return F'''`pip install {cls.pip_package or cls.name}`'''



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'optuna'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_optuna_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_optuna(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_optuna(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'ray'
		_a									=       '\'ray[tune]\''




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_ray_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_ray(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_ray(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'sigopt'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_sigopt_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_sigopt(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_sigopt(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'wandb'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_wandb_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_wandb(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_wandb(UpperCamelCase_							)



__snake_case							   =  {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}


def     a   (    )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
					if len(__a     ) > 0:
										UpperCamelCase__     :Tuple							      =				available_backends[0].name
										if len(__a     ) > 1:
															logger.info(
															    f'''{len(__a     )} hyperparameter search backends available. Using {name} as the default.'''     )
										return name
					raise RuntimeError(
					    '''No hyperparameter search backend available.\n'''
					    + '''\n'''.join(
					        f''' - To install {backend.name} run {backend.pip_install()}'''
					        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()     )     )