






"""simple docstring"""



import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
_lowerCAmelCase	:	str				=      logging.get_logger("""transformers.models.encodec""")

_lowerCAmelCase	:	List[str]				=      {
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
_lowerCAmelCase	:	str				=      {
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
_lowerCAmelCase	:	str				=      {
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
_lowerCAmelCase	:	List[Any]				=      {
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
_lowerCAmelCase	:	Any				=      {
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
_lowerCAmelCase	:	Optional[Any]				=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_lowerCAmelCase	:	Optional[int]				=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_lowerCAmelCase	:	Union[str, Any]				=      []
_lowerCAmelCase	:	Optional[Any]				=      []


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]			,					snake_case       : Optional[int]			,					snake_case       : List[Any]			,					snake_case       : Optional[int]			,					snake_case       : List[str]				)->		Any:






     '''simple docstring'''



     for attribute in key.split("."				):
          UpperCAmelCase__						:    str							=							getattr(snake_case			,					snake_case				)

     if weight_type is not None:
          UpperCAmelCase__						:    Optional[int]							=							getattr(snake_case			,					snake_case				).shape
     else:
          UpperCAmelCase__						:    Optional[Any]							=							hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
              f' {value.shape} for {full_name}'				)

     if weight_type == "weight":
          UpperCAmelCase__						:    List[Any]							=							value
     elif weight_type == "weight_g":
          UpperCAmelCase__						:    List[str]							=							value
     elif weight_type == "weight_v":
          UpperCAmelCase__						:    str							=							value
     elif weight_type == "bias":
          UpperCAmelCase__						:    List[Any]							=							value
     elif weight_type == "running_mean":
          UpperCAmelCase__						:    Union[str, Any]							=							value
     elif weight_type == "running_var":
          UpperCAmelCase__						:    str							=							value
     elif weight_type == "num_batches_tracked":
          UpperCAmelCase__						:    Optional[int]							=							value
     elif weight_type == "weight_ih_l0":
          UpperCAmelCase__						:    List[str]							=							value
     elif weight_type == "weight_hh_l0":
          UpperCAmelCase__						:    Union[str, Any]							=							value
     elif weight_type == "bias_ih_l0":
          UpperCAmelCase__						:    Any							=							value
     elif weight_type == "bias_hh_l0":
          UpperCAmelCase__						:    Optional[Any]							=							value
     elif weight_type == "weight_ih_l1":
          UpperCAmelCase__						:    int							=							value
     elif weight_type == "weight_hh_l1":
          UpperCAmelCase__						:    Optional[int]							=							value
     elif weight_type == "bias_ih_l1":
          UpperCAmelCase__						:    Any							=							value
     elif weight_type == "bias_hh_l1":
          UpperCAmelCase__						:    Union[str, Any]							=							value
     else:
          UpperCAmelCase__						:    List[Any]							=							value

     logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'				)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : List[Any]				)->		List[Any]:






     '''simple docstring'''



     for key in ignore_keys:
          if key.endswith(".*"				):
               if name.startswith(key[:-1]				):
                    return True
          elif ".*." in key:
               UpperCAmelCase__							,				UpperCAmelCase__						:    Tuple							=							key.split(".*."				)
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : List[Any]			,					snake_case       : Dict				)->		Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    List[Any]							=							[]

     if model_name == "encodec_24khz" or "encodec_32khz":
          UpperCAmelCase__						:    Tuple							=							MAPPING_24K
     elif model_name == "encodec_48khz":
          UpperCAmelCase__						:    Dict							=							MAPPING_48K
     else:
          raise ValueError(f'Unsupported model: {model_name}'				)

     for name, value in orig_dict.items():
          if should_ignore(snake_case			,					snake_case				):
               logger.info(f'{name} was ignored'				)
               continue

          UpperCAmelCase__						:    int							=							False
          for key, mapped_key in MAPPING.items():
               if "*" in key:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    List[str]							=							key.split(".*."				)
                    if prefix in name and suffix in name:
                         UpperCAmelCase__						:    int							=							suffix

               if key in name:
                    # HACK otherwise .embed gets initialized with .embed_avg too
                    if key.endswith("embed"				) and name.endswith("embed_avg"				):
                         continue

