import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Any		,							__SCREAMING_SNAKE_CASE       :					List[Any]		,							__SCREAMING_SNAKE_CASE       :					List[str]=0   ):







    '''simple docstring'''
    # Format the message.
    if name is None:
        __snake_case       :				List[str] 			=    None
    else:
        __snake_case       :				int 			=    """.""" * max(0		,							spaces - 2   ) + """# {:""" + str(5_0 - spaces   ) + """s}"""
        __snake_case       :				Optional[int] 			=    fmt.format(__SCREAMING_SNAKE_CASE   )

    # Print and recurse (if needed).
    if isinstance(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ):
        if msg is not None:
            print(__SCREAMING_SNAKE_CASE   )
        for k in val.keys():
            recursive_print(__SCREAMING_SNAKE_CASE		,							val[k]		,							spaces + 2   )
    elif isinstance(__SCREAMING_SNAKE_CASE		,							torch.Tensor   ):
        print(__SCREAMING_SNAKE_CASE		,							""":"""		,							val.size()   )
    else:
        print(__SCREAMING_SNAKE_CASE		,							""":"""		,							__SCREAMING_SNAKE_CASE   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]		,							__SCREAMING_SNAKE_CASE       :					int		,							__SCREAMING_SNAKE_CASE       :					List[str]		,							__SCREAMING_SNAKE_CASE       :					Union[str, Any]   ):







    '''simple docstring'''
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    __snake_case       :				List[str] 			=    param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    __snake_case       :				List[str] 			=    (num_heads, num_splits, hidden_size) + input_shape[1:]
    __snake_case       :				Dict 			=    param.view(*__SCREAMING_SNAKE_CASE   )
    __snake_case       :				str 			=    param.transpose(0		,							1   ).contiguous()
    __snake_case       :				Optional[Any] 			=    param.view(*__SCREAMING_SNAKE_CASE   )
    return param







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Any		,							__SCREAMING_SNAKE_CASE       :					List[Any]   ):







    '''simple docstring'''
    __snake_case       :				Union[str, Any] 			=    config.n_positions
    __snake_case       :				List[Any] 			=    config.n_layer
    __snake_case       :				str 			=    config.vocab_size
    __snake_case       :				Union[str, Any] 			=    config.n_head
    __snake_case       :				List[str] 			=    config.n_embd // config.n_head

    __snake_case       :				Dict 			=    sd_megatron["""model.language_model.embedding.word_embeddings.weight"""][:vocab_size, :]
    __snake_case       :				Tuple 			=    {
        """transformer.wte.weight""": word_embeddings,
        """transformer.wpe.weight""": sd_megatron["""model.language_model.embedding.position_embeddings.weight"""],
        """transformer.ln_f.weight""": sd_megatron["""model.language_model.encoder.final_layernorm.weight"""],
        """transformer.ln_f.bias""": sd_megatron["""model.language_model.encoder.final_layernorm.bias"""],
    }

    __snake_case       :				str 			=    """model.language_model.encoder.layers."""
    for i in range(__SCREAMING_SNAKE_CASE   ):
        __snake_case       :				Dict 			=    torch.tril(torch.ones((n_positions, n_positions)		,							dtype=torch.bool   )   )
        __snake_case       :				Union[str, Any] 			=    causal_mask.view(1		,							1		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
        __snake_case       :				List[Any] 			=    causal_mask
        __snake_case       :				List[Any] 			=    torch.tensor(-1E4		,							dtype=torch.bfloataa   )

        __snake_case       :				Optional[int] 			=    sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
        __snake_case       :				Dict 			=    sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

        __snake_case       :				List[Any] 			=    sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
        __snake_case       :				Any 			=    fix_query_key_value_ordering(__SCREAMING_SNAKE_CASE		,							3		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
        __snake_case       :				Any 			=    vala.transpose(0		,							1   ).contiguous()

        __snake_case       :				List[str] 			=    sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
        __snake_case       :				int 			=    fix_query_key_value_ordering(__SCREAMING_SNAKE_CASE		,							3		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
        __snake_case       :				List[str] 			=    vala

