

# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig
def 	a (     A__      :  Union[str, Any] ,  A__      :  Any ,  A__      :  Any=0	)       -> Any:





      """simple docstring"""



      if name is None:
            _lowercase      			=None
      else:
            _lowercase      			='.' * max(0 ,  spaces - 2	) + '# {:' + str(50 - spaces	) + 's}'
            _lowercase      			=fmt.format(UpperCAmelCase__	)

      # Print and recurse (if needed).
      if isinstance(UpperCAmelCase__ ,  UpperCAmelCase__	):
            if msg is not None:
                  print(UpperCAmelCase__	)
            for k in val.keys():
                  recursive_print(UpperCAmelCase__ ,  val[k] ,  spaces + 2	)
      elif isinstance(UpperCAmelCase__ ,  torch.Tensor	):
            print(UpperCAmelCase__ ,  ':' ,  val.size()	)
      else:
            print(UpperCAmelCase__ ,  ':' ,  UpperCAmelCase__	)
def 	a (     A__      :  Optional[int] ,  A__      :  int ,  A__      :  str ,  A__      :  Tuple ,  A__      :  Any	)       -> Dict:





      """simple docstring"""



      _lowercase      			=param.size()
      if checkpoint_version == 1.0:
            # version 1.0 stores [num_heads * hidden_size * num_splits, :]
            _lowercase      			=(num_heads, hidden_size, num_splits) + input_shape[1:]
            _lowercase      			=param.view(*UpperCAmelCase__	)
            _lowercase      			=param.transpose(0 ,  2	)
            _lowercase      			=param.transpose(1 ,  2	).contiguous()
      elif checkpoint_version >= 2.0:
            # other versions store [num_heads * num_splits * hidden_size, :]
            _lowercase      			=(num_heads, num_splits, hidden_size) + input_shape[1:]
            _lowercase      			=param.view(*UpperCAmelCase__	)
            _lowercase      			=param.transpose(0 ,  1	).contiguous()
      _lowercase      			=param.view(*UpperCAmelCase__	)
      return param
def 	a (     A__      :  Union[str, Any] ,  A__      :  str ,  A__      :  List[Any]	)       -> Any:





      """simple docstring"""



      _lowercase      			={}

      # old versions did not store training args
      _lowercase      			=input_state_dict.get('args' ,  UpperCAmelCase__	)
      if ds_args is not None:
            # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
            # from pprint import pprint
            # pprint(vars(ds_args))

            _lowercase      			=ds_args.padded_vocab_size
            _lowercase      			=ds_args.max_position_embeddings
            _lowercase      			=ds_args.hidden_size
            _lowercase      			=ds_args.num_layers
            _lowercase      			=ds_args.num_attention_heads
            _lowercase      			=ds_args.ffn_hidden_size
            # pprint(config)

      # The number of heads.
      _lowercase      			=config.n_head
      # The hidden_size per head.
      _lowercase      			=config.n_embd // config.n_head
      # Megatron-LM checkpoint version
      if "checkpoint_version" in input_state_dict.keys():
            _lowercase      			=input_state_dict['checkpoint_version']
      else:
            _lowercase      			=0.0

      # The model.
      _lowercase      			=input_state_dict['model']
      # The language model.
      _lowercase      			=model['language_model']
      # The embeddings.
      _lowercase      			=lm['embedding']

      # The word embeddings.
      _lowercase      			=embeddings['word_embeddings']['weight']
      # Truncate the embedding table to vocab_size rows.
      _lowercase      			=word_embeddings[: config.vocab_size, :]
      _lowercase      			=word_embeddings

      # The position embeddings.
      _lowercase      			=embeddings['position_embeddings']['weight']
      # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
      _lowercase      			=pos_embeddings.size(0	)
      if n_positions != config.n_positions:
            raise ValueError(
                F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'''	)
      # Store the position embeddings.
      _lowercase      			=pos_embeddings

      # The transformer.
      _lowercase      			=lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

      # The regex to extract layer names.
      _lowercase      			=re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'	)

      # The simple map of names for "automated" rules.
      _lowercase      			={
          'attention.dense': '.attn.c_proj.',
          'self_attention.dense': '.attn.c_proj.',
          'mlp.dense_h_to_4h': '.mlp.c_fc.',
          'mlp.dense_4h_to_h': '.mlp.c_proj.',
      }

      # Extract the layers.
      for key, val in transformer.items():
            # Match the name.
            _lowercase      			=layer_re.match(UpperCAmelCase__	)

