





import math
from typing import Optional

import numpy as np

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


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

UpperCAmelCase						:   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 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[Any]					    =	'''encodec'''



				def __init__(			self     ,					_A=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]     ,					_A=24000     ,					_A=1     ,					_A=False     ,					_A=None     ,					_A=None     ,					_A=128     ,					_A=32     ,					_A=1     ,					_A=[8, 5, 4, 2]     ,					_A="weight_norm"     ,					_A=7     ,					_A=7     ,					_A=3     ,					_A=2     ,					_A=True     ,					_A="reflect"     ,					_A=2     ,					_A=2     ,					_A=1.0     ,					_A=1024     ,					_A=None     ,					_A=True     ,					**_A     ,					):
									__A	:						Any       =				target_bandwidths
									__A	:						List[Any]       =				sampling_rate
									__A	:						List[str]       =				audio_channels
									__A	:						int       =				normalize
									__A	:						int       =				chunk_length_s
									__A	:						str       =				overlap
									__A	:						Optional[Any]       =				hidden_size
									__A	:						Dict       =				num_filters
									__A	:						Dict       =				num_residual_layers
									__A	:						List[Any]       =				upsampling_ratios
									__A	:						Dict       =				norm_type
									__A	:						Dict       =				kernel_size
									__A	:						Optional[Any]       =				last_kernel_size
									__A	:						List[Any]       =				residual_kernel_size
									__A	:						List[Any]       =				dilation_growth_rate
									__A	:						Tuple       =				use_causal_conv
									__A	:						str       =				pad_mode
									__A	:						Optional[int]       =				compress
									__A	:						str       =				num_lstm_layers
									__A	:						Dict       =				trim_right_ratio
									__A	:						Tuple       =				codebook_size
									__A	:						Optional[Any]       =				codebook_dim if codebook_dim is not None else hidden_size
									__A	:						Tuple       =				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__(**_A     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									if self.chunk_length_s is None:
														return None
									else:
														return int(self.chunk_length_s * self.sampling_rate     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									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 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				np.prod(self.upsampling_ratios     )
									return math.ceil(self.sampling_rate / hop_length     )





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











import argparse
import json

from tqdm import tqdm







def  _SCREAMING_SNAKE_CASE				(   )      ->      List[Any]:
					__A	:						Tuple       =				argparse.ArgumentParser()

					# Required parameters
					parser.add_argument(
					    '--src_path'					,						type=a					,						default='biencoder-nq-dev.json'					,						help='Path to raw DPR training data'					,						)
					parser.add_argument(
					    '--evaluation_set'					,						type=a					,						help='where to store parsed evaluation_set file'					,						)
					parser.add_argument(
					    '--gold_data_path'					,						type=a					,						help='where to store parsed gold_data_path file'					,						)
					__A	:						Optional[int]       =				parser.parse_args()

					with open(args.src_path					,						'r'  ) as src_file, open(args.evaluation_set					,						'w'  ) as eval_file, open(
					    args.gold_data_path					,						'w'  ) as gold_file:
										__A	:						List[Any]       =				json.load(a  )
										for dpr_record in tqdm(a  ):
															__A	:						Dict       =				dpr_record['question']
															__A	:						Any       =				[context['title'] for context in dpr_record['positive_ctxs']]
															eval_file.write(question + '\n'  )
															gold_file.write('\t'.join(a  ) + '\n'  )


if __name__ == "__main__":
				main()