        __snake_case       :				List[str] 			=    sd_megatron[F'''{pf}{i}.self_attention.dense.weight'''].transpose(
            0		,							1   )
        __snake_case       :				Union[str, Any] 			=    sd_megatron[F'''{pf}{i}.self_attention.dense.bias''']
        __snake_case       :				Dict 			=    sd_megatron[F'''{pf}{i}.post_attention_layernorm.weight''']
        __snake_case       :				Optional[Any] 			=    sd_megatron[F'''{pf}{i}.post_attention_layernorm.bias''']
        __snake_case       :				Optional[int] 			=    sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0		,							1   )
        __snake_case       :				str 			=    sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.bias''']
        __snake_case       :				int 			=    sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
            0		,							1   )
        __snake_case       :				List[Any] 			=    sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.bias''']

    # For LM head, transformers' wants the matrix to weight embeddings.
    __snake_case       :				Any 			=    word_embeddings

    return sd_hf







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]		,							__SCREAMING_SNAKE_CASE       :					Optional[int]   ):







    '''simple docstring'''
    __snake_case       :				int 			=    6_4_0_0_0
    __snake_case       :				List[Any] 			=    config_megatron["""encoder_seq_length"""]
    __snake_case       :				Optional[int] 			=    config_megatron["""hidden_size"""]
    __snake_case       :				List[Any] 			=    config_megatron["""num_layers"""]
    __snake_case       :				List[Any] 			=    config_megatron["""num_attention_heads"""]
    __snake_case       :				Optional[Any] 			=    config_megatron["""ffn_hidden_size"""]
    __snake_case       :				Optional[int] 			=    """gelu"""
    __snake_case       :				List[str] 			=    0.1
    __snake_case       :				str 			=    0.1
    __snake_case       :				Optional[int] 			=    0.1
    __snake_case       :				int 			=    config_megatron["""layernorm_epsilon"""]  # 1e-5
    __snake_case       :				List[str] 			=    config_megatron["""init_method_std"""]  # 0.02
    __snake_case       :				int 			=    config_megatron["""apply_query_key_layer_scaling"""]  # True
    __snake_case       :				List[Any] 			=    True
    __snake_case       :				int 			=    True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4_0_9_6:
        __snake_case       :				int 			=    1  # <|endoftext|>
        __snake_case       :				Optional[int] 			=    1  # <|endoftext|>
        __snake_case       :				Union[str, Any] 			=    0  # <unk>
    else:
        __snake_case       :				List[Any] 			=    2  # <s>
        __snake_case       :				int 			=    3  # <|endoftext|>
        __snake_case       :				List[str] 			=    0  # <pad>

    return config_hf







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]   ):







    '''simple docstring'''
    print(__SCREAMING_SNAKE_CASE   )

    __snake_case       :				Tuple 			=    args.checkpoint_path
    __snake_case       :				int 			=    args.save_path
    if isfile(__SCREAMING_SNAKE_CASE   ):
        raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''   )

    # Load the model.
    __snake_case       :				Dict 			=    torch.load(__SCREAMING_SNAKE_CASE		,							map_location="""cpu"""   )

    # Load the config.
    __snake_case       :				int 			=    checkpoint["""hyper_parameters"""]["""cfg"""]
    __snake_case       :				Any 			=    GPTaConfig()
    __snake_case       :				Any 			=    copy_config(config_hf=__SCREAMING_SNAKE_CASE		,							config_megatron=__SCREAMING_SNAKE_CASE   )
    __snake_case       :				int 			=    ["""GPT2LMHeadModel"""]

    __snake_case       :				Optional[Any] 			=    checkpoint["""state_dict"""]

    # Convert.
    print("""Converting"""   )
    __snake_case       :				Dict 			=    convert_megatron_checkpoint(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

    __snake_case       :				Any 			=    """GPTSw3Tokenizer"""

    # Store the config to file.
    print("""Saving config"""   )
    config_hf.save_pretrained(__SCREAMING_SNAKE_CASE   )

    # Store the state_dict to file.
    __snake_case       :				Any 			=    os.path.join(__SCREAMING_SNAKE_CASE		,							"""pytorch_model.bin"""   )
    print(F'''Saving checkpoint to "{output_checkpoint_file}"'''   )
    torch.save(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
     lowercase_  							=			argparse.ArgumentParser()
     parser.add_argument(
         "--checkpoint_path",
         type=str,
         required=True,
         help="e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000",
     )
     parser.add_argument("--save_path", type=str, required=True, help="e.g. /home/user/gpt-sw3/hf")
     parser.add_argument("--print-checkpoint-structure", action="store_true")
     lowercase_  							=			parser.parse_args()
     main(_args)




import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


lowercase_  							=			logging.getLogger(__name__)