            # Stop if that's not a layer
            if m is None:
                  break

            # The index of the layer.
            _lowercase      			=int(m.group(1	)	)
            # The name of the operation.
            _lowercase      			=m.group(2	)
            # Is it a weight or a bias?
            _lowercase      			=m.group(3	)

            # The name of the layer.
            _lowercase      			=F'''transformer.h.{layer_idx}'''

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith('layernorm'	):
                  _lowercase      			='ln_1' if op_name.startswith('input'	) else 'ln_2'
                  _lowercase      			=val

            # Transpose the QKV matrix.
            elif (
                op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
            ) and weight_or_bias == "weight":
                  # Insert a tensor of 1x1xDxD bias.
                  _lowercase      			=torch.tril(torch.ones((n_positions, n_positions) ,  dtype=torch.floataa	)	).view(
                      1 ,  1 ,  UpperCAmelCase__ ,  UpperCAmelCase__	)
                  _lowercase      			=causal_mask

                  # Insert a "dummy" tensor for masked_bias.
                  _lowercase      			=torch.tensor(-1e4 ,  dtype=torch.floataa	)
                  _lowercase      			=masked_bias

                  _lowercase      			=fix_query_key_value_ordering(UpperCAmelCase__ ,  UpperCAmelCase__ ,  3 ,  UpperCAmelCase__ ,  UpperCAmelCase__	)
                  # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
                  _lowercase      			=out_val.transpose(0 ,  1	).contiguous()
                  # Store.
                  _lowercase      			=out_val

            # Transpose the bias.
            elif (
                op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
            ) and weight_or_bias == "bias":
                  _lowercase      			=fix_query_key_value_ordering(UpperCAmelCase__ ,  UpperCAmelCase__ ,  3 ,  UpperCAmelCase__ ,  UpperCAmelCase__	)
                  # Store. No change of shape.
                  _lowercase      			=out_val

            # Transpose the weights.
            elif weight_or_bias == "weight":
                  _lowercase      			=megatron_to_transformers[op_name]
                  _lowercase      			=val.transpose(0 ,  1	)

            # Copy the bias.
            elif weight_or_bias == "bias":
                  _lowercase      			=megatron_to_transformers[op_name]
                  _lowercase      			=val

    # DEBUG.
      assert config.n_layer == layer_idx + 1

      # The final layernorm.
      _lowercase      			=transformer['final_layernorm.weight']
      _lowercase      			=transformer['final_layernorm.bias']

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

      # It should be done!
      return output_state_dict
def 	a (     )       -> Optional[int]:





      """simple docstring"""



      _lowercase      			=argparse.ArgumentParser()
      parser.add_argument('--print-checkpoint-structure' ,  action='store_true'	)
      parser.add_argument(
          'path_to_checkpoint' ,  type=UpperCAmelCase__ ,  help='Path to the checkpoint file (.zip archive or direct .pt file)' ,  )
      parser.add_argument(
          '--config_file' ,  default='' ,  type=UpperCAmelCase__ ,  help='An optional config json file describing the pre-trained model.' ,  )
      _lowercase      			=parser.parse_args()

      # Extract the basename.
      _lowercase      			=os.path.dirname(args.path_to_checkpoint	)

      # Load the model.
      # the .zip is very optional, let's keep it for backward compatibility
      print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}'''	)
      if args.path_to_checkpoint.endswith('.zip'	):
            with zipfile.ZipFile(args.path_to_checkpoint ,  'r'	) as checkpoint:
                  with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'	) as pytorch_dict:
                        _lowercase      			=torch.load(UpperCAmelCase__ ,  map_location='cpu'	)
      else:
            _lowercase      			=torch.load(args.path_to_checkpoint ,  map_location='cpu'	)

      _lowercase      			=input_state_dict.get('args' ,  UpperCAmelCase__	)

      # Read the config, or default to the model released by NVIDIA.
      if args.config_file == "":
            if ds_args is not None:
                  if ds_args.bias_gelu_fusion:
                        _lowercase      			='gelu_fast'
                  elif ds_args.openai_gelu:
                        _lowercase      			='gelu_new'
                  else:
                        _lowercase      			='gelu'
            else:
                  # in the very early days this used to be "gelu_new"
                  _lowercase      			='gelu_new'

