


'''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


__UpperCAmelCase       =							random.Random()


def  _snake_case       (					A :    Optional[int]			,   A :    Any=1.0			,   A :    Dict=None			,   A :    List[str]=None	)						->   Union[str, 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
class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_=7 ,   lowerCamelCase_=4_00 ,   lowerCamelCase_=20_00 ,   lowerCamelCase_=1 ,   lowerCamelCase_=0.0 ,   lowerCamelCase_=1_60_00 ,   lowerCamelCase_=True ,   lowerCamelCase_=80 ,   lowerCamelCase_=16 ,   lowerCamelCase_=64 ,   lowerCamelCase_="hann_window" ,   lowerCamelCase_=80 ,   lowerCamelCase_=76_00 ,   lowerCamelCase_=1e-10 ,   lowerCamelCase_=True ,   )	->							Optional[Any]:
														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__	       =     padding_value
														lowerCAmelCase__	       =     sampling_rate
														lowerCAmelCase__	       =     do_normalize
														lowerCAmelCase__	       =     num_mel_bins
														lowerCAmelCase__	       =     hop_length
														lowerCAmelCase__	       =     win_length
														lowerCAmelCase__	       =     win_function
														lowerCAmelCase__	       =     fmin
														lowerCAmelCase__	       =     fmax
														lowerCAmelCase__	       =     mel_floor
														lowerCAmelCase__	       =     return_attention_mask







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_=False ,   lowerCamelCase_=False       )	->							int:
														def _flatten(lowerCamelCase_       ):
																					return list(itertools.chain(*lowercase_       )       )

														if equal_length:
																					lowerCAmelCase__	       =     floats_list((self.batch_size, self.max_seq_length)       )
														else:
																					# make sure that inputs increase in size
																					lowerCAmelCase__	       =     [
																					    _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:
																					lowerCAmelCase__	       =     [np.asarray(lowercase_       ) for x in speech_inputs]

														return speech_inputs







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_=False ,   lowerCamelCase_=False       )	->							str:
														if equal_length:
																					lowerCAmelCase__	       =     [floats_list((self.max_seq_length, self.num_mel_bins)       ) for _ in range(self.batch_size       )]
														else:
																					# make sure that inputs increase in size
																					lowerCAmelCase__	       =     [
																					    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:
																					lowerCAmelCase__	       =     [np.asarray(lowercase_       ) for x in speech_inputs]

														return speech_inputs




@require_torch
class 		a__    (							_UpperCAmelCase    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				SpeechTaFeatureExtractor







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     SpeechTaFeatureExtractionTester(self       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Any:
														self.assertTrue(np.all(np.mean(lowercase_ ,   axis=0       ) < 1e-3       )       )
														self.assertTrue(np.all(np.abs(np.var(lowercase_ ,   axis=0       ) - 1       ) < 1e-3       )       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														# 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_00 ,   14_00 ,   2_00       )]
														lowerCAmelCase__	       =     [np.asarray(lowercase_       ) for speech_input in speech_inputs]

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

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														lowerCAmelCase__	       =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
														lowerCAmelCase__	       =     [floats_list((1, x)       )[0] for x in range(8_00 ,   14_00 ,   2_00       )]

														lowerCAmelCase__	       =     ["""longest""", """max_length""", """do_not_pad"""]
														lowerCAmelCase__	       =     [None, 16_00, None]
														for max_length, padding in zip(lowercase_ ,   lowercase_       ):
																					lowerCAmelCase__	       =     feat_extract(lowercase_ ,   padding=lowercase_ ,   max_length=lowercase_ ,   return_tensors='''np'''       )
																					lowerCAmelCase__	       =     processed.input_values

																					self._check_zero_mean_unit_variance(input_values[0][:8_00]       )
																					self.assertTrue(input_values[0][8_00:].sum() < 1e-6       )
																					self._check_zero_mean_unit_variance(input_values[1][:10_00]       )
																					self.assertTrue(input_values[0][10_00:].sum() < 1e-6       )
																					self._check_zero_mean_unit_variance(input_values[2][:12_00]       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														lowerCAmelCase__	       =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
														lowerCAmelCase__	       =     range(8_00 ,   14_00 ,   2_00       )
														lowerCAmelCase__	       =     [floats_list((1, x)       )[0] for x in lengths]

														lowerCAmelCase__	       =     ["""longest""", """max_length""", """do_not_pad"""]
														lowerCAmelCase__	       =     [None, 16_00, None]

