


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()

_snake_case	:						Optional[int]         =							2

class 							a						:




							"""simple docstring"""


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

							def __eq__(				self			:    Any       ,      lowerCamelCase			:    Optional[Any]							)      ->       int:
														return self.indices == other.indices

							def __getitem__(				self			:    Union[str, Any]       ,      lowerCamelCase			:    Dict							)      ->       Any:
														if idx < len(self.symbols							):
																					return self.symbols[idx]
														return self.unk_word

							def __len__(				self			:    int							)      ->       Optional[int]:
														return len(self.symbols							)

							def __contains__(				self			:    str       ,      lowerCamelCase			:    str							)      ->       Any:
														return sym in self.indices

							@classmethod
							def        __snake_case   (				cls			:    Union[str, Any]       ,      lowerCamelCase			:    Optional[Any]							)      ->       List[str]:
														__snake_case						:     Optional[Any]									=  cls()
														d.add_from_file(lowerCamelCase							)
														return d

							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    List[str]=1       ,      lowerCamelCase			:    int=False							)      ->       Union[str, Any]:
														if word in self.indices and not overwrite:
																					__snake_case						:     Optional[int]									=  self.indices[word]
																					__snake_case						:     Optional[Any]									=  self.count[idx] + n
																					return idx
														else:
																					__snake_case						:     Dict									=  len(self.symbols							)
																					__snake_case						:     List[Any]									=  idx
																					self.symbols.append(lowerCamelCase							)
																					self.count.append(lowerCamelCase							)
																					return idx

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    Tuple							)      ->       Any:
														return 0






							def        __snake_case   (				self			:    str       ,      lowerCamelCase			:    Tuple							)      ->       List[str]:
														if isinstance(lowerCamelCase       ,      lowerCamelCase							):
																					try:
																												with open(lowerCamelCase       ,      "r"       ,      encoding="utf-8"							) as fd:
																																			self.add_from_file(lowerCamelCase							)
																					except FileNotFoundError as fnfe:
																												raise fnfe
																					except UnicodeError:
																												raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(lowerCamelCase							)							)
																					return

														__snake_case						:     Dict									=  f.readlines()
														__snake_case						:     Dict									=  self._load_meta(lowerCamelCase							)

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







def 							lowerCAmelCase_	(   __lowerCamelCase      ):
							# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
							# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
							__snake_case						:     List[Any]									=  dict((re.sub(R"@@$"				,      ""				,      __lowerCamelCase      ), v) if k.endswith("@@"      ) else (re.sub(R"$"				,      "</w>"				,      __lowerCamelCase      ), v) for k, v in d.items()      )
							__snake_case						:     str									=  "<s> <pad> </s> <unk>".split()
							# restore the special tokens
							for k in keep_keys:
														del da[F'{k}</w>']
														__snake_case						:     Optional[int]									=  d[k]  # restore
							return da







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase      ):
							# prep
							if not os.path.exists(__lowerCamelCase      ):
														raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'      )
							os.makedirs(__lowerCamelCase				,      exist_ok=__lowerCamelCase      )
							print(F'Writing results to {pytorch_dump_folder_path}'      )

							# handle various types of models

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

							__snake_case						:     List[Any]									=  chkpt["cfg"]["model"]

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

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

							__snake_case						:     List[Any]									=  os.path.join(__lowerCamelCase				,      VOCAB_FILES_NAMES["merges_file"]      )
							shutil.copyfile(__lowerCamelCase				,      __lowerCamelCase      )

							# model config
							__snake_case						:     List[str]									=  os.path.join(__lowerCamelCase				,      "config.json"      )

							__snake_case						:     Optional[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": 0.0_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(__lowerCamelCase				,      "w"				,      encoding="utf-8"      ) as f:
														f.write(json.dumps(__lowerCamelCase				,      ensure_ascii=__lowerCamelCase				,      indent=__lowerCamelCase      )      )

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

							__snake_case						:     int									=  {
							    "bos_token": "<s>",
							    "eos_token": "</s>",
							    "model_max_length": 1_0_2_4,
							    "pad_token": "<pad>",
							    "special_tokens_map_file": None,
							    "tokenizer_class": "BioGptTokenizer",
							    "unk_token": "<unk>",
							}

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

							# model
							__snake_case						:     Dict									=  chkpt["model"]

							# remove unneeded keys
							__snake_case						:     int									=  [
							    "decoder.version",
							]
							for k in ignore_keys:
														model_state_dict.pop(__lowerCamelCase				,      __lowerCamelCase      )

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

							__snake_case						:     Dict									=  BioGptConfig.from_pretrained(__lowerCamelCase      )
							__snake_case						:     Any									=  BioGptForCausalLM(__lowerCamelCase      )

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

							# save
							__snake_case						:     List[str]									=  os.path.join(__lowerCamelCase				,      __lowerCamelCase      )
							print(F'Generating {pytorch_weights_dump_path}'      )
							torch.save(__lowerCamelCase				,      __lowerCamelCase      )

							print("Conversion is done!"      )


if __name__ == "__main__":
			_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."
			)
			_snake_case	:						Union[str, Any]         =							parser.parse_args()
			convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)




from collections.abc import Callable

import numpy as np





def 	__a     (      A__			:  Callable			,  A__			:  float			,  A__			:  float			,  A__			:  float			,  A__			:  float       ):
    SCREAMING_SNAKE_CASE										=   int(np.ceil((x_end - xa) / step_size       )       )
    SCREAMING_SNAKE_CASE										=   np.zeros((n + 1,)       )
    SCREAMING_SNAKE_CASE										=   ya
    SCREAMING_SNAKE_CASE										=   xa

    for k in range(A__       ):
        SCREAMING_SNAKE_CASE										=   y[k] + step_size * ode_func(A__			,  y[k]       )
        SCREAMING_SNAKE_CASE										=   y[k] + (
            (step_size / 2) * (ode_func(A__			,  y[k]       ) + ode_func(x + step_size			,  A__       ))
        )
        x += step_size

    return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()