
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 List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__a  :     Optional[Any]       =  logging.get_logger(__name__)


__a  :     List[str]       =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = "first_exhausted"							,			):







				"""simple docstring"""



				from .arrow_dataset import Dataset
				from .iterable_dataset import IterableDataset

				if not datasets:
								raise ValueError('''Unable to interleave an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
								raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."							)
				if dataset_type is Dataset:
								return _interleave_map_style_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)
				else:
								return _interleave_iterable_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = 0							,			):







				"""simple docstring"""



				if not dsets:
								raise ValueError('''Unable to concatenate an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if dataset_type is Dataset:
								return _concatenate_map_style_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)
				else:
								return _concatenate_iterable_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)