




import math
from typing import Optional

import numpy as np

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


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[Any]          =  {
    "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   (      UpperCamelCase__      ):
						UpperCamelCase      =       """encodec"""

						def __init__(		self :str     ,			__snake_case :Any=[1.5, 3.0, 6.0, 12.0, 24.0]     ,			__snake_case :Dict=2_40_00     ,			__snake_case :Optional[int]=1     ,			__snake_case :str=False     ,			__snake_case :Optional[int]=None     ,			__snake_case :int=None     ,			__snake_case :Any=1_28     ,			__snake_case :Dict=32     ,			__snake_case :Optional[int]=1     ,			__snake_case :Union[str, Any]=[8, 5, 4, 2]     ,			__snake_case :Optional[int]="weight_norm"     ,			__snake_case :Dict=7     ,			__snake_case :str=7     ,			__snake_case :int=3     ,			__snake_case :Union[str, Any]=2     ,			__snake_case :Optional[int]=True     ,			__snake_case :Any="reflect"     ,			__snake_case :List[Any]=2     ,			__snake_case :Any=2     ,			__snake_case :Tuple=1.0     ,			__snake_case :int=10_24     ,			__snake_case :Optional[int]=None     ,			__snake_case :str=True     ,			**__snake_case :Optional[int]     ,			):
										'''simple docstring'''

										__magic_name__						:						Any										=target_bandwidths
										__magic_name__						:						Optional[Any]										=sampling_rate
										__magic_name__						:						Any										=audio_channels
										__magic_name__						:						List[Any]										=normalize
										__magic_name__						:						List[str]										=chunk_length_s
										__magic_name__						:						Optional[int]										=overlap
										__magic_name__						:						Any										=hidden_size
										__magic_name__						:						List[Any]										=num_filters
										__magic_name__						:						List[str]										=num_residual_layers
										__magic_name__						:						Optional[int]										=upsampling_ratios
										__magic_name__						:						str										=norm_type
										__magic_name__						:						Optional[Any]										=kernel_size
										__magic_name__						:						List[str]										=last_kernel_size
										__magic_name__						:						List[str]										=residual_kernel_size
										__magic_name__						:						str										=dilation_growth_rate
										__magic_name__						:						Optional[Any]										=use_causal_conv
										__magic_name__						:						int										=pad_mode
										__magic_name__						:						Optional[int]										=compress
										__magic_name__						:						Any										=num_lstm_layers
										__magic_name__						:						Optional[Any]										=trim_right_ratio
										__magic_name__						:						Any										=codebook_size
										__magic_name__						:						Tuple										=codebook_dim if codebook_dim is not None else hidden_size
										__magic_name__						:						List[str]										=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__(**__snake_case			)

						@property
						def      A__    (		self :List[str]			):
										'''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 :List[str]			):
										'''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 :int			):
										'''simple docstring'''

										__magic_name__						:						Any										=np.prod(self.upsampling_ratios			)
										return math.ceil(self.sampling_rate / hop_length			)


						@property
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)			)










from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType




@dataclass
class 		__A   :
						UpperCamelCase      =       42
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""Translation"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def __call__(		self :Union[str, Any]			):
										'''simple docstring'''

										return pa.struct({lang: pa.string() for lang in sorted(self.languages			)}			)


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Value

										return {k: Value("""string"""			) for k in sorted(self.languages			)}




@dataclass
class 		__A   :
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""TranslationVariableLanguages"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=sorted(set(self.languages			)			) if self.languages else None
										__magic_name__						:						Optional[int]										=len(self.languages			) if self.languages else None

						def __call__(		self :List[str]			):
										'''simple docstring'''

										return pa.struct({"""language""": pa.list_(pa.string()			), """translation""": pa.list_(pa.string()			)}			)

						def      A__    (		self :str     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=set(self.languages			)
										if self.languages and set(__snake_case			) - lang_set:
														raise ValueError(
														    f"Some languages in example ({', '.join(sorted(set(__snake_case			) - lang_set			)			)}) are not in valid set ({', '.join(__snake_case			)})."			)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										__magic_name__						:						Any										=[]
										for lang, text in translation_dict.items():
														if isinstance(__snake_case     ,			__snake_case			):
																		translation_tuples.append((lang, text)			)
														else:
																		translation_tuples.extend([(lang, el) for el in text]			)

        # Ensure translations are in ascending order by language code.
										__magic_name__					,			__magic_name__						:						List[str]										=zip(*sorted(__snake_case			)			)

										return {"language": languages, "translation": translations}


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Sequence, Value

										return {
										    "language": Sequence(Value("""string"""			)			),
										    "translation": Sequence(Value("""string"""			)			),
										}




