


'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






						return DownloadCommand(args.model   ,     args.cache_dir   ,     args.force   ,     args.trust_remote_code  )






class  UpperCamelCase__(     lowerCAmelCase ):




				@staticmethod
				def   a__(	lowerCAmelCase    :				ArgumentParser   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			parser.add_parser('''download'''   )
										download_parser.add_argument(
										    '''--cache-dir'''   ,					type=lowerCAmelCase   ,					default=lowerCAmelCase   ,					help='''Path to location to store the models'''   )
										download_parser.add_argument(
										    '''--force'''   ,					action='''store_true'''   ,					help='''Force the model to be download even if already in cache-dir'''   )
										download_parser.add_argument(
										    '''--trust-remote-code'''   ,					action='''store_true'''   ,					help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''   ,					)
										download_parser.add_argument('''model'''   ,					type=lowerCAmelCase   ,					help='''Name of the model to download'''   )
										download_parser.set_defaults(func=lowerCAmelCase   )




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				bool   ,					lowerCAmelCase    :				bool   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			model
										UpperCAmelCase         =			cache
										UpperCAmelCase         =			force
										UpperCAmelCase         =			trust_remote_code





				def   a__(	self    :				int   )->  Optional[Any]:

										"""simple docstring"""




										from ..models.auto import AutoModel, AutoTokenizer

										AutoModel.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )
										AutoTokenizer.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )









'''simple docstring'''



import math
from typing import Optional

import numpy as np

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


_lowercase    :    Union[str, Any]			     =  logging.get_logger(__name__)

_lowercase    :    List[str]			     =  {
    """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__(     lowerCAmelCase ):
				__magic_name__       :   List[str]							= "encodec"




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				int=[1.5, 3.0, 6.0, 12.0, 24.0]   ,					lowerCAmelCase    :				Tuple=24000   ,					lowerCAmelCase    :				List[Any]=1   ,					lowerCAmelCase    :				Union[str, Any]=False   ,					lowerCAmelCase    :				str=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=128   ,					lowerCAmelCase    :				Any=32   ,					lowerCAmelCase    :				Any=1   ,					lowerCAmelCase    :				List[Any]=[8, 5, 4, 2]   ,					lowerCAmelCase    :				Union[str, Any]="weight_norm"   ,					lowerCAmelCase    :				str=7   ,					lowerCAmelCase    :				Optional[int]=7   ,					lowerCAmelCase    :				Any=3   ,					lowerCAmelCase    :				Tuple=2   ,					lowerCAmelCase    :				Optional[Any]=True   ,					lowerCAmelCase    :				List[str]="reflect"   ,					lowerCAmelCase    :				Optional[int]=2   ,					lowerCAmelCase    :				List[Any]=2   ,					lowerCAmelCase    :				Union[str, Any]=1.0   ,					lowerCAmelCase    :				Optional[Any]=1024   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				str=True   ,					**lowerCAmelCase    :				str   ,					)->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			target_bandwidths
										UpperCAmelCase         =			sampling_rate
										UpperCAmelCase         =			audio_channels
										UpperCAmelCase         =			normalize
										UpperCAmelCase         =			chunk_length_s
										UpperCAmelCase         =			overlap
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_filters
										UpperCAmelCase         =			num_residual_layers
										UpperCAmelCase         =			upsampling_ratios
										UpperCAmelCase         =			norm_type
										UpperCAmelCase         =			kernel_size
										UpperCAmelCase         =			last_kernel_size
										UpperCAmelCase         =			residual_kernel_size
										UpperCAmelCase         =			dilation_growth_rate
										UpperCAmelCase         =			use_causal_conv
										UpperCAmelCase         =			pad_mode
										UpperCAmelCase         =			compress
										UpperCAmelCase         =			num_lstm_layers
										UpperCAmelCase         =			trim_right_ratio
										UpperCAmelCase         =			codebook_size
										UpperCAmelCase         =			codebook_dim if codebook_dim is not None else hidden_size
										UpperCAmelCase         =			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    :				str   )->  Optional[int]:

										"""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]   )->  Optional[int]:

										"""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    :				List[Any]   )->  int:

										"""simple docstring"""




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





				@property
				def   a__(	self    :				int   )->  int:

										"""simple docstring"""




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





