import math
from typing import Optional

import numpy as np

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


__lowerCamelCase    :				int							     =	logging.get_logger(__name__)

__lowerCamelCase    :				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 __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"encodec"




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   target_bandwidths
     SCREAMING_SNAKE_CASE__    							=   sampling_rate
     SCREAMING_SNAKE_CASE__    							=   audio_channels
     SCREAMING_SNAKE_CASE__    							=   normalize
     SCREAMING_SNAKE_CASE__    							=   chunk_length_s
     SCREAMING_SNAKE_CASE__    							=   overlap
     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   num_filters
     SCREAMING_SNAKE_CASE__    							=   num_residual_layers
     SCREAMING_SNAKE_CASE__    							=   upsampling_ratios
     SCREAMING_SNAKE_CASE__    							=   norm_type
     SCREAMING_SNAKE_CASE__    							=   kernel_size
     SCREAMING_SNAKE_CASE__    							=   last_kernel_size
     SCREAMING_SNAKE_CASE__    							=   residual_kernel_size
     SCREAMING_SNAKE_CASE__    							=   dilation_growth_rate
     SCREAMING_SNAKE_CASE__    							=   use_causal_conv
     SCREAMING_SNAKE_CASE__    							=   pad_mode
     SCREAMING_SNAKE_CASE__    							=   compress
     SCREAMING_SNAKE_CASE__    							=   num_lstm_layers
     SCREAMING_SNAKE_CASE__    							=   trim_right_ratio
     SCREAMING_SNAKE_CASE__    							=   codebook_size
     SCREAMING_SNAKE_CASE__    							=   codebook_dim if codebook_dim is not None else hidden_size
     SCREAMING_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__(**_lowercase   )




   @property
   def 			__a		(					self    :       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    :       Union[str, Any]   ):



     """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    :       Any   ):



     """simple docstring"""






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




   @property
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






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

import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__lowerCamelCase    :				Union[str, Any]							     =	datasets.utils.logging.get_logger(__name__)







@dataclass
class __snake_case       (		datasets.BuilderConfig	):
   lowerCAmelCase_														=							1_00_00
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None







class __snake_case       (		datasets.ArrowBasedBuilder	):
   lowerCAmelCase_														=							ParquetConfig




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     return datasets.DatasetInfo(features=self.config.features   )




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     if not self.config.data_files:
       raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""   )
     SCREAMING_SNAKE_CASE__    							=   dl_manager.download_and_extract(self.config.data_files   )
     if isinstance(_lowercase ,			(str, list, tuple)   ):
       SCREAMING_SNAKE_CASE__    							=   data_files
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,			gen_kwargs={"""files""": files}   )]
     SCREAMING_SNAKE_CASE__    							=   []
     for split_name, files in data_files.items():
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       # Infer features is they are stoed in the arrow schema
       if self.info.features is None:
         for file in itertools.chain.from_iterable(_lowercase   ):
           with open(_lowercase ,			"""rb"""   ) as f:
             SCREAMING_SNAKE_CASE__    							=   datasets.Features.from_arrow_schema(pq.read_schema(_lowercase   )   )
           break
       splits.append(datasets.SplitGenerator(name=_lowercase ,			gen_kwargs={"""files""": files}   )   )
     return splits




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       pa.Table   ):



     """simple docstring"""






     if self.info.features is not None:
       # more expensive cast to support nested features with keys in a different order
       # allows str <-> int/float or str to Audio for example
       SCREAMING_SNAKE_CASE__    							=   table_cast(_lowercase ,			self.info.features.arrow_schema   )
     return pa_table




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.info.features.arrow_schema if self.info.features is not None else None
     if self.info.features is not None and self.config.columns is not None:
       if sorted(field.name for field in schema   ) != sorted(self.config.columns   ):
         raise ValueError(
             f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""   )
     for file_idx, file in enumerate(itertools.chain.from_iterable(_lowercase   )   ):
       with open(_lowercase ,			"""rb"""   ) as f:
         SCREAMING_SNAKE_CASE__    							=   pq.ParquetFile(_lowercase   )
         try:
           for batch_idx, record_batch in enumerate(
               parquet_file.iter_batches(batch_size=self.config.batch_size ,			columns=self.config.columns   )   ):
             SCREAMING_SNAKE_CASE__    							=   pa.Table.from_batches([record_batch]   )
             # Uncomment for debugging (will print the Arrow table size and elements)
             # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
             # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
             yield f"""{file_idx}_{batch_idx}""", self._cast_table(_lowercase   )
         except ValueError as e:
           logger.error(f"""Failed to read file '{file}' with error {type(_lowercase   )}: {e}"""   )
           raise
