
"""simple docstring"""







import math
from typing import Optional

import numpy as np

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


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """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__ (			_lowerCamelCase):
		A_		: Optional[Any]            =			'encodec'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=[1.5, 3.0, 6.0, 12.0, 24.0] ,	_SCREAMING_SNAKE_CASE=2_40_00 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=1_28 ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=[8, 5, 4, 2] ,	_SCREAMING_SNAKE_CASE="weight_norm" ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE="reflect" ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=1.0 ,	_SCREAMING_SNAKE_CASE=10_24 ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=True ,	**_SCREAMING_SNAKE_CASE ,	):
			__lowerCAmelCase    :		Tuple		    =   target_bandwidths
			__lowerCAmelCase    :		str		    =   sampling_rate
			__lowerCAmelCase    :		Optional[Any]		    =   audio_channels
			__lowerCAmelCase    :		List[str]		    =   normalize
			__lowerCAmelCase    :		Tuple		    =   chunk_length_s
			__lowerCAmelCase    :		Optional[Any]		    =   overlap
			__lowerCAmelCase    :		List[str]		    =   hidden_size
			__lowerCAmelCase    :		int		    =   num_filters
			__lowerCAmelCase    :		Tuple		    =   num_residual_layers
			__lowerCAmelCase    :		List[str]		    =   upsampling_ratios
			__lowerCAmelCase    :		Dict		    =   norm_type
			__lowerCAmelCase    :		Tuple		    =   kernel_size
			__lowerCAmelCase    :		List[str]		    =   last_kernel_size
			__lowerCAmelCase    :		List[str]		    =   residual_kernel_size
			__lowerCAmelCase    :		Optional[Any]		    =   dilation_growth_rate
			__lowerCAmelCase    :		Tuple		    =   use_causal_conv
			__lowerCAmelCase    :		Union[str, Any]		    =   pad_mode
			__lowerCAmelCase    :		Tuple		    =   compress
			__lowerCAmelCase    :		Optional[int]		    =   num_lstm_layers
			__lowerCAmelCase    :		List[str]		    =   trim_right_ratio
			__lowerCAmelCase    :		Any		    =   codebook_size
			__lowerCAmelCase    :		Optional[Any]		    =   codebook_dim if codebook_dim is not None else hidden_size
			__lowerCAmelCase    :		int		    =   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__(**_SCREAMING_SNAKE_CASE				)

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

		@property
		def 							__lowerCamelCase							(   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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   np.prod(self.upsampling_ratios				)
			return math.ceil(self.sampling_rate / hop_length				)







		@property
		def 							__lowerCamelCase							(   self				):
			return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)

"""simple docstring"""







import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCamelCase__           =	data_utils.TransfoXLTokenizer
lowerCamelCase__           =	data_utils.TransfoXLCorpus
lowerCamelCase__           =	data_utils
lowerCamelCase__           =	data_utils
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	if transfo_xl_dataset_file:
		# Convert a pre-processed corpus (see original TensorFlow repo)
		with open(_UpperCamelCase ,						'rb'	) as fp:
			__lowerCAmelCase    :		int		    =   pickle.load(_UpperCamelCase ,						encoding='latin1'	)
		# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
		__lowerCAmelCase    :		Any		    =   pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
		print(F"Save vocabulary to {pytorch_vocab_dump_path}"	)
		__lowerCAmelCase    :		Optional[Any]		    =   corpus.vocab.__dict__
		torch.save(_UpperCamelCase ,						_UpperCamelCase	)

		__lowerCAmelCase    :		Dict		    =   corpus.__dict__
		corpus_dict_no_vocab.pop('vocab' ,						_UpperCamelCase	)
		__lowerCAmelCase    :		List[str]		    =   pytorch_dump_folder_path + '/' + CORPUS_NAME
		print(F"Save dataset to {pytorch_dataset_dump_path}"	)
		torch.save(_UpperCamelCase ,						_UpperCamelCase	)

	if tf_checkpoint_path:
		# Convert a pre-trained TensorFlow model
		__lowerCAmelCase    :		Tuple		    =   os.path.abspath(_UpperCamelCase	)
		__lowerCAmelCase    :		List[Any]		    =   os.path.abspath(_UpperCamelCase	)

		print(F"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."	)
		# Initialise PyTorch model
		if transfo_xl_config_file == "":
			__lowerCAmelCase    :		Any		    =   TransfoXLConfig()
		else:
			__lowerCAmelCase    :		str		    =   TransfoXLConfig.from_json_file(_UpperCamelCase	)
		print(F"Building PyTorch model from configuration: {config}"	)
		__lowerCAmelCase    :		Optional[Any]		    =   TransfoXLLMHeadModel(_UpperCamelCase	)

		__lowerCAmelCase    :		Dict		    =   load_tf_weights_in_transfo_xl(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
		# Save pytorch-model
		__lowerCAmelCase    :		str		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
		__lowerCAmelCase    :		Tuple		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
		print(F"Save PyTorch model to {os.path.abspath(_UpperCamelCase	)}"	)
		torch.save(model.state_dict() ,						_UpperCamelCase	)
		print(F"Save configuration file to {os.path.abspath(_UpperCamelCase	)}"	)
		with open(_UpperCamelCase ,						'w' ,						encoding='utf-8'	) as f:
			f.write(config.to_json_string()	)


if __name__ == "__main__":
							lowerCamelCase__           =	argparse.ArgumentParser()
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
							)
							parser.add_argument(
							    """--tf_checkpoint_path""",
							    default="""""",
							    type=str,
							    help="""An optional path to a TensorFlow checkpoint path to be converted.""",
							)
							parser.add_argument(
							    """--transfo_xl_config_file""",
							    default="""""",
							    type=str,
							    help=(
							        """An optional config json file corresponding to the pre-trained BERT model. \n"""
							        """This specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--transfo_xl_dataset_file""",
							    default="""""",
							    type=str,
							    help="""An optional dataset file to be converted in a vocabulary.""",
							)
							lowerCamelCase__           =	parser.parse_args()
							convert_transfo_xl_checkpoint_to_pytorch(
							    args.tf_checkpoint_path,
							    args.transfo_xl_config_file,
							    args.pytorch_dump_folder_path,
							    args.transfo_xl_dataset_file,
							)