"""simple docstring"""


import math
from typing import Optional

import numpy as np

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


__UpperCamelCase									=			logging.get_logger(__name__)

__UpperCamelCase									=			{
    '''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  UpperCamelCase					(				__a   ):
       SCREAMING_SNAKE_CASE_			=      """encodec"""







       def __init__( self,							lowerCAmelCase__=[1.5, 3.0, 6.0, 12.0, 24.0],							lowerCAmelCase__=2_4000,							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__,							)      ->      List[Any]:
            snake_case_					       =					target_bandwidths
            snake_case_					       =					sampling_rate
            snake_case_					       =					audio_channels
            snake_case_					       =					normalize
            snake_case_					       =					chunk_length_s
            snake_case_					       =					overlap
            snake_case_					       =					hidden_size
            snake_case_					       =					num_filters
            snake_case_					       =					num_residual_layers
            snake_case_					       =					upsampling_ratios
            snake_case_					       =					norm_type
            snake_case_					       =					kernel_size
            snake_case_					       =					last_kernel_size
            snake_case_					       =					residual_kernel_size
            snake_case_					       =					dilation_growth_rate
            snake_case_					       =					use_causal_conv
            snake_case_					       =					pad_mode
            snake_case_					       =					compress
            snake_case_					       =					num_lstm_layers
            snake_case_					       =					trim_right_ratio
            snake_case_					       =					codebook_size
            snake_case_					       =					codebook_dim if codebook_dim is not None else hidden_size
            snake_case_					       =					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)      ->      Optional[Any]:

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







       @property
       def 		a_						( self)      ->      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 		a_						( self)      ->      int:
            snake_case_					       =					np.prod(self.upsampling_ratios)
            return math.ceil(self.sampling_rate / hop_length)







       @property
       def 		a_						( self)      ->      int:
            return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10))







'''simple docstring'''







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     =logging.get_logger(__name__)


A     =TypeVar('DatasetType', Dataset, IterableDataset)





def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[List[float]] = None    ,   _a	:							Optional[int] = None    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,   ):
    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(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (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(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            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(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )
    else:
        return _interleave_iterable_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )







def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							int = 0    ,   ):

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