            # Spell out all parameters in case the defaults change.
            _lowercase      			=GPTaConfig(
                vocab_size=50257 ,  n_positions=1024 ,  n_embd=1024 ,  n_layer=24 ,  n_head=16 ,  n_inner=4096 ,  activation_function=UpperCAmelCase__ ,  resid_pdrop=0.1 ,  embd_pdrop=0.1 ,  attn_pdrop=0.1 ,  layer_norm_epsilon=1e-5 ,  initializer_range=0.02 ,  summary_type='cls_index' ,  summary_use_proj=UpperCAmelCase__ ,  summary_activation=UpperCAmelCase__ ,  summary_proj_to_labels=UpperCAmelCase__ ,  summary_first_dropout=0.1 ,  scale_attn_weights=UpperCAmelCase__ ,  use_cache=UpperCAmelCase__ ,  bos_token_id=50256 ,  eos_token_id=50256 ,  )
      else:
            _lowercase      			=GPTaConfig.from_json_file(args.config_file	)

      _lowercase      			=['GPT2LMHeadModel']

      # Convert.
      print('Converting'	)
      _lowercase      			=convert_megatron_checkpoint(UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__	)

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

      # Add tokenizer class info to config
      # see https://github.com/huggingface/transformers/issues/13906)
      if ds_args is not None:
            _lowercase      			=ds_args.tokenizer_type
            if tokenizer_type == "GPT2BPETokenizer":
                  _lowercase      			='gpt2'
            elif tokenizer_type == "PretrainedFromHF":
                  _lowercase      			=ds_args.tokenizer_name_or_path
            else:
                  raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}'''	)
      else:
            _lowercase      			='gpt2'

      _lowercase      			=AutoTokenizer.from_pretrained(UpperCAmelCase__	)
      _lowercase      			=type(UpperCAmelCase__	).__name__
      _lowercase      			=tokenizer_class

      # Store the config to file.
      print('Saving config'	)
      config.save_pretrained(UpperCAmelCase__	)

      # Save tokenizer based on args
      print(F'''Adding {tokenizer_class} tokenizer files'''	)
      tokenizer.save_pretrained(UpperCAmelCase__	)

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


####################################################################################################

if __name__ == "__main__":
  main()

####################################################################################################




import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


lowercase   =	logging.getLogger(__name__)
lowercase   =	list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowercase   =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class    __A:
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={
					        '''help''': (
					            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
					        )
					    }  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCAmelCase							)}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={
					        '''help''': (
					            '''Override some existing default config settings when a model is trained from scratch. Example: '''
					            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
					        )
					    }  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default='''main'''  ,		metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={
					        '''help''': (
					            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
					            '''with private models).'''
					        )
					    }  ,		)

					def  lowercase__   (						self       :  str ):
												if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
																			raise ValueError(
																			    """--config_overrides can't be used in combination with --config_name or --model_name_or_path""" )





@dataclass
class    __A:
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}							)
					SCREAMING_SNAKE_CASE           =     field(default=UpperCAmelCase  ,		metadata={'''help''': '''The input training data file (a text file).'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=5  ,		metadata={
					        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
					    }  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={
					        '''help''': (
					            '''The maximum total input sequence length after tokenization. Sequences longer '''
					            '''than this will be truncated. Default to the max input length of the model.'''
					        )
					    }  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={'''help''': '''The number of processes to use for the preprocessing.'''}  ,		)
					SCREAMING_SNAKE_CASE           =     field(
					    default=0.15  ,		metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}							)
					SCREAMING_SNAKE_CASE           =     field(
					    default=UpperCAmelCase  ,		metadata={
					        '''help''': (
					            '''Whether to pad all samples to `max_seq_length`. '''
					            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
					        )
					    }  ,		)

					def  lowercase__   (						self       :  Tuple ):
												if self.train_file is not None:
																			lowerCamelCase_					=       self.train_file.split(""".""" )[-1]
																			assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
												if self.validation_file is not None:
																			lowerCamelCase_					=       self.validation_file.split(""".""" )[-1]
																			assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   Tuple							)	->     Dict:
							with open(UpperCAmelCase__      ,					"""r"""      ,					encoding="""utf-8"""							) as f:
														lowerCamelCase_					=       [json.loads(UpperCAmelCase__							) for line in f.read().splitlines() if (len(UpperCAmelCase__							) > 0 and not line.isspace())]
							assert len(UpperCAmelCase__							) == len(UpperCAmelCase__							)

							lowerCamelCase_					=       {c: dataset[c] for c in dataset.column_names}
							lowerCamelCase_					=       refs
							return Dataset.from_dict(UpperCAmelCase__							)



def   __lowerCAmelCase    (				)	->     Any:
							# 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.

