






'''simple docstring'''




import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
    from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
    raise ImportError('''You need to install the version of fairseq from the Mega repo!''')

class      _UpperCAmelCase    (  nn.Module		):




   """simple docstring"""

   def __init__(	self				:			int   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			Union[str, Any]   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         super().__init__()
         _A      =			mega_args
         _A      =			nn.Embedding(__UpperCAmelCase   ,   self.mega_args.encoder_embed_dim	)
         _A      =			nn.ModuleList([MegaEncoderLayer(self.mega_args	) for _ in range(__UpperCAmelCase	)]	)
         _A      =			depth

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			Tuple   ,   __UpperCAmelCase				:			Optional[int]   ,   __UpperCAmelCase				:			int=True   ,   __UpperCAmelCase				:			Tuple=0	):





         '''simple docstring'''





         if batch_first:
               _A      =			input_ids.T

         # to make things more confusing, Mega expects the attention mask to
         # be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
         # which is the opposite of what HF returns
         if ignore_mask_value == 0:
               _A      =			1 - attention_mask

         # get token embeddings from IDs
         _A      =			self.embedding_layer(__UpperCAmelCase	)

         # pass through the Mega layers
         # input is (time, batch, encoder dim) and output is the same
         for encoder in self.encoders:
               _A      =			encoder(__UpperCAmelCase   ,   __UpperCAmelCase	)

         # return according to the shape specified
         if batch_first:
               # (T, B, H) --> (B, T, H)
               return torch.transpose(__UpperCAmelCase   ,   0   ,   1	)
         else:
               return embeds





class      _UpperCAmelCase    (  nn.Module		):




   """simple docstring"""

   def __init__(	self				:			Any   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			Optional[Any]   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         super().__init__()
         _A      =			MegaLM(__UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase	)
         _A      =			nn.Linear(mega_args.encoder_embed_dim   ,   __UpperCAmelCase	)
         _A      =			nn.Dropout(p=0.1	)

   def 		lowerCAmelCase				(	self				:			List[str]   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			List[Any]   ,   __UpperCAmelCase				:			Dict=True   ,   __UpperCAmelCase				:			List[Any]=0	):





         '''simple docstring'''





         _A      =			self.mega(__UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase	)
         return self.mlm_head(self.dropout(__UpperCAmelCase	)	)





def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase ) ->		Optional[Any]:

      '''simple docstring'''


      with open(os.path.join(__lowercase     ,						"model_args.pkl" )     ,						"rb" ) as f:
            _A      =			pkl.load(__lowercase )

      # load the original encoder
      _A      =			OriginalMegaForMaskedLM(**__lowercase ).eval()

      # load its weights
      print(
          "Original Mega encoder:"     ,						original_mlm.mega.load_state_dict(
              torch.load(os.path.join(__lowercase     ,						"encoder_weights.pt" )     ,						map_location="cpu" ) )     ,						)
      print(
          "Original Mega MLM layer:"     ,						original_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(__lowercase     ,						"mlm_head_weights.pt" )     ,						map_location="cpu" ) )     ,						)

      # create a new config from the old one
      _A      =			MegaConfig(
          num_hidden_layers=mega_original_args["depth"]     ,						vocab_size=mega_original_args["vocab_size"]     ,						hidden_size=mega_original_args["mega_args"].encoder_embed_dim     ,						shared_representation_size=mega_original_args["mega_args"].encoder_z_dim     ,						intermediate_size=mega_original_args["mega_args"].encoder_hidden_dim     ,						ema_projection_size=mega_original_args["mega_args"].encoder_n_dim     ,						dropout_prob=mega_original_args["mega_args"].dropout     ,						attention_probs_dropout_prob=mega_original_args["mega_args"].attention_dropout     ,						hidden_dropout_prob=mega_original_args["mega_args"].hidden_dropout     ,						activation=mega_original_args["mega_args"].activation_fn     ,						attention_activation=mega_original_args["mega_args"].attention_activation_fn     ,						bidirectional=mega_original_args["mega_args"].bidirectional     ,						use_chunking=mega_original_args["mega_args"].encoder_chunk_size > 0     ,						chunk_size=mega_original_args["mega_args"].encoder_chunk_size     ,						truncation=mega_original_args["mega_args"].truncation_length     ,						normalization_type=mega_original_args["mega_args"].normalization_type     ,						normalize_before_mega=__lowercase     ,						norm_affine=__lowercase     ,						use_feature_dropout=mega_original_args["mega_args"].feature_dropout     ,						relative_positional_bias=mega_original_args["mega_args"].rel_pos_bias     ,						max_positions=mega_original_args["mega_args"].max_source_positions     ,						nffn_hidden_size=mega_original_args["mega_args"].encoder_ffn_embed_dim     ,						normalize_before_ffn=mega_original_args["mega_args"].normalize_before     ,						nffn_activation_dropout_prob=0.0     ,						add_token_type_embeddings=__lowercase     ,						add_lm_hidden_dense_layer=__lowercase     ,						)

