'''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()

__SCREAMING_SNAKE_CASE       :     str     =  2

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				List[Any]						,					*,  # begin keyword-only arguments
          A						:				Union[str, Any]="<s>"						,					A						:				Dict="<pad>"						,					A						:				Any="</s>"						,					A						:				Tuple="<unk>"						,					A						:				List[Any]=None						,					):
             _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    bos, unk, pad, eos
             _UpperCAmelCase     :  int							      =    []
             _UpperCAmelCase     :  Dict							      =    []
             _UpperCAmelCase     :  Dict							      =    {}
             _UpperCAmelCase     :  Optional[Any]							      =    self.add_symbol(A       )
             _UpperCAmelCase     :  Dict							      =    self.add_symbol(A       )
             _UpperCAmelCase     :  int							      =    self.add_symbol(A       )
             _UpperCAmelCase     :  List[Any]							      =    self.add_symbol(A       )
             if extra_special_symbols:
                    for s in extra_special_symbols:
                           self.add_symbol(A       )
             _UpperCAmelCase     :  Tuple							      =    len(self.symbols       )




      def __eq__(       self						:				Optional[Any]						,					A						:				Tuple       ):
             return self.indices == other.indices




      def __getitem__(       self						:				int						,					A						:				Optional[Any]       ):
             if idx < len(self.symbols       ):
                    return self.symbols[idx]
             return self.unk_word




      def __len__(       self						:				Union[str, Any]       ):
             return len(self.symbols       )




      def __contains__(       self						:				List[Any]						,					A						:				Dict       ):
             return sym in self.indices




      @classmethod
      def      _A	(       cls						:				int						,					A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    cls()
             d.add_from_file(A       )
             return d




      def      _A	(       self						:				int						,					A						:				Tuple						,					A						:				Optional[Any]=1						,					A						:				str=False       ):
             if word in self.indices and not overwrite:
                    _UpperCAmelCase     :  Union[str, Any]							      =    self.indices[word]
                    _UpperCAmelCase     :  Tuple							      =    self.count[idx] + n
                    return idx
             else:
                    _UpperCAmelCase     :  List[Any]							      =    len(self.symbols       )
                    _UpperCAmelCase     :  int							      =    idx
                    self.symbols.append(A       )
                    self.count.append(A       )
                    return idx




      def      _A	(       self						:				int						,					A						:				List[Any]       ):
             return 0





      def      _A	(       self						:				Dict						,					A						:				Optional[int]       ):
             if isinstance(A						,					A       ):
                    try:
                           with open(A						,					"r"						,					encoding="utf-8"       ) as fd:
                                  self.add_from_file(A       )
                    except FileNotFoundError as fnfe:
                           raise fnfe
                    except UnicodeError:
                           raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(A       )       )
                    return

             _UpperCAmelCase     :  Union[str, Any]							      =    f.readlines()
             _UpperCAmelCase     :  Optional[Any]							      =    self._load_meta(A       )

             for line in lines[indices_start_line:]:
                    try:
                           _UpperCAmelCase      ,  _UpperCAmelCase     :  Any							      =    line.rstrip().rsplit(" "						,					1       )
                           if field == "#fairseq:overwrite":
                                  _UpperCAmelCase     :  Tuple							      =    True
                                  _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    line.rsplit(" "						,					1       )
                           else:
                                  _UpperCAmelCase     :  Optional[Any]							      =    False
                           _UpperCAmelCase     :  str							      =    int(A       )
                           _UpperCAmelCase     :  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(A       )       )
                           self.add_symbol(A						,					n=A						,					overwrite=A       )
                    except ValueError:
                           raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"       )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Tuple    )    ->    Any:


       """simple docstring"""