														for max_length, padding in zip(lowercase_ ,   lowercase_       ):
																					lowerCAmelCase__	       =     feat_extract(lowercase_ ,   max_length=lowercase_ ,   padding=lowercase_       )
																					lowerCAmelCase__	       =     processed.input_values

																					self._check_zero_mean_unit_variance(input_values[0][:8_00]       )
																					self._check_zero_mean_unit_variance(input_values[1][:10_00]       )
																					self._check_zero_mean_unit_variance(input_values[2][:12_00]       )







							def   __SCREAMING_SNAKE_CASE							(		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_00 ,   14_00 ,   2_00       )]
														lowerCAmelCase__	       =     feat_extract(
														    lowercase_ ,   truncation=lowercase_ ,   max_length=10_00 ,   padding='''max_length''' ,   return_tensors='''np'''       )
														lowerCAmelCase__	       =     processed.input_values

														self._check_zero_mean_unit_variance(input_values[0, :8_00]       )
														self._check_zero_mean_unit_variance(input_values[1]       )
														self._check_zero_mean_unit_variance(input_values[2]       )







							def   __SCREAMING_SNAKE_CASE							(		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_00 ,   14_00 ,   2_00       )]
														lowerCAmelCase__	       =     feat_extract(
														    lowercase_ ,   truncation=lowercase_ ,   max_length=10_00 ,   padding='''longest''' ,   return_tensors='''np'''       )
														lowerCAmelCase__	       =     processed.input_values

														self._check_zero_mean_unit_variance(input_values[0, :8_00]       )
														self._check_zero_mean_unit_variance(input_values[1, :10_00]       )
														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, 10_00)       )

														lowerCAmelCase__	       =     [floats_list((1, x)       )[0] for x in range(8_00 ,   14_00 ,   2_00       )]
														lowerCAmelCase__	       =     feat_extract(
														    lowercase_ ,   truncation=lowercase_ ,   max_length=20_00 ,   padding='''longest''' ,   return_tensors='''np'''       )
														lowerCAmelCase__	       =     processed.input_values

														self._check_zero_mean_unit_variance(input_values[0, :8_00]       )
														self._check_zero_mean_unit_variance(input_values[1, :10_00]       )
														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, 12_00)       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
														lowerCAmelCase__	       =     np.random.rand(1_00       ).astype(np.floataa       )
														lowerCAmelCase__	       =     np_speech_inputs.tolist()

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







							def   __SCREAMING_SNAKE_CASE							(		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_00 ,   14_00 ,   2_00       )]
														lowerCAmelCase__	       =     [np.asarray(lowercase_       ) for speech_input in speech_inputs]

														# Test feature size
														lowerCAmelCase__	       =     feature_extractor(audio_target=lowercase_ ,   padding=lowercase_ ,   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
														lowerCAmelCase__	       =     feature_extractor(speech_inputs[0] ,   return_tensors='''np'''       ).input_values
														lowerCAmelCase__	       =     feature_extractor(np_speech_inputs[0] ,   return_tensors='''np'''       ).input_values
														self.assertTrue(np.allclose(lowercase_ ,   lowercase_ ,   atol=1e-3       )       )

														# Test batched
														lowerCAmelCase__	       =     feature_extractor(lowercase_ ,   return_tensors='''np'''       ).input_values
														lowerCAmelCase__	       =     feature_extractor(lowercase_ ,   return_tensors='''np'''       ).input_values
														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.
														lowerCAmelCase__	       =     [floats_list((1, x)       )[0] for x in (8_00, 8_00, 8_00)]
														lowerCAmelCase__	       =     np.asarray(lowercase_       )
														lowerCAmelCase__	       =     feature_extractor(lowercase_ ,   return_tensors='''np'''       ).input_values
														lowerCAmelCase__	       =     feature_extractor(lowercase_ ,   return_tensors='''np'''       ).input_values
														for enc_seq_a, enc_seq_a in zip(lowercase_ ,   lowercase_       ):
																					self.assertTrue(np.allclose(lowercase_ ,   lowercase_ ,   atol=1e-3       )       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     self.feat_extract_tester.prepare_inputs_for_target()
														lowerCAmelCase__	       =     self.feature_extraction_class(**self.feat_extract_dict       )
														lowerCAmelCase__	       =     feat_extract.model_input_names[0]

														lowerCAmelCase__	       =     BatchFeature({input_name: speech_inputs}       )

