





'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__lowerCAmelCase							:     Optional[Any]		     =logging.getLogger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : List[Any]	):
    return (preds == labels).mean()






@dataclass
class        UpperCAmelCase     :
 __lowercase       		=    field(
     metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
 __lowercase       		=    field(
     default=UpperCamelCase__      ,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
 __lowercase       		=    field(
     default=UpperCamelCase__      ,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
 __lowercase       		=    field(
     default=UpperCamelCase__      ,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,	)






@dataclass
class        UpperCAmelCase     :
 __lowercase       		=    field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()    )}    )
 __lowercase       		=    field(metadata={"""help""": """Should contain the data files for the task."""}    )
 __lowercase       		=    field(
     default=128      ,	metadata={
         """help""": (
             """The maximum total input sequence length after tokenization. Sequences longer """
             """than this will be truncated, sequences shorter will be padded."""
         )
     }      ,	)
 __lowercase       		=    field(
     default=UpperCamelCase__      ,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )





def 						UpperCamelCase    (							):
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    A__    =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
    A__,     A__,     A__    =      parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir	)
        and os.listdir(training_args.output_dir	)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
            " --overwrite_output_dir to overcome."	)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"						,	datefmt="%m/%d/%Y %H:%M:%S"						,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN						,	)
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"						,	training_args.local_rank						,	training_args.device						,	training_args.n_gpu						,	bool(training_args.local_rank != -1	)						,	training_args.fpaa						,	)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank	):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s"						,	_lowerCamelCase	)

    # Set seed
    set_seed(training_args.seed	)

    try:
        A__    =      processors[data_args.task_name]()
        A__    =      processor.get_labels()
        A__    =      len(_lowerCamelCase	)
    except KeyError:
        raise ValueError("Task not found: %s" % (data_args.task_name)	)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    A__    =      AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path						,	num_labels=_lowerCamelCase						,	finetuning_task=data_args.task_name						,	cache_dir=model_args.cache_dir						,	)
    A__    =      AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,	cache_dir=model_args.cache_dir						,	)
    A__    =      AutoModelForMultipleChoice.from_pretrained(
        model_args.model_name_or_path						,	from_tf=bool(".ckpt" in model_args.model_name_or_path	)						,	config=_lowerCamelCase						,	cache_dir=model_args.cache_dir						,	)

    # Get datasets
    A__    =      (
        MultipleChoiceDataset(
            data_dir=data_args.data_dir						,	tokenizer=_lowerCamelCase						,	task=data_args.task_name						,	max_seq_length=data_args.max_seq_length						,	overwrite_cache=data_args.overwrite_cache						,	mode=Split.train						,	)
        if training_args.do_train
        else None
    )
    A__    =      (
        MultipleChoiceDataset(
            data_dir=data_args.data_dir						,	tokenizer=_lowerCamelCase						,	task=data_args.task_name						,	max_seq_length=data_args.max_seq_length						,	overwrite_cache=data_args.overwrite_cache						,	mode=Split.dev						,	)
        if training_args.do_eval
        else None
    )

    def compute_metrics(_lowerCamelCase : EvalPrediction	) -> Dict:
        A__    =      np.argmax(p.predictions						,	axis=1	)
        return {"acc": simple_accuracy(_lowerCamelCase						,	p.label_ids	)}

    # Data collator
    A__    =      DataCollatorWithPadding(_lowerCamelCase						,	pad_to_multiple_of=8	) if training_args.fpaa else None

    # Initialize our Trainer
    A__    =      Trainer(
        model=_lowerCamelCase						,	args=_lowerCamelCase						,	train_dataset=_lowerCamelCase						,	eval_dataset=_lowerCamelCase						,	compute_metrics=_lowerCamelCase						,	data_collator=_lowerCamelCase						,	)

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
    A__    =      {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"	)

        A__    =      trainer.evaluate()

        A__    =      os.path.join(training_args.output_dir						,	"eval_results.txt"	)
        if trainer.is_world_master():
            with open(_lowerCamelCase						,	"w"	) as writer:
                logger.info("***** Eval results *****"	)
                for key, value in result.items():
                    logger.info("  %s = %s"						,	_lowerCamelCase						,	_lowerCamelCase	)
                    writer.write("%s = %s\n" % (key, value)	)

                results.update(_lowerCamelCase	)

    return results





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]	):
    # For xla_spawn (TPUs)
    main()


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)



