





'''simple docstring'''

import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__lowerCAmelCase							:     int		     =logging.getLogger(__name__)





def 						UpperCamelCase    (							):
    A__    =      argparse.ArgumentParser(
        description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."	)
    parser.add_argument("--file_path"						,	type=_lowerCamelCase						,	default="data/dump.txt"						,	help="The path to the data."	)
    parser.add_argument("--tokenizer_type"						,	type=_lowerCamelCase						,	default="bert"						,	choices=["bert", "roberta", "gpt2"]	)
    parser.add_argument("--tokenizer_name"						,	type=_lowerCamelCase						,	default="bert-base-uncased"						,	help="The tokenizer to use."	)
    parser.add_argument("--dump_file"						,	type=_lowerCamelCase						,	default="data/dump"						,	help="The dump file prefix."	)
    A__    =      parser.parse_args()

    logger.info(F"Loading Tokenizer ({args.tokenizer_name})"	)
    if args.tokenizer_type == "bert":
        A__    =      BertTokenizer.from_pretrained(args.tokenizer_name	)
        A__    =      tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
        A__    =      tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
    elif args.tokenizer_type == "roberta":
        A__    =      RobertaTokenizer.from_pretrained(args.tokenizer_name	)
        A__    =      tokenizer.special_tokens_map["cls_token"]  # `<s>`
        A__    =      tokenizer.special_tokens_map["sep_token"]  # `</s>`
    elif args.tokenizer_type == "gpt2":
        A__    =      GPTaTokenizer.from_pretrained(args.tokenizer_name	)
        A__    =      tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
        A__    =      tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

    logger.info(F"Loading text from {args.file_path}"	)
    with open(args.file_path						,	"r"						,	encoding="utf8"	) as fp:
        A__    =      fp.readlines()

    logger.info("Start encoding"	)
    logger.info(F"{len(_lowerCamelCase	)} examples to process."	)

    A__    =      []
    A__    =      0
    A__    =      1_00_00
    A__    =      time.time()
    for text in data:
        A__    =      F"{bos} {text.strip()} {sep}"
        A__    =      tokenizer.encode(_lowerCamelCase						,	add_special_tokens=_lowerCamelCase	)
        rslt.append(_lowerCamelCase	)

        iter += 1
        if iter % interval == 0:
            A__    =      time.time()
            logger.info(F"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"	)
            A__    =      time.time()
    logger.info("Finished binarization"	)
    logger.info(F"{len(_lowerCamelCase	)} examples processed."	)

    A__    =      F"{args.dump_file}.{args.tokenizer_name}.pickle"
    A__    =      tokenizer.vocab_size
    if vocab_size < (1 << 16):
        A__    =      [np.uintaa(_lowerCamelCase	) for d in rslt]
    else:
        A__    =      [np.intaa(_lowerCamelCase	) for d in rslt]
    random.shuffle(rslt_	)
    logger.info(F"Dump to {dp_file}"	)
    with open(_lowerCamelCase						,	"wb"	) as handle:
        pickle.dump(rslt_						,	_lowerCamelCase						,	protocol=pickle.HIGHEST_PROTOCOL	)


if __name__ == "__main__":
  main()










'''simple docstring'''

import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							:     Union[str, Any]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     Optional[int]		     =[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        A__    =      k.replace(_lowerCamelCase						,	_lowerCamelCase	)

    if k.startswith("encoder"	):
        A__    =      k.replace(".attn"						,	".self_attn"	)
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"final_layer_norm"	)
    elif k.startswith("decoder"	):
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"encoder_attn_layer_norm"	)
        A__    =      k.replace("norm3"						,	"final_layer_norm"	)
    return k





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      [
        "model.encoder.layernorm_embedding.weight",
        "model.encoder.layernorm_embedding.bias",
        "model.decoder.layernorm_embedding.weight",
        "model.decoder.layernorm_embedding.bias",
    ]
    for k in keys:
        A__    =      sd.pop(_lowerCamelCase	)
        A__    =      k.replace("layernorm_embedding"						,	"layer_norm"	)
        assert new_k not in sd
        A__    =      v


__lowerCAmelCase							:     Optional[int]		     =["START"]





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    A__    =      model["model"]
    A__    =      BlenderbotConfig.from_json_file(_lowerCamelCase	)
    A__    =      BlenderbotForConditionalGeneration(_lowerCamelCase	)
    A__    =      m.model.state_dict().keys()
    A__    =      []
    A__    =      {}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        A__    =      rename_state_dict_key(_lowerCamelCase	)
        if new_k not in valid_keys:
            failures.append([k, new_k]	)
        else:
            A__    =      v
    if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
        rename_layernorm_keys(_lowerCamelCase	)
    m.model.load_state_dict(_lowerCamelCase						,	strict=_lowerCamelCase	)
    m.half()
    m.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
  __lowerCAmelCase							:     Union[str, Any]		     =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
  parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
  parser.add_argument(
      "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
  )
  __lowerCAmelCase							:     Union[str, Any]		     =parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