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

														lowerCAmelCase__	       =     self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase_       )
														lowerCAmelCase__	       =     BatchFeature({input_name: speech_inputs} ,   tensor_type='''np'''       )

														lowerCAmelCase__	       =     processed_features[input_name]

														if len(batch_features_input.shape       ) < 3:
																					lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase_       )
														lowerCAmelCase__	       =     self.feature_extraction_class(**self.feat_extract_dict       )
														lowerCAmelCase__	       =     feat_extract.model_input_names[0]

														lowerCAmelCase__	       =     BatchFeature({input_name: speech_inputs} ,   tensor_type='''pt'''       )

														lowerCAmelCase__	       =     processed_features[input_name]

														if len(batch_features_input.shape       ) < 3:
																					lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     self.feature_extraction_class(**self.feat_extract_dict       )
														lowerCAmelCase__	       =     self.feat_extract_tester.prepare_inputs_for_target()
														lowerCAmelCase__	       =     feat_extract.model_input_names[0]

														lowerCAmelCase__	       =     BatchFeature({input_name: speech_inputs}       )

														lowerCAmelCase__	       =     feat_extract.num_mel_bins  # hack!

														lowerCAmelCase__	       =     feat_extract.pad(lowercase_ ,   padding='''longest''' ,   return_tensors='''np'''       )[input_name]
														lowerCAmelCase__	       =     feat_extract.pad(lowercase_ ,   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   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__	       =     self.feat_extract_dict
														lowerCAmelCase__	       =     True
														lowerCAmelCase__	       =     self.feature_extraction_class(**lowercase_       )
														lowerCAmelCase__	       =     self.feat_extract_tester.prepare_inputs_for_target()
														lowerCAmelCase__	       =     [len(lowercase_       ) for x in speech_inputs]
														lowerCAmelCase__	       =     feat_extract.model_input_names[0]

														lowerCAmelCase__	       =     BatchFeature({input_name: speech_inputs}       )

														lowerCAmelCase__	       =     feat_extract.num_mel_bins  # hack!

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     self.feat_extract_dict
														lowerCAmelCase__	       =     True
														lowerCAmelCase__	       =     self.feature_extraction_class(**lowercase_       )
														lowerCAmelCase__	       =     self.feat_extract_tester.prepare_inputs_for_target()
														lowerCAmelCase__	       =     [len(lowercase_       ) for x in speech_inputs]
														lowerCAmelCase__	       =     feat_extract.model_input_names[0]

														lowerCAmelCase__	       =     BatchFeature({input_name: speech_inputs}       )
														lowerCAmelCase__	       =     min(lowercase_       )

														lowerCAmelCase__	       =     feat_extract.num_mel_bins  # hack!

														lowerCAmelCase__	       =     feat_extract.pad(
														    lowercase_ ,   padding='''max_length''' ,   max_length=lowercase_ ,   truncation=lowercase_ ,   return_tensors='''np'''       )
														self.assertIn('''attention_mask''' ,   lowercase_       )
														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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Dict:
														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(lowercase_       )       )[:num_samples]["""audio"""]

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														# fmt: off
														lowerCAmelCase__	       =     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

														lowerCAmelCase__	       =     self._load_datasamples(1       )
														lowerCAmelCase__	       =     SpeechTaFeatureExtractor()
														lowerCAmelCase__	       =     feature_extractor(lowercase_ ,   return_tensors='''pt'''       ).input_values
														self.assertEquals(input_values.shape ,   (1, 9_36_80)       )
														self.assertTrue(torch.allclose(input_values[0, :30] ,   lowercase_ ,   atol=1e-6       )       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														# fmt: off
														lowerCAmelCase__	       =     torch.tensor(
														    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
														     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
														     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
														     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]       )
														# fmt: on

														lowerCAmelCase__	       =     self._load_datasamples(1       )
														lowerCAmelCase__	       =     SpeechTaFeatureExtractor()
														lowerCAmelCase__	       =     feature_extractor(audio_target=lowercase_ ,   return_tensors='''pt'''       ).input_values
														self.assertEquals(input_values.shape ,   (1, 3_66, 80)       )
														self.assertTrue(torch.allclose(input_values[0, 0, :30] ,   lowercase_ ,   atol=1e-4       )       )



'''simple docstring'''






import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__UpperCAmelCase       =							logging.get_logger(__name__)