      _A      =			MegaForMaskedLM(__lowercase ).eval()

      # the originl checkpoint just uses nn.Embedding for the word embeddings
      # we use a wrapper module for embeddings to add support for positional embeddings
      _A      =			original_mlm.mega.embedding_layer.weight

      # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
      # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
      # also renaming previously confusing parameter names
      _A      =			original_mlm.mega.encoders.state_dict()
      _A      =			{}
      for module_name in original_state_dict.keys():
            _A      =			None
            # have to handle gamma, beta, and alpha differently due to their use
            # in multiple modules within the original repository;
            # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
            # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
            if "beta" in module_name:
                  # EMA sub-layers were always called "move" in the original repo
                  if "move.beta" in module_name:
                        _A      =			module_name.replace("move.beta"     ,						"ema_gate.ema_expansion_matrix" )
                  elif "mega_layer.beta" in module_name:
                        _A      =			module_name.replace("beta"     ,						"qk_bias" )
                  else:
                        _A      =			module_name.replace("beta"     ,						"b_param" )
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
            elif "gamma" in module_name:
                  if "move.gamma" in module_name:
                        _A      =			module_name.replace("move.gamma"     ,						"ema_gate.kernel_projection_matrix" )
                  elif "mega_layer.gamma" in module_name:
                        _A      =			module_name.replace("gamma"     ,						"qk_weight" )
                  else:
                        _A      =			module_name.replace("gamma"     ,						"g_param" )
        # alpha is used in EMA and positional bias; renaming to improve readability
            elif "move.alpha" in module_name:
                  _A      =			module_name.replace("move.alpha"     ,						"ema_gate.decay_factor" )
            # delta is only used in EMA; renaming to improve readability
            elif "move.delta" in module_name:
                  _A      =			module_name.replace("move.delta"     ,						"ema_gate.damping_factor" )
            # omega is only used in EMA; renaming to improve readability
            elif "omega" in module_name:
                  _A      =			module_name.replace("move.omega"     ,						"ema_gate.residual_weight" )

            if new_module_name:
                  _A      =			new_module_name

      if len(__lowercase ) != 0:
            print(F'''Renaming these keys: {updated_keys.keys()}''' )
      else:
            print("No need to rename state dict entries" )
      for old, new in updated_keys.items():
            _A      =			original_state_dict.pop(__lowercase )

      # now attempt to load the state dictionary with updated names
      # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
      print("HF Mega encoder:"     ,						hf_mlm.mega.layers.load_state_dict(__lowercase ) )

      # load the MLM head weights directly
      print(
          "HF Mega MLM layer:"     ,						hf_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(__lowercase     ,						"mlm_head_weights.pt" )     ,						map_location="cpu" ) )     ,						)

      # test on a randomly generated input sequence
      _A      =			torch.randint(0     ,						hf_config.vocab_size     ,						size=(4, 256) )
      _A      =			torch.ones_like(__lowercase )
      # mask a few tokens to make sure masking is applied appropriately :)
      _A      =			0

      # run forward passes
      _A      =			original_mlm(__lowercase     ,						__lowercase     ,						batch_first=__lowercase     ,						ignore_mask_value=0 )
      _A      =			hf_mlm(__lowercase     ,						__lowercase )[0]