							lowerCamelCase_					=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
							if len(sys.argv							) == 2 and sys.argv[1].endswith(""".json"""							):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
							else:
														lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       parser.parse_args_into_dataclasses()

							# Detecting last checkpoint.
							lowerCamelCase_					=       None
							if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
														lowerCamelCase_					=       get_last_checkpoint(training_args.output_dir							)
														if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
																					raise ValueError(
																					    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
																					    """Use --overwrite_output_dir to overcome."""							)
														elif last_checkpoint is not None:
																					logger.info(
																					    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
																					    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""							)

    # Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""      ,					datefmt="""%m/%d/%Y %H:%M:%S"""      ,					handlers=[logging.StreamHandler(sys.stdout							)]      ,					)
							logger.setLevel(logging.INFO if is_main_process(training_args.local_rank							) else logging.WARN							)

							# Log on each process the small summary:
							logger.warning(
							    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
							    + F'''distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {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"""      ,					UpperCAmelCase__							)

							# Set seed before initializing model.
							set_seed(training_args.seed							)

							# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
							# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
							# (the dataset will be downloaded automatically from the datasets Hub).
							#
							# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
							# 'text' is found. You can easily tweak this behavior (see below).
							#
							# In distributed training, the load_dataset function guarantee that only one local process can concurrently
							# download the dataset.
							if data_args.dataset_name is not None:
														# Downloading and loading a dataset from the hub.
														lowerCamelCase_					=       load_dataset(data_args.dataset_name      ,					data_args.dataset_config_name							)
														if "validation" not in datasets.keys():
																					lowerCamelCase_					=       load_dataset(
																					    data_args.dataset_name      ,					data_args.dataset_config_name      ,					split=F'''train[:{data_args.validation_split_percentage}%]'''      ,					)
																					lowerCamelCase_					=       load_dataset(
																					    data_args.dataset_name      ,					data_args.dataset_config_name      ,					split=F'''train[{data_args.validation_split_percentage}%:]'''      ,					)
							else:
														lowerCamelCase_					=       {}
														if data_args.train_file is not None:
																					lowerCamelCase_					=       data_args.train_file
														if data_args.validation_file is not None:
																					lowerCamelCase_					=       data_args.validation_file
														lowerCamelCase_					=       data_args.train_file.split("""."""							)[-1]
														if extension == "txt":
																					lowerCamelCase_					=       """text"""
														lowerCamelCase_					=       load_dataset(UpperCAmelCase__      ,					data_files=UpperCAmelCase__							)
							# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
							# https://huggingface.co/docs/datasets/loading_datasets.html.

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							lowerCamelCase_					=       {
							    """cache_dir""": model_args.cache_dir,
							    """revision""": model_args.model_revision,
							    """use_auth_token""": True if model_args.use_auth_token else None,
							}
							if model_args.config_name:
														lowerCamelCase_					=       AutoConfig.from_pretrained(model_args.config_name      ,					**UpperCAmelCase__							)
							elif model_args.model_name_or_path:
														lowerCamelCase_					=       AutoConfig.from_pretrained(model_args.model_name_or_path      ,					**UpperCAmelCase__							)
							else:
														lowerCamelCase_					=       CONFIG_MAPPING[model_args.model_type]()
														logger.warning("""You are instantiating a new config instance from scratch."""							)
														if model_args.config_overrides is not None:
																					logger.info(F'''Overriding config: {model_args.config_overrides}'''							)
																					config.update_from_string(model_args.config_overrides							)
																					logger.info(F'''New config: {config}'''							)

							lowerCamelCase_					=       {
							    """cache_dir""": model_args.cache_dir,
							    """use_fast""": model_args.use_fast_tokenizer,
							    """revision""": model_args.model_revision,
							    """use_auth_token""": True if model_args.use_auth_token else None,
							}
							if model_args.tokenizer_name:
														lowerCamelCase_					=       AutoTokenizer.from_pretrained(model_args.tokenizer_name      ,					**UpperCAmelCase__							)
							elif model_args.model_name_or_path:
														lowerCamelCase_					=       AutoTokenizer.from_pretrained(model_args.model_name_or_path      ,					**UpperCAmelCase__							)
							else:
														raise ValueError(
														    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
														    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""							)

							if model_args.model_name_or_path:
														lowerCamelCase_					=       AutoModelForMaskedLM.from_pretrained(
														    model_args.model_name_or_path      ,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path							)      ,					config=UpperCAmelCase__      ,					cache_dir=model_args.cache_dir      ,					revision=model_args.model_revision      ,					use_auth_token=True if model_args.use_auth_token else None      ,					)
							else:
														logger.info("""Training new model from scratch"""							)
														lowerCamelCase_					=       AutoModelForMaskedLM.from_config(UpperCAmelCase__							)

							model.resize_token_embeddings(len(UpperCAmelCase__							)							)