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       int     						=				"linear"
							lowercase__      :       Any     						=				"cosine"
							lowercase__      :       Optional[int]     						=				"cosine_with_restarts"
							lowercase__      :       Optional[Any]     						=				"polynomial"
							lowercase__      :       Tuple     						=				"constant"
							lowercase__      :       Optional[int]     						=				"constant_with_warmup"
							lowercase__      :       Optional[int]     						=				"piecewise_constant"


def  _snake_case       (					A			,   A = -1	)						->   Any:
							return LambdaLR(A			,   lambda A	: 1			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A = -1	)						->   Optional[Any]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1.0			,   A	)	)
														return 1.0

							return LambdaLR(A			,   A			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A = -1	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     {}
							lowerCAmelCase__	       =     step_rules.split(''','''	)
							for rule_str in rule_list[:-1]:
														lowerCAmelCase__       , lowerCAmelCase__	       =     rule_str.split(''':'''	)
														lowerCAmelCase__	       =     int(A	)
														lowerCAmelCase__	       =     float(A	)
														lowerCAmelCase__	       =     value
							lowerCAmelCase__	       =     float(rule_list[-1]	)

							def create_rules_function(A			,   A	):
														def rule_func(A	) -> float:
																					lowerCAmelCase__	       =     sorted(rules_dict.keys()	)
																					for i, sorted_step in enumerate(A	):
																												if steps < sorted_step:
																																			return rules_dict[sorted_steps[i]]
																					return last_lr_multiple

														return rule_func

							lowerCAmelCase__	       =     create_rules_function(A			,   A	)

							return LambdaLR(A			,   A			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A			,   A=-1	)						->   Optional[int]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														return max(
														    0.0			,   float(num_training_steps - current_step	) / float(max(1			,   num_training_steps - num_warmup_steps	)	)	)

							return LambdaLR(A			,   A			,   A	)


def  _snake_case       (					A			,   A			,   A			,   A = 0.5			,   A = -1	)						->   List[str]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														lowerCAmelCase__	       =     float(current_step - num_warmup_steps	) / float(max(1			,   num_training_steps - num_warmup_steps	)	)
														return max(0.0			,   0.5 * (1.0 + math.cos(math.pi * float(A	) * 2.0 * progress	))	)

							return LambdaLR(A			,   A			,   A	)


def  _snake_case       (					A			,   A			,   A			,   A = 1			,   A = -1	)						->   Union[str, Any]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														lowerCAmelCase__	       =     float(current_step - num_warmup_steps	) / float(max(1			,   num_training_steps - num_warmup_steps	)	)
														if progress >= 1.0:
																					return 0.0
														return max(0.0			,   0.5 * (1.0 + math.cos(math.pi * ((float(A	) * progress) % 1.0)	))	)

							return LambdaLR(A			,   A			,   A	)


def  _snake_case       (					A			,   A			,   A			,   A=1E-7			,   A=1.0			,   A=-1	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     optimizer.defaults['''lr''']
							if not (lr_init > lr_end):
														raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""	)

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														elif current_step > num_training_steps:
																					return lr_end / lr_init  # as LambdaLR multiplies by lr_init
														else:
																					lowerCAmelCase__	       =     lr_init - lr_end
																					lowerCAmelCase__	       =     num_training_steps - num_warmup_steps
																					lowerCAmelCase__	       =     1 - (current_step - num_warmup_steps) / decay_steps
																					lowerCAmelCase__	       =     lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(A			,   A			,   A	)


__UpperCAmelCase       =							{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def  _snake_case       (					A			,   A			,   A = None			,   A = None			,   A = None			,   A = 1			,   A = 1.0			,   A = -1			,   )						->   int:
							lowerCAmelCase__	       =     SchedulerType(A	)
							lowerCAmelCase__	       =     TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(A			,   last_epoch=A	)

							if name == SchedulerType.PIECEWISE_CONSTANT:
														return schedule_func(A			,   step_rules=A			,   last_epoch=A	)

							# All other schedulers require `num_warmup_steps`
							if num_warmup_steps is None:
														raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument."""	)

							if name == SchedulerType.CONSTANT_WITH_WARMUP:
														return schedule_func(A			,   num_warmup_steps=A			,   last_epoch=A	)

							# All other schedulers require `num_training_steps`
							if num_training_steps is None:
														raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument."""	)

							if name == SchedulerType.COSINE_WITH_RESTARTS:
														return schedule_func(
														    A			,   num_warmup_steps=A			,   num_training_steps=A			,   num_cycles=A			,   last_epoch=A			,   )

							if name == SchedulerType.POLYNOMIAL:
														return schedule_func(
														    A			,   num_warmup_steps=A			,   num_training_steps=A			,   power=A			,   last_epoch=A			,   )

							return schedule_func(
							    A			,   num_warmup_steps=A			,   num_training_steps=A			,   last_epoch=A	)