      # print shapes and diff
      print(F'''original output {original_output.shape}''' )
      print(F'''hf output {hf_output.shape}''' )
      print(F'''max diff: {(original_output - hf_output).max()}''' )  # 0.0
      _A      =			torch.allclose(__lowercase     ,						__lowercase     ,						atol=1e-3 )

      if success:
            print("Yay!" )
            hf_mlm.save_pretrained(__lowercase )
      else:
            raise RuntimeError(F'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}''' )

      if includes_tokenizer:
            print("Transferring tokenizer" )
            _A      =			AutoTokenizer.from_pretrained(__lowercase )
            tokenizer.save_pretrained(__lowercase )


if __name__ == "__main__":
    lowerCamelCase_							      =      argparse.ArgumentParser()

    parser.add_argument(
        '''--pretrained_checkpoint_path''',
        default=None,
        type=str,
        required=True,
        help='''Point to the directory containing your model weights using the official Mega repo''',
    )

    parser.add_argument(
        '''--output_path''', default=None, type=str, required=True, help='''Location to save the Hugging Face version'''
    )

    parser.add_argument(
        '''--includes_tokenizer''',
        action='''store_true''',
        help='''Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo''',
    )

    lowerCamelCase_							      =      parser.parse_args()

    convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)








'''simple docstring'''




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()


lowerCamelCase_							      =      logging.getLogger(__name__)

@dataclass
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
   snake_case					       =		field(
       default='''NER'''							,					metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}		)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
   snake_case					       =		field(default=snake_case_							,					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.
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,					)





@dataclass
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}		)
   snake_case					       =		field(
       metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}		)
   snake_case					       =		field(
       default=1_28							,					metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }							,					)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





def       __lowercase		(    ) ->		Any:

      '''simple docstring'''


      _A      =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
      _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." )

      _A      =			import_module("tasks" )

      try:
            _A      =			getattr(__lowercase     ,						model_args.task_type )
            _A      =			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"     ,						__lowercase )

      # Prepare Token Classification task
      _A      =			token_classification_task.get_labels(data_args.labels )
      _A      =			dict(enumerate(__lowercase ) )
      _A      =			len(__lowercase )

      # 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=__lowercase     ,						idalabel=__lowercase     ,						labelaid={label: i for i, label in enumerate(__lowercase )}     ,						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     ,						use_fast=model_args.use_fast     ,						)

      with training_args.strategy.scope():
            _A      =			TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path     ,						from_pt=bool(".bin" in model_args.model_name_or_path )     ,						config=__lowercase     ,						cache_dir=model_args.cache_dir     ,						)

      # Get datasets
      _A      =			(
          TFTokenClassificationDataset(
              token_classification_task=__lowercase     ,						data_dir=data_args.data_dir     ,						tokenizer=__lowercase     ,						labels=__lowercase     ,						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
      )
      _A      =			(
          TFTokenClassificationDataset(
              token_classification_task=__lowercase     ,						data_dir=data_args.data_dir     ,						tokenizer=__lowercase     ,						labels=__lowercase     ,						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(__lowercase     ,						__lowercase ) -> Tuple[List[int], List[int]]:
            _A      =			np.argmax(__lowercase     ,						axis=2 )
            _A  ,			_A      =			preds.shape
            _A      =			[[] for _ in range(__lowercase )]
            _A      =			[[] for _ in range(__lowercase )]

            for i in range(__lowercase ):
                  for j in range(__lowercase ):
                        if label_ids[i, j] != -100:
                              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(__lowercase ) -> Dict:
            _A  ,			_A      =			align_predictions(p.predictions     ,						p.label_ids )

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

      # Initialize our Trainer
      _A      =			TFTrainer(
          model=__lowercase     ,						args=__lowercase     ,						train_dataset=train_dataset.get_dataset() if train_dataset else None     ,						eval_dataset=eval_dataset.get_dataset() if eval_dataset else None     ,						compute_metrics=__lowercase     ,						)

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            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" )

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

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

                  results.update(__lowercase )

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

            _A  ,			_A  ,			_A      =			trainer.predict(test_dataset.get_dataset() )
            _A  ,			_A      =			align_predictions(__lowercase     ,						__lowercase )
            _A      =			classification_report(__lowercase     ,						__lowercase )

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

            _A      =			os.path.join(training_args.output_dir     ,						"test_results.txt" )

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

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

            with open(__lowercase     ,						"w" ) as writer:
                  with open(os.path.join(data_args.data_dir     ,						"test.txt" )     ,						"r" ) as f:
                        _A      =			0

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

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

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

      return results


if __name__ == "__main__":
    main()