                    UpperCAmelCase__						:    Union[str, Any]							=							True
                    if "*" in mapped_key:
                         UpperCAmelCase__						:    List[Any]							=							name.split(snake_case				)[0].split("."				)[-2]
                         UpperCAmelCase__						:    List[str]							=							mapped_key.replace("*"			,					snake_case				)
                    if "weight_g" in name:
                         UpperCAmelCase__						:    int							=							"weight_g"
                    elif "weight_v" in name:
                         UpperCAmelCase__						:    List[Any]							=							"weight_v"
                    elif "weight_ih_l0" in name:
                         UpperCAmelCase__						:    Union[str, Any]							=							"weight_ih_l0"
                    elif "weight_hh_l0" in name:
                         UpperCAmelCase__						:    str							=							"weight_hh_l0"
                    elif "bias_ih_l0" in name:
                         UpperCAmelCase__						:    Any							=							"bias_ih_l0"
                    elif "bias_hh_l0" in name:
                         UpperCAmelCase__						:    Union[str, Any]							=							"bias_hh_l0"
                    elif "weight_ih_l1" in name:
                         UpperCAmelCase__						:    Tuple							=							"weight_ih_l1"
                    elif "weight_hh_l1" in name:
                         UpperCAmelCase__						:    List[Any]							=							"weight_hh_l1"
                    elif "bias_ih_l1" in name:
                         UpperCAmelCase__						:    List[str]							=							"bias_ih_l1"
                    elif "bias_hh_l1" in name:
                         UpperCAmelCase__						:    Optional[int]							=							"bias_hh_l1"
                    elif "bias" in name:
                         UpperCAmelCase__						:    Optional[Any]							=							"bias"
                    elif "weight" in name:
                         UpperCAmelCase__						:    Optional[int]							=							"weight"
                    elif "running_mean" in name:
                         UpperCAmelCase__						:    List[str]							=							"running_mean"
                    elif "running_var" in name:
                         UpperCAmelCase__						:    Dict							=							"running_var"
                    elif "num_batches_tracked" in name:
                         UpperCAmelCase__						:    Tuple							=							"num_batches_tracked"
                    else:
                         UpperCAmelCase__						:    Optional[int]							=							None
                    set_recursively(snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
               continue
          if not is_used:
               unused_weights.append(snake_case				)

     logger.warning(f'Unused weights: {unused_weights}'				)


@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Dict			,					snake_case       : Union[str, Any]			,					snake_case       : int			,					snake_case       : List[str]=None			,					snake_case       : Optional[int]=None			,					)->		Dict:






     '''simple docstring'''



     if config_path is not None:
          UpperCAmelCase__						:    List[Any]							=							EncodecConfig.from_pretrained(snake_case				)
     else:
          UpperCAmelCase__						:    Tuple							=							EncodecConfig()

     if model_name == "encodec_24khz":
          pass  # config is already correct
     elif model_name == "encodec_32khz":
          UpperCAmelCase__						:    int							=							[8, 5, 4, 4]
          UpperCAmelCase__						:    int							=							[2.2]
          UpperCAmelCase__						:    str							=							64
          UpperCAmelCase__						:    Tuple							=							3_2000
          UpperCAmelCase__						:    str							=							2048
          UpperCAmelCase__						:    Optional[Any]							=							False
          UpperCAmelCase__						:    Optional[Any]							=							False
          UpperCAmelCase__						:    Dict							=							False
     elif model_name == "encodec_48khz":
          UpperCAmelCase__						:    int							=							[8, 5, 4, 2]
          UpperCAmelCase__						:    str							=							[3.0, 6.0, 12.0, 24.0]
          UpperCAmelCase__						:    Union[str, Any]							=							4_8000
          UpperCAmelCase__						:    Tuple							=							2
          UpperCAmelCase__						:    Any							=							False
          UpperCAmelCase__						:    Any							=							"time_group_norm"
          UpperCAmelCase__						:    str							=							True
          UpperCAmelCase__						:    Tuple							=							1.0
          UpperCAmelCase__						:    str							=							0.01
     else:
          raise ValueError(f'Unknown model name: {model_name}'				)

     UpperCAmelCase__						:    List[Any]							=							EncodecModel(snake_case				)

     UpperCAmelCase__						:    str							=							EncodecFeatureExtractor(
         feature_size=config.audio_channels			,					sampling_rate=config.sampling_rate			,					chunk_length_s=config.chunk_length_s			,					overlap=config.overlap			,					)
     feature_extractor.save_pretrained(snake_case				)

     UpperCAmelCase__						:    Optional[Any]							=							torch.load(snake_case				)
     if "best_state" in original_checkpoint:
          # we might have a training state saved, in which case discard the yaml results and just retain the weights
          UpperCAmelCase__						:    Optional[Any]							=							original_checkpoint["best_state"]
     recursively_load_weights(snake_case			,					snake_case			,					snake_case				)
     model.save_pretrained(snake_case				)

     if repo_id:
          print("Pushing to the hub..."				)
          feature_extractor.push_to_hub(snake_case				)
          model.push_to_hub(snake_case				)


if __name__ == "__main__":
 _lowerCAmelCase	:	Any				=      argparse.ArgumentParser()
 parser.add_argument(
     """--model""",
     default="""encodec_24khz""",
     type=str,
     help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
 )
 parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 parser.add_argument(
     """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
 )
 parser.add_argument(
     """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
 )