@dataclass
class      SCREAMING_SNAKE_CASE__					:
       A							:	str          =	field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
       A							:	Optional[str]          =	field(
           default=__UpperCamelCase   ,							metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
       A							:	Optional[str]          =	field(
           default="NER"   ,							metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}    )
       A							:	Optional[str]          =	field(
           default=__UpperCamelCase   ,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
       A							:	bool          =	field(default=__UpperCamelCase   ,							metadata={"help": "Set this flag to use fast tokenization."}    )
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       A							:	Optional[str]          =	field(
           default=__UpperCamelCase   ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,							)







@dataclass
class      SCREAMING_SNAKE_CASE__					:
       A							:	str          =	field(
           metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}    )
       A							:	Optional[str]          =	field(
           metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}    )
       A							:	int          =	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."
               )
           }   ,							)
       A							:	bool          =	field(
           default=__UpperCamelCase   ,							metadata={"help": "Overwrite the cached training and evaluation sets"}    )







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    # 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.
    __snake_case       :				Tuple 			=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
    __snake_case			, __snake_case			, __snake_case       :				Tuple 			=    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."""   )

    __snake_case       :				int 			=    import_module("""tasks"""   )

    try:
        __snake_case       :				Union[str, Any] 			=    getattr(__SCREAMING_SNAKE_CASE		,							model_args.task_type   )
        __snake_case       :				TokenClassificationTask 			=    token_classification_task_clazz()
    except AttributeError:
        raise ValueError(
            F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
            F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''   )

    # Setup logging
    logging.basicConfig(
        format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""		,							datefmt="""%m/%d/%Y %H:%M:%S"""		,							level=logging.INFO		,							)
    logger.info(
        """n_replicas: %s, distributed training: %s, 16-bits training: %s"""		,							training_args.n_replicas		,							bool(training_args.n_replicas > 1   )		,							training_args.fpaa		,							)
    logger.info("""Training/evaluation parameters %s"""		,							__SCREAMING_SNAKE_CASE   )

    # Prepare Token Classification task
    __snake_case       :				str 			=    token_classification_task.get_labels(data_args.labels   )
    __snake_case       :				Dict[int, str] 			=    dict(enumerate(__SCREAMING_SNAKE_CASE   )   )
    __snake_case       :				Any 			=    len(__SCREAMING_SNAKE_CASE   )

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

    __snake_case       :				List[str] 			=    AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path		,							num_labels=__SCREAMING_SNAKE_CASE		,							idalabel=__SCREAMING_SNAKE_CASE		,							labelaid={label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE   )}		,							cache_dir=model_args.cache_dir		,							)
    __snake_case       :				List[str] 			=    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		,							use_fast=model_args.use_fast		,							)

    with training_args.strategy.scope():
        __snake_case       :				Optional[int] 			=    TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path		,							from_pt=bool(""".bin""" in model_args.model_name_or_path   )		,							config=__SCREAMING_SNAKE_CASE		,							cache_dir=model_args.cache_dir		,							)

    # Get datasets
    __snake_case       :				Optional[int] 			=    (
        TFTokenClassificationDataset(
            token_classification_task=__SCREAMING_SNAKE_CASE		,							data_dir=data_args.data_dir		,							tokenizer=__SCREAMING_SNAKE_CASE		,							labels=__SCREAMING_SNAKE_CASE		,							model_type=config.model_type		,							max_seq_length=data_args.max_seq_length		,							overwrite_cache=data_args.overwrite_cache		,							mode=Split.train		,							)
        if training_args.do_train
        else None
    )
    __snake_case       :				Union[str, Any] 			=    (
        TFTokenClassificationDataset(
            token_classification_task=__SCREAMING_SNAKE_CASE		,							data_dir=data_args.data_dir		,							tokenizer=__SCREAMING_SNAKE_CASE		,							labels=__SCREAMING_SNAKE_CASE		,							model_type=config.model_type		,							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 align_predictions(__SCREAMING_SNAKE_CASE       :					np.ndarray		,							__SCREAMING_SNAKE_CASE       :					np.ndarray   ) -> Tuple[List[int], List[int]]:
        __snake_case       :				Dict 			=    np.argmax(__SCREAMING_SNAKE_CASE		,							axis=2   )
        __snake_case			, __snake_case       :				int 			=    preds.shape
        __snake_case       :				Union[str, Any] 			=    [[] for _ in range(__SCREAMING_SNAKE_CASE   )]
        __snake_case       :				Union[str, Any] 			=    [[] for _ in range(__SCREAMING_SNAKE_CASE   )]

