
import math
from typing import Optional

import numpy as np

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


__a  			=					logging.get_logger(__name__)

__a  			=					{
    '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  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:List[str]         =	'encodec'
	def __init__(       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Tuple=[1.5, 3.0, 6.0, 12.0, 24.0]       ,							SCREAMING_SNAKE_CASE_  :			Dict=2_4_0_0_0       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=1       ,							SCREAMING_SNAKE_CASE_  :			str=False       ,							SCREAMING_SNAKE_CASE_  :			List[str]=None       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=None       ,							SCREAMING_SNAKE_CASE_  :			List[str]=1_2_8       ,							SCREAMING_SNAKE_CASE_  :			str=3_2       ,							SCREAMING_SNAKE_CASE_  :			str=1       ,							SCREAMING_SNAKE_CASE_  :			Dict=[8, 5, 4, 2]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]="weight_norm"       ,							SCREAMING_SNAKE_CASE_  :			str=7       ,							SCREAMING_SNAKE_CASE_  :			Tuple=7       ,							SCREAMING_SNAKE_CASE_  :			Tuple=3       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=2       ,							SCREAMING_SNAKE_CASE_  :			str=True       ,							SCREAMING_SNAKE_CASE_  :			str="reflect"       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=2       ,							SCREAMING_SNAKE_CASE_  :			str=2       ,							SCREAMING_SNAKE_CASE_  :			str=1.0       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=1_0_2_4       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=None       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=True       ,							**SCREAMING_SNAKE_CASE_  :			Dict       ,							)       ->     int:
					lowercase_       =					target_bandwidths
					lowercase_       =					sampling_rate
					lowercase_       =					audio_channels
					lowercase_       =					normalize
					lowercase_       =					chunk_length_s
					lowercase_       =					overlap
					lowercase_       =					hidden_size
					lowercase_       =					num_filters
					lowercase_       =					num_residual_layers
					lowercase_       =					upsampling_ratios
					lowercase_       =					norm_type
					lowercase_       =					kernel_size
					lowercase_       =					last_kernel_size
					lowercase_       =					residual_kernel_size
					lowercase_       =					dilation_growth_rate
					lowercase_       =					use_causal_conv
					lowercase_       =					pad_mode
					lowercase_       =					compress
					lowercase_       =					num_lstm_layers
					lowercase_       =					trim_right_ratio
					lowercase_       =					codebook_size
					lowercase_       =					codebook_dim if codebook_dim is not None else hidden_size
					lowercase_       =					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__(**SCREAMING_SNAKE_CASE_       )
	@property
	def 						_lowercase  (       self  :			List[str]       )       ->     Optional[int]:
					if self.chunk_length_s is None:
									return None
					else:
									return int(self.chunk_length_s * self.sampling_rate       )
	@property
	def 						_lowercase  (       self  :			str       )       ->     Optional[int]:
					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 						_lowercase  (       self  :			Optional[int]       )       ->     int:
					lowercase_       =					np.prod(self.upsampling_ratios       )
					return math.ceil(self.sampling_rate / hop_length       )




	@property
	def 						_lowercase  (       self  :			str       )       ->     int:
					return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)       )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a  			=					{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RemBertForCausalLM',
						    'RemBertForMaskedLM',
						    'RemBertForMultipleChoice',
						    'RemBertForQuestionAnswering',
						    'RemBertForSequenceClassification',
						    'RemBertForTokenClassification',
						    'RemBertLayer',
						    'RemBertModel',
						    'RemBertPreTrainedModel',
						    'load_tf_weights_in_rembert',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRemBertForCausalLM',
						    'TFRemBertForMaskedLM',
						    'TFRemBertForMultipleChoice',
						    'TFRemBertForQuestionAnswering',
						    'TFRemBertForSequenceClassification',
						    'TFRemBertForTokenClassification',
						    'TFRemBertLayer',
						    'TFRemBertModel',
						    'TFRemBertPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert import RemBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert_fast import RemBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rembert import (
												    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RemBertForCausalLM,
												    RemBertForMaskedLM,
												    RemBertForMultipleChoice,
												    RemBertForQuestionAnswering,
												    RemBertForSequenceClassification,
												    RemBertForTokenClassification,
												    RemBertLayer,
												    RemBertModel,
												    RemBertPreTrainedModel,
												    load_tf_weights_in_rembert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rembert import (
												    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRemBertForCausalLM,
												    TFRemBertForMaskedLM,
												    TFRemBertForMultipleChoice,
												    TFRemBertForQuestionAnswering,
												    TFRemBertForSequenceClassification,
												    TFRemBertForTokenClassification,
												    TFRemBertLayer,
												    TFRemBertModel,
												    TFRemBertPreTrainedModel,
												)


else:
						import sys

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