       _UpperCAmelCase     :  str							      =    dict((re.sub(R"@@$"			,	""			,	_UpperCAmelCase    ), v) if k.endswith("@@"    ) else (re.sub(R"$"			,	"</w>"			,	_UpperCAmelCase    ), v) for k, v in d.items()    )
       _UpperCAmelCase     :  str							      =    "<s> <pad> </s> <unk>".split()
       # restore the special tokens
       for k in keep_keys:
              del da[F"""{k}</w>"""]
              _UpperCAmelCase     :  Any							      =    d[k]  # restore
       return da

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      Optional[Any]    )    ->    Optional[int]:


       """simple docstring"""







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

       # handle various types of models

       _UpperCAmelCase     :  List[str]							      =    os.path.join(_UpperCAmelCase			,	"checkpoint.pt"    )
       if not os.path.isfile(_UpperCAmelCase    ):
              raise ValueError(F"""path to the file {checkpoint_file} does not exist!"""    )
       _UpperCAmelCase     :  List[str]							      =    torch.load(_UpperCAmelCase			,	map_location="cpu"    )

       _UpperCAmelCase     :  Any							      =    chkpt["cfg"]["model"]

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

       # merges_file (bpecodes)
       _UpperCAmelCase     :  Tuple							      =    os.path.join(_UpperCAmelCase			,	"bpecodes"    )
       if not os.path.isfile(_UpperCAmelCase    ):
              raise ValueError(F"""path to the file {bpecodes_file} does not exist!"""    )

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

       # model config
       _UpperCAmelCase     :  Any							      =    os.path.join(_UpperCAmelCase			,	"config.json"    )

       _UpperCAmelCase     :  Optional[int]							      =    {
           "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": 0.0_2,
           "intermediate_size": args["decoder_ffn_embed_dim"],
           "layer_norm_eps": 1e-12,
           "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(_UpperCAmelCase			,	"w"			,	encoding="utf-8"    ) as f:
              f.write(json.dumps(_UpperCAmelCase			,	ensure_ascii=_UpperCAmelCase			,	indent=_UpperCAmelCase    )    )

       # tokenizer config
       _UpperCAmelCase     :  Tuple							      =    os.path.join(_UpperCAmelCase			,	_UpperCAmelCase    )

       _UpperCAmelCase     :  List[Any]							      =    {
           "bos_token": "<s>",
           "eos_token": "</s>",
           "model_max_length": 1_024,
           "pad_token": "<pad>",
           "special_tokens_map_file": None,
           "tokenizer_class": "BioGptTokenizer",
           "unk_token": "<unk>",
       }

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

       # model
       _UpperCAmelCase     :  str							      =    chkpt["model"]

       # remove unneeded keys
       _UpperCAmelCase     :  Optional[Any]							      =    [
           "decoder.version",
       ]
       for k in ignore_keys:
              model_state_dict.pop(_UpperCAmelCase			,	_UpperCAmelCase    )

       _UpperCAmelCase     :  Any							      =    list(model_state_dict.keys()    )
       for layer_name in layer_names:
              if layer_name.endswith("output_projection.weight"    ):
                     _UpperCAmelCase     :  Optional[Any]							      =    model_state_dict.pop(_UpperCAmelCase    )
              else:
                     _UpperCAmelCase     :  Optional[Any]							      =    model_state_dict.pop(_UpperCAmelCase    )

       _UpperCAmelCase     :  Any							      =    BioGptConfig.from_pretrained(_UpperCAmelCase    )
       _UpperCAmelCase     :  str							      =    BioGptForCausalLM(_UpperCAmelCase    )

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

       # save
       _UpperCAmelCase     :  Tuple							      =    os.path.join(_UpperCAmelCase			,	_UpperCAmelCase    )
       print(F"""Generating {pytorch_weights_dump_path}"""    )
       torch.save(_UpperCAmelCase			,	_UpperCAmelCase    )

       print("Conversion is done!"    )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     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."""
  )
  __SCREAMING_SNAKE_CASE       :     List[str]     =  parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)


'''simple docstring'''




import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput

class 	lowerCamelCase_	(snake_case__							, snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						1