        for i in range(__SCREAMING_SNAKE_CASE   ):
            for j in range(__SCREAMING_SNAKE_CASE   ):
                if label_ids[i, j] != -1_0_0:
                    out_label_list[i].append(label_map[label_ids[i][j]]   )
                    preds_list[i].append(label_map[preds[i][j]]   )

        return preds_list, out_label_list

    def compute_metrics(__SCREAMING_SNAKE_CASE       :					EvalPrediction   ) -> Dict:
        __snake_case			, __snake_case       :				Optional[int] 			=    align_predictions(p.predictions		,							p.label_ids   )

        return {
            "precision": precision_score(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ),
            "recall": recall_score(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ),
            "f1": fa_score(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ),
        }

    # Initialize our Trainer
    __snake_case       :				Optional[Any] 			=    TFTrainer(
        model=__SCREAMING_SNAKE_CASE		,							args=__SCREAMING_SNAKE_CASE		,							train_dataset=train_dataset.get_dataset() if train_dataset else None		,							eval_dataset=eval_dataset.get_dataset() if eval_dataset else None		,							compute_metrics=__SCREAMING_SNAKE_CASE		,							)

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir   )

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

        __snake_case       :				List[str] 			=    trainer.evaluate()
        __snake_case       :				Dict 			=    os.path.join(training_args.output_dir		,							"""eval_results.txt"""   )

        with open(__SCREAMING_SNAKE_CASE		,							"""w"""   ) as writer:
            logger.info("""***** Eval results *****"""   )

            for key, value in result.items():
                logger.info("""  %s = %s"""		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
                writer.write("""%s = %s\n""" % (key, value)   )

            results.update(__SCREAMING_SNAKE_CASE   )

    # Predict
    if training_args.do_predict:
        __snake_case       :				Any 			=    TFTokenClassificationDataset(
            token_classification_task=__SCREAMING_SNAKE_CASE		,							data_dir=data_args.data_dir		,							tokenizer=__SCREAMING_SNAKE_CASE		,							labels=__SCREAMING_SNAKE_CASE		,							model_type=config.model_type		,							max_seq_length=data_args.max_seq_length		,							overwrite_cache=data_args.overwrite_cache		,							mode=Split.test		,							)

        __snake_case			, __snake_case			, __snake_case       :				Optional[Any] 			=    trainer.predict(test_dataset.get_dataset()   )
        __snake_case			, __snake_case       :				Dict 			=    align_predictions(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
        __snake_case       :				Optional[Any] 			=    classification_report(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

        logger.info("""\n%s"""		,							__SCREAMING_SNAKE_CASE   )

        __snake_case       :				Optional[int] 			=    os.path.join(training_args.output_dir		,							"""test_results.txt"""   )

        with open(__SCREAMING_SNAKE_CASE		,							"""w"""   ) as writer:
            writer.write("""%s\n""" % report   )

        # Save predictions
        __snake_case       :				Dict 			=    os.path.join(training_args.output_dir		,							"""test_predictions.txt"""   )

        with open(__SCREAMING_SNAKE_CASE		,							"""w"""   ) as writer:
            with open(os.path.join(data_args.data_dir		,							"""test.txt"""   )		,							"""r"""   ) as f:
                __snake_case       :				List[str] 			=    0

                for line in f:
                    if line.startswith("""-DOCSTART-"""   ) or line == "" or line == "\n":
                        writer.write(__SCREAMING_SNAKE_CASE   )

                        if not preds_list[example_id]:
                            example_id += 1
                    elif preds_list[example_id]:
                        __snake_case       :				List[Any] 			=    line.split()[0] + """ """ + preds_list[example_id].pop(0   ) + """\n"""

                        writer.write(__SCREAMING_SNAKE_CASE   )
                    else:
                        logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""		,							line.split()[0]   )

    return results


if __name__ == "__main__":
     main()