 _lowerCAmelCase	:	Tuple				=      parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )










"""simple docstring"""



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowerCAmelCase	:	Optional[int]				=      2




class 			lowerCAmelCase__			:
   def __init__(		self      :	Any							,			*,  # begin keyword-only arguments
       snake_case__      :	List[str]="<s>"							,			snake_case__      :	str="<pad>"							,			snake_case__      :	List[str]="</s>"							,			snake_case__      :	Any="<unk>"							,			snake_case__      :	List[Any]=None							,			):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							bos, unk, pad, eos
        UpperCAmelCase__						:    str							=							[]
        UpperCAmelCase__						:    Tuple							=							[]
        UpperCAmelCase__						:    Optional[int]							=							{}
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    str							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        if extra_special_symbols:
             for s in extra_special_symbols:
                  self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    List[str]							=							len(self.symbols							)
   def __eq__(		self      :	List[Any]							,			snake_case__      :	str							):


        '''simple docstring'''
        return self.indices == other.indices
   def __getitem__(		self      :	Dict							,			snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        if idx < len(self.symbols							):
             return self.symbols[idx]
        return self.unk_word
   def __len__(		self      :	int							):


        '''simple docstring'''
        return len(self.symbols							)
   def __contains__(		self      :	Dict							,			snake_case__      :	Optional[Any]							):


        '''simple docstring'''
        return sym in self.indices
   @classmethod
   def       __a							(		cls      :	Optional[int]							,			snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							cls()
        d.add_from_file(snake_case__							)
        return d
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	str							,			snake_case__      :	Dict=1							,			snake_case__      :	Dict=False							):


        '''simple docstring'''
        if word in self.indices and not overwrite:
             UpperCAmelCase__						:    List[Any]							=							self.indices[word]
             UpperCAmelCase__						:    Dict							=							self.count[idx] + n
             return idx
        else:
             UpperCAmelCase__						:    Optional[Any]							=							len(self.symbols							)
             UpperCAmelCase__						:    List[str]							=							idx
             self.symbols.append(snake_case__							)
             self.count.append(snake_case__							)
             return idx
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        return 0




   def       __a							(		self      :	Any							,			snake_case__      :	Dict							):


        '''simple docstring'''
        if isinstance(snake_case__							,			snake_case__							):
             try:
                  with open(snake_case__							,			"r"							,			encoding="utf-8"							) as fd:
                       self.add_from_file(snake_case__							)
             except FileNotFoundError as fnfe:
                  raise fnfe
             except UnicodeError:
                  raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(snake_case__							)							)
             return

        UpperCAmelCase__						:    Optional[int]							=							f.readlines()
        UpperCAmelCase__						:    Dict							=							self._load_meta(snake_case__							)

        for line in lines[indices_start_line:]:
             try:
                  UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							line.rstrip().rsplit(" "							,			1							)
                  if field == "#fairseq:overwrite":
                       UpperCAmelCase__						:    Union[str, Any]							=							True
                       UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							line.rsplit(" "							,			1							)
                  else:
                       UpperCAmelCase__						:    Tuple							=							False
                  UpperCAmelCase__						:    Any							=							int(snake_case__							)
                  UpperCAmelCase__						:    List[Any]							=							line
                  if word in self and not overwrite:
                       raise RuntimeError(
                           "Duplicate word found when loading Dictionary: '{}'. "
                           "Duplicate words can overwrite earlier ones by adding the "
                           "#fairseq:overwrite flag at the end of the corresponding row "
                           "in the dictionary file. If using the Camembert model, please "
                           "download an updated copy of the model file.".format(snake_case__							)							)
                  self.add_symbol(snake_case__							,			n=snake_case__							,			overwrite=snake_case__							)
             except ValueError:
                  raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"							)




def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							dict((re.sub(r"@@$"			,					""			,					snake_case				), v) if k.endswith("@@"				) else (re.sub(r"$"			,					"</w>"			,					snake_case				), v) for k, v in d.items()				)
     UpperCAmelCase__						:    Optional[int]							=							"<s> <pad> </s> <unk>".split()
     # restore the special tokens
     for k in keep_keys:
          del da[f'{k}</w>']
          UpperCAmelCase__						:    List[str]							=							d[k]  # restore
     return da


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : int				)->		Union[str, Any]:






     '''simple docstring'''



     if not os.path.exists(snake_case				):
          raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'				)
     os.makedirs(snake_case			,					exist_ok=snake_case				)
     print(f'Writing results to {pytorch_dump_folder_path}'				)

     # handle various types of models

     UpperCAmelCase__						:    Dict							=							os.path.join(snake_case			,					"checkpoint.pt"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {checkpoint_file} does not exist!'				)
     UpperCAmelCase__						:    Optional[int]							=							torch.load(snake_case			,					map_location="cpu"				)

     UpperCAmelCase__						:    Optional[int]							=							chkpt["cfg"]["model"]

     # dicts
     UpperCAmelCase__						:    Optional[int]							=							os.path.join(snake_case			,					"dict.txt"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {dict_file} does not exist!'				)
     UpperCAmelCase__						:    Dict							=							Dictionary.load(snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							rewrite_dict_keys(src_dict.indices				)
     UpperCAmelCase__						:    List[str]							=							len(snake_case				)
     UpperCAmelCase__						:    Tuple							=							os.path.join(snake_case			,					VOCAB_FILES_NAMES["vocab_file"]				)
     print(f'Generating {src_vocab_file} of {src_vocab_size} records'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # merges_file (bpecodes)
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					"bpecodes"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {bpecodes_file} does not exist!'				)

     UpperCAmelCase__						:    int							=							os.path.join(snake_case			,					VOCAB_FILES_NAMES["merges_file"]				)
     shutil.copyfile(snake_case			,					snake_case				)

     # model config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					"config.json"				)

     UpperCAmelCase__						:    Any							=							{
         "activation_dropout": args["activation_dropout"],
         "architectures": ["BioGptForCausalLM"],
         "attention_probs_dropout_prob": args["attention_dropout"],
         "bos_token_id": 0,
         "eos_token_id": 2,
         "hidden_act": args["activation_fn"],
         "hidden_dropout_prob": args["dropout"],
         "hidden_size": args["decoder_embed_dim"],
         "initializer_range": 0.02,
         "intermediate_size": args["decoder_ffn_embed_dim"],
         "layer_norm_eps": 1E-1_2,
         "layerdrop": args["decoder_layerdrop"],
         "max_position_embeddings": args["max_target_positions"],
         "model_type": "biogpt",
         "num_attention_heads": args["decoder_attention_heads"],
         "num_hidden_layers": args["decoder_layers"],
         "pad_token_id": 1,
         "scale_embedding": not args["no_scale_embedding"],
         "tie_word_embeddings": args["share_decoder_input_output_embed"],
         "vocab_size": src_vocab_size,
     }

     # good hparam defaults to start with

     print(f'Generating {biogpt_model_config_file}'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # tokenizer config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					snake_case				)

     UpperCAmelCase__						:    str							=							{
         "bos_token": "<s>",
         "eos_token": "</s>",
         "model_max_length": 1024,
         "pad_token": "<pad>",
         "special_tokens_map_file": None,
         "tokenizer_class": "BioGptTokenizer",
         "unk_token": "<unk>",
     }

     print(f'Generating {biogpt_tokenizer_config_file}'				)
     with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # model
     UpperCAmelCase__						:    Union[str, Any]							=							chkpt["model"]

     # remove unneeded keys
     UpperCAmelCase__						:    Union[str, Any]							=							[
         "decoder.version",
     ]
     for k in ignore_keys:
          model_state_dict.pop(snake_case			,					snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							list(model_state_dict.keys()				)
     for layer_name in layer_names:
          if layer_name.endswith("output_projection.weight"				):
               UpperCAmelCase__						:    int							=							model_state_dict.pop(snake_case				)
          else:
               UpperCAmelCase__						:    Tuple							=							model_state_dict.pop(snake_case				)

     UpperCAmelCase__						:    Any							=							BioGptConfig.from_pretrained(snake_case				)
     UpperCAmelCase__						:    List[Any]							=							BioGptForCausalLM(snake_case				)

     # check that it loads ok
     model_new.load_state_dict(snake_case				)

     # save
     UpperCAmelCase__						:    Union[str, Any]							=							os.path.join(snake_case			,					snake_case				)
     print(f'Generating {pytorch_weights_dump_path}'				)
     torch.save(snake_case			,					snake_case				)

     print("Conversion is done!"				)


if __name__ == "__main__":
 _lowerCAmelCase	:	List[Any]				=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--biogpt_checkpoint_path""",
     default=None,
     type=str,
     required=True,
     help=(
         """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
         """ bpecodes, etc."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 _lowerCAmelCase	:	List[str]				=      parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)