      @register_to_config
      def __init__(       self						:				Optional[int]						,					A						:				int = 1000						,					A						:				Optional[Union[np.ndarray, List[float]]] = None       ):
             # set `betas`, `alphas`, `timesteps`
             self.set_timesteps(A       )

             # standard deviation of the initial noise distribution
             _UpperCAmelCase     :  int							      =    1.0

             # For now we only support F-PNDM, i.e. the runge-kutta method
             # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
             # mainly at formula (9), (12), (13) and the Algorithm 2.
             _UpperCAmelCase     :  int							      =    4

             # running values
             _UpperCAmelCase     :  Dict							      =    []




      def      _A	(       self						:				Optional[int]						,					A						:				int						,					A						:				Union[str, torch.device] = None       ):
             _UpperCAmelCase     :  int							      =    num_inference_steps
             _UpperCAmelCase     :  Union[str, Any]							      =    torch.linspace(1						,					0						,					num_inference_steps + 1       )[:-1]
             _UpperCAmelCase     :  Any							      =    torch.cat([steps, torch.tensor([0.0]       )]       )

             if self.config.trained_betas is not None:
                    _UpperCAmelCase     :  str							      =    torch.tensor(self.config.trained_betas						,					dtype=torch.floataa       )
             else:
                    _UpperCAmelCase     :  Dict							      =    torch.sin(steps * math.pi / 2       ) ** 2

             _UpperCAmelCase     :  List[Any]							      =    (1.0 - self.betas**2) ** 0.5

             _UpperCAmelCase     :  List[str]							      =    (torch.atana(self.betas						,					self.alphas       ) / math.pi * 2)[:-1]
             _UpperCAmelCase     :  Dict							      =    timesteps.to(A       )

             _UpperCAmelCase     :  Dict							      =    []




      def      _A	(       self						:				Optional[int]						,					A						:				torch.FloatTensor						,					A						:				int						,					A						:				torch.FloatTensor						,					A						:				bool = True						,					):
             if self.num_inference_steps is None:
                    raise ValueError(
                        "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"       )

             _UpperCAmelCase     :  Tuple							      =    (self.timesteps == timestep).nonzero().item()
             _UpperCAmelCase     :  Optional[Any]							      =    timestep_index + 1

             _UpperCAmelCase     :  int							      =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
             self.ets.append(A       )

             if len(self.ets       ) == 1:
                    _UpperCAmelCase     :  List[Any]							      =    self.ets[-1]
             elif len(self.ets       ) == 2:
                    _UpperCAmelCase     :  str							      =    (3 * self.ets[-1] - self.ets[-2]) / 2
             elif len(self.ets       ) == 3:
                    _UpperCAmelCase     :  Tuple							      =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
             else:
                    _UpperCAmelCase     :  Union[str, Any]							      =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

             _UpperCAmelCase     :  Union[str, Any]							      =    self._get_prev_sample(A						,					A						,					A						,					A       )

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=A       )




      def      _A	(       self						:				Union[str, Any]						,					A						:				torch.FloatTensor						,					*A						:				Union[str, Any]						,					**A						:				Dict       ):
             return sample




      def      _A	(       self						:				Optional[Any]						,					A						:				Optional[int]						,					A						:				int						,					A						:				Optional[Any]						,					A						:				List[str]       ):
             _UpperCAmelCase     :  List[str]							      =    self.alphas[timestep_index]
             _UpperCAmelCase     :  List[Any]							      =    self.betas[timestep_index]

             _UpperCAmelCase     :  Optional[Any]							      =    self.alphas[prev_timestep_index]
             _UpperCAmelCase     :  Dict							      =    self.betas[prev_timestep_index]

             _UpperCAmelCase     :  Tuple							      =    (sample - sigma * ets) / max(A						,					1E-8       )
             _UpperCAmelCase     :  List[str]							      =    next_alpha * pred + ets * next_sigma

             return prev_sample





      def __len__(       self						:				Union[str, Any]       ):
             return self.config.num_train_timesteps

