
import math
from typing import Optional

import numpy as np

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


_A   =			logging.get_logger(__name__)

_A   =			{
    "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 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							str									=		"encodec"





     def __init__(self				:   Any    ,   _A				:   Dict=[1.5, 3.0, 6.0, 12.0, 24.0]    ,   _A				:   Dict=2_4_0_0_0    ,   _A				:   int=1    ,   _A				:   Optional[int]=False    ,   _A				:   Tuple=None    ,   _A				:   Union[str, Any]=None    ,   _A				:   List[Any]=1_2_8    ,   _A				:   Union[str, Any]=3_2    ,   _A				:   List[str]=1    ,   _A				:   Dict=[8, 5, 4, 2]    ,   _A				:   Dict="weight_norm"    ,   _A				:   Dict=7    ,   _A				:   Optional[Any]=7    ,   _A				:   int=3    ,   _A				:   Optional[int]=2    ,   _A				:   Tuple=True    ,   _A				:   Optional[Any]="reflect"    ,   _A				:   Optional[Any]=2    ,   _A				:   Tuple=2    ,   _A				:   Any=1.0    ,   _A				:   Union[str, Any]=1_0_2_4    ,   _A				:   Optional[int]=None    ,   _A				:   Dict=True    ,   **_A				:   str    ,   )						->						List[Any]:
         snake_case								= target_bandwidths
         snake_case								= sampling_rate
         snake_case								= audio_channels
         snake_case								= normalize
         snake_case								= chunk_length_s
         snake_case								= overlap
         snake_case								= hidden_size
         snake_case								= num_filters
         snake_case								= num_residual_layers
         snake_case								= upsampling_ratios
         snake_case								= norm_type
         snake_case								= kernel_size
         snake_case								= last_kernel_size
         snake_case								= residual_kernel_size
         snake_case								= dilation_growth_rate
         snake_case								= use_causal_conv
         snake_case								= pad_mode
         snake_case								= compress
         snake_case								= num_lstm_layers
         snake_case								= trim_right_ratio
         snake_case								= codebook_size
         snake_case								= codebook_dim if codebook_dim is not None else hidden_size
         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__(**_A       )





     @property
     def 		UpperCAmelCase(self				:   List[Any]       )						->						Optional[int]:
         if self.chunk_length_s is None:
             return None
         else:
             return int(self.chunk_length_s * self.sampling_rate       )





     @property
     def 		UpperCAmelCase(self				:   List[str]       )						->						Optional[int]:
         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				:   Union[str, Any]       )						->						int:
         snake_case								= np.prod(self.upsampling_ratios       )
         return math.ceil(self.sampling_rate / hop_length       )





     @property
     def 		UpperCAmelCase(self				:   Any       )						->						int:
         return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)       )



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
_A   =			logging.get_logger(__name__)



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

    snake_case								= RobertaPreLayerNormConfig.from_pretrained(
        A__			,		architectures=["RobertaPreLayerNormForMaskedLM"]		)

    # convert state_dict
    snake_case								= torch.load(hf_hub_download(repo_id=A__			,		filename="pytorch_model.bin"		)		)
    snake_case								= {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("roberta."		):
            snake_case								= "roberta_prelayernorm." + tensor_key[len("roberta."		) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(".self.LayerNorm.weight"		) or tensor_key.endswith(".self.LayerNorm.bias"		):
            continue

        snake_case								= tensor_value

    snake_case								= RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=A__			,		config=A__			,		state_dict=A__		)
    model.save_pretrained(A__		)

    # convert tokenizer
    snake_case								= AutoTokenizer.from_pretrained(A__		)
    tokenizer.save_pretrained(A__		)


if __name__ == "__main__":
   _A   =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--checkpoint-repo",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   _A   =			parser.parse_args()
   convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

