



import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


UpperCAmelCase__     = logging.getLogger(__name__)

UpperCAmelCase__     = 50  # max width of layer names
UpperCAmelCase__     = 70  # max width of quantizer names





def 							A			(  _UpperCAmelCase       :     Tuple					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     parser.add_argument_group('quant_trainer arguments'					)
			group.add_argument('--wprec'							,     type=_UpperCAmelCase							,     default=8							,     help='weight precision'					)
			group.add_argument('--aprec'							,     type=_UpperCAmelCase							,     default=8							,     help='activation precision'					)
			group.add_argument('--quant-per-tensor'							,     action='store_true'							,     help='per tensor weight scaling'					)
			group.add_argument('--quant-disable'							,     action='store_true'							,     help='disable all quantizers'					)
			group.add_argument('--quant-disable-embeddings'							,     action='store_true'							,     help='disable all embeddings quantizers'					)
			group.add_argument('--quant-disable-keyword'							,     type=_UpperCAmelCase							,     nargs='+'							,     help='disable quantizers by keyword'					)
			group.add_argument('--quant-disable-layer-module'							,     type=_UpperCAmelCase							,     help='disable quantizers by keyword under layer.'					)
			group.add_argument('--quant-enable-layer-module'							,     type=_UpperCAmelCase							,     help='enable quantizers by keyword under layer'					)
			group.add_argument('--calibrator'							,     default='max'							,     help='which quantization range calibrator to use'					)
			group.add_argument('--percentile'							,     default=_UpperCAmelCase							,     type=_UpperCAmelCase							,     help='percentile for PercentileCalibrator'					)
			group.add_argument('--fuse-qkv'							,     action='store_true'							,     help='use the same scale factor for qkv'					)
			group.add_argument('--clip-gelu'							,     metavar='N'							,     type=_UpperCAmelCase							,     help='clip gelu output maximum value to N'					)
			group.add_argument(
			    '--recalibrate-weights'							,     action='store_true'							,     help=(
			        'recalibrate weight amaxes by taking the max of the weights.'
			        ' amaxes will be computed with the current quantization granularity (axis).'
			    )							,     )





def 							A			(  _UpperCAmelCase       :     Tuple					)       ->				Optional[Any]:







			'''simple docstring'''

			if args.calibrator == "max":
						_UpperCAmelCase	 =     'max'
			elif args.calibrator == "percentile":
						if args.percentile is None:
									raise ValueError('Specify --percentile when using percentile calibrator'					)
						_UpperCAmelCase	 =     'histogram'
			elif args.calibrator == "mse":
						_UpperCAmelCase	 =     'histogram'
			else:
						raise ValueError(F"Invalid calibrator {args.calibrator}"					)

			_UpperCAmelCase	 =     QuantDescriptor(num_bits=args.aprec							,     calib_method=_UpperCAmelCase					)
			_UpperCAmelCase	 =     QuantDescriptor(num_bits=args.wprec							,     axis=(None if args.quant_per_tensor else (0,))					)
			quant_nn.QuantLinear.set_default_quant_desc_input(_UpperCAmelCase					)
			quant_nn.QuantLinear.set_default_quant_desc_weight(_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Optional[int]=False							,     _UpperCAmelCase       :     List[Any]=False					)       ->				Optional[int]:







			'''simple docstring'''
			logger.info('Configuring Model for Quantization'					)
			logger.info(F"using quantization package {pytorch_quantization.__file__}"					)

			if not calib:
						if args.quant_disable_embeddings:
									set_quantizer_by_name(_UpperCAmelCase							,     ['embeddings']							,     which='weight'							,     _disabled=_UpperCAmelCase					)

						if args.quant_disable:
									set_quantizer_by_name(_UpperCAmelCase							,     ['']							,     _disabled=_UpperCAmelCase					)

						if args.quant_disable_keyword:
									set_quantizer_by_name(_UpperCAmelCase							,     args.quant_disable_keyword							,     _disabled=_UpperCAmelCase					)

						if args.quant_disable_layer_module:
									set_quantizer_by_name(_UpperCAmelCase							,     [R'layer.\d+.' + args.quant_disable_layer_module]							,     _disabled=_UpperCAmelCase					)

						if args.quant_enable_layer_module:
									set_quantizer_by_name(_UpperCAmelCase							,     [R'layer.\d+.' + args.quant_enable_layer_module]							,     _disabled=_UpperCAmelCase					)

						if args.recalibrate_weights:
									recalibrate_weights(_UpperCAmelCase					)

						if args.fuse_qkv:
									fuse_qkv(_UpperCAmelCase							,     _UpperCAmelCase					)

			if args.clip_gelu:
						clip_gelu(_UpperCAmelCase							,     args.clip_gelu					)

			# if args.local_rank in [-1, 0] and not calib:
			print_quant_summary(_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     int					)       ->				Optional[int]:







			'''simple docstring'''
			logger.info('Enabling Calibration'					)
			for name, module in model.named_modules():
						if name.endswith('_quantizer'					):
									if module._calibrator is not None:
												module.disable_quant()
												module.enable_calib()
									else:
												module.disable()
									logger.info(F"{name:80}: {module}"					)





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Dict					)       ->				str:







			'''simple docstring'''
			logger.info('Loading calibrated amax'					)
			for name, module in model.named_modules():
						if name.endswith('_quantizer'					):
									if module._calibrator is not None:
												if isinstance(module._calibrator							,     calib.MaxCalibrator					):
															module.load_calib_amax()
												else:
															module.load_calib_amax('percentile'							,     percentile=args.percentile					)
												module.enable_quant()
												module.disable_calib()
									else:
												module.enable()
			model.cuda()
			print_quant_summary(_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Any					)       ->				List[Any]:







			'''simple docstring'''

			def fusea(_UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[Any]					):
						for mod in [qq, qk, qv]:
									if not hasattr(_UpperCAmelCase							,     '_amax'					):
												print('          WARNING: NO AMAX BUFFER'					)
												return
						_UpperCAmelCase	 =     qq._amax.detach().item()
						_UpperCAmelCase	 =     qk._amax.detach().item()
						_UpperCAmelCase	 =     qv._amax.detach().item()

						_UpperCAmelCase	 =     max(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)
						qq._amax.fill_(_UpperCAmelCase					)
						qk._amax.fill_(_UpperCAmelCase					)
						qv._amax.fill_(_UpperCAmelCase					)
						logger.info(F"          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}"					)

			for name, mod in model.named_modules():
						if name.endswith('.attention.self'					):
									logger.info(F"FUSE_QKV: {name:{name_width}}"					)
									fusea(mod.matmul_q_input_quantizer							,     mod.matmul_k_input_quantizer							,     mod.matmul_v_input_quantizer					)
									if args.quant_per_tensor:
												fusea(mod.query._weight_quantizer							,     mod.key._weight_quantizer							,     mod.value._weight_quantizer					)





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				str:







			'''simple docstring'''

			for name, mod in model.named_modules():
						if name.endswith('.output.dense'					) and not name.endswith('attention.output.dense'					):
									_UpperCAmelCase	 =     mod._input_quantizer._amax.data.detach().item()
									mod._input_quantizer._amax.data.detach().clamp_(max=_UpperCAmelCase					)
									_UpperCAmelCase	 =     mod._input_quantizer._amax.data.detach().item()
									logger.info(F"CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}"					)





def 							A			(  _UpperCAmelCase       :     str					)       ->				Union[str, Any]:







			'''simple docstring'''

			for name, mod in model.named_modules():
						if hasattr(_UpperCAmelCase							,     '_weight_quantizer'					) and mod._weight_quantizer.axis is not None:
									_UpperCAmelCase	 =     mod.weight.shape[0]
									_UpperCAmelCase	 =     mod._weight_quantizer._amax.detach()
									_UpperCAmelCase	 =     torch.ones(_UpperCAmelCase							,     dtype=amax.dtype							,     device=amax.device					) * amax
									print(F"expanding {name} {amax} -> {mod._weight_quantizer._amax}"					)





def 							A			(  _UpperCAmelCase       :     Dict					)       ->				List[str]:







			'''simple docstring'''

			for name, mod in model.named_modules():
						if hasattr(_UpperCAmelCase							,     '_weight_quantizer'					):
									if not hasattr(mod.weight_quantizer							,     '_amax'					):
												print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER'					)
												continue

									# determine which axes to reduce across
									# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
									_UpperCAmelCase	 =     set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis					)
									_UpperCAmelCase	 =     set(range(len(mod.weight.size()					)					)					) - axis_set
									_UpperCAmelCase	 =     pytorch_quantization.utils.reduce_amax(mod.weight							,     axis=_UpperCAmelCase							,     keepdims=_UpperCAmelCase					).detach()
									logger.info(F"RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"					)
									_UpperCAmelCase	 =     amax





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Union[str, Any]=25							,     _UpperCAmelCase       :     int=180							,     _UpperCAmelCase       :     int=None					)       ->				Tuple:







			'''simple docstring'''

			if ignore is None:
						_UpperCAmelCase	 =     []
			elif not isinstance(_UpperCAmelCase							,     _UpperCAmelCase					):
						_UpperCAmelCase	 =     [ignore]

			_UpperCAmelCase	 =     0
			for name, mod in model.named_modules():
						if not hasattr(_UpperCAmelCase							,     'weight'					):
									continue
						_UpperCAmelCase	 =     max(_UpperCAmelCase							,     len(_UpperCAmelCase					)					)

			for name, mod in model.named_modules():
						_UpperCAmelCase	 =     getattr(_UpperCAmelCase							,     '_input_quantizer'							,     _UpperCAmelCase					)
						_UpperