'''simple docstring'''




import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase          =  logging.get_logger(__name__)

__lowercase          =  {
    '''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
    '''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Union[str, Any]      =       '''encodec'''



			def __init__(   self       ,    __lowerCAmelCase=[1.5, 3.0, 6.0, 12.0, 24.0]       ,    __lowerCAmelCase=24000       ,    __lowerCAmelCase=1       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=128       ,    __lowerCAmelCase=32       ,    __lowerCAmelCase=1       ,    __lowerCAmelCase=[8, 5, 4, 2]       ,    __lowerCAmelCase="weight_norm"       ,    __lowerCAmelCase=7       ,    __lowerCAmelCase=7       ,    __lowerCAmelCase=3       ,    __lowerCAmelCase=2       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase="reflect"       ,    __lowerCAmelCase=2       ,    __lowerCAmelCase=2       ,    __lowerCAmelCase=1.0       ,    __lowerCAmelCase=1024       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  target_bandwidths
					lowerCAmelCase   					=  sampling_rate
					lowerCAmelCase   					=  audio_channels
					lowerCAmelCase   					=  normalize
					lowerCAmelCase   					=  chunk_length_s
					lowerCAmelCase   					=  overlap
					lowerCAmelCase   					=  hidden_size
					lowerCAmelCase   					=  num_filters
					lowerCAmelCase   					=  num_residual_layers
					lowerCAmelCase   					=  upsampling_ratios
					lowerCAmelCase   					=  norm_type
					lowerCAmelCase   					=  kernel_size
					lowerCAmelCase   					=  last_kernel_size
					lowerCAmelCase   					=  residual_kernel_size
					lowerCAmelCase   					=  dilation_growth_rate
					lowerCAmelCase   					=  use_causal_conv
					lowerCAmelCase   					=  pad_mode
					lowerCAmelCase   					=  compress
					lowerCAmelCase   					=  num_lstm_layers
					lowerCAmelCase   					=  trim_right_ratio
					lowerCAmelCase   					=  codebook_size
					lowerCAmelCase   					=  codebook_dim if codebook_dim is not None else hidden_size
					lowerCAmelCase   					=  use_conv_shortcut

					if self.norm_type not in ["weight_norm", "time_group_norm"]:
							raise ValueError(
							    f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}")

					super().__init__(**__lowerCAmelCase)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					if self.chunk_length_s is None:
							return None
					else:
							return int(self.chunk_length_s * self.sampling_rate)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					if self.chunk_length_s is None or self.overlap is None:
							return None
					else:
							return max(1       ,    int((1.0 - self.overlap) * self.chunk_length))



			@property
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  np.prod(self.upsampling_ratios)
					return math.ceil(self.sampling_rate / hop_length)







			@property
			def 							a_       (   self):




					"""simple docstring"""


					return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10))





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowercase          =  {
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''NezhaForNextSentencePrediction''',
						    '''NezhaForMaskedLM''',
						    '''NezhaForPreTraining''',
						    '''NezhaForMultipleChoice''',
						    '''NezhaForQuestionAnswering''',
						    '''NezhaForSequenceClassification''',
						    '''NezhaForTokenClassification''',
						    '''NezhaModel''',
						    '''NezhaPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