							# Preprocessing the datasets.
							# First we tokenize all the texts.
							if training_args.do_train:
														lowerCamelCase_					=       datasets["""train"""].column_names
							else:
														lowerCamelCase_					=       datasets["""validation"""].column_names
							lowerCamelCase_					=       """text""" if """text""" in column_names else column_names[0]

							lowerCamelCase_					=       """max_length""" if data_args.pad_to_max_length else False

							def tokenize_function(UpperCAmelCase__					:   Any							):
														# Remove empty lines
														lowerCamelCase_					=       [line for line in examples["""text"""] if len(UpperCAmelCase__							) > 0 and not line.isspace()]
														return tokenizer(examples["""text"""]      ,					padding=UpperCAmelCase__      ,					truncation=UpperCAmelCase__      ,					max_length=data_args.max_seq_length							)

							lowerCamelCase_					=       datasets.map(
							    UpperCAmelCase__      ,					batched=UpperCAmelCase__      ,					num_proc=data_args.preprocessing_num_workers      ,					remove_columns=[text_column_name]      ,					load_from_cache_file=not data_args.overwrite_cache      ,					)

							# Add the chinese references if provided
							if data_args.train_ref_file is not None:
														lowerCamelCase_					=       add_chinese_references(tokenized_datasets["""train"""]      ,					data_args.train_ref_file							)
							if data_args.validation_ref_file is not None:
														lowerCamelCase_					=       add_chinese_references(
														    tokenized_datasets["""validation"""]      ,					data_args.validation_ref_file							)
							# If we have ref files, need to avoid it removed by trainer
							lowerCamelCase_					=       data_args.train_ref_file or data_args.validation_ref_file
							if has_ref:
														lowerCamelCase_					=       False

							# Data collator
							# This one will take care of randomly masking the tokens.
							lowerCamelCase_					=       DataCollatorForWholeWordMask(tokenizer=UpperCAmelCase__      ,					mlm_probability=data_args.mlm_probability							)

							# Initialize our Trainer
							lowerCamelCase_					=       Trainer(
							    model=UpperCAmelCase__      ,					args=UpperCAmelCase__      ,					train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None      ,					eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None      ,					tokenizer=UpperCAmelCase__      ,					data_collator=UpperCAmelCase__      ,					)

							# Training
							if training_args.do_train:
														if last_checkpoint is not None:
																					lowerCamelCase_					=       last_checkpoint
														elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path							):
																					lowerCamelCase_					=       model_args.model_name_or_path
														else:
																					lowerCamelCase_					=       None
														lowerCamelCase_					=       trainer.train(resume_from_checkpoint=UpperCAmelCase__							)
														trainer.save_model()  # Saves the tokenizer too for easy upload

														lowerCamelCase_					=       os.path.join(training_args.output_dir      ,					"""train_results.txt"""							)
														if trainer.is_world_process_zero():
																					with open(UpperCAmelCase__      ,					"""w"""							) as writer:
																												logger.info("""***** Train results *****"""							)
																												for key, value in sorted(train_result.metrics.items()							):
																																			logger.info(F'''  {key} = {value}'''							)
																																			writer.write(F'''{key} = {value}\n'''							)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																					trainer.state.save_to_json(os.path.join(training_args.output_dir      ,					"""trainer_state.json"""							)							)

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

														lowerCamelCase_					=       trainer.evaluate()

														lowerCamelCase_					=       math.exp(eval_output["""eval_loss"""]							)
														lowerCamelCase_					=       perplexity

														lowerCamelCase_					=       os.path.join(training_args.output_dir      ,					"""eval_results_mlm_wwm.txt"""							)
														if trainer.is_world_process_zero():
																					with open(UpperCAmelCase__      ,					"""w"""							) as writer:
																												logger.info("""***** Eval results *****"""							)
																												for key, value in sorted(results.items()							):
																																			logger.info(F'''  {key} = {value}'''							)
																																			writer.write(F'''{key} = {value}\n'''							)

							return results



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[Any]							)	->     Optional[Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()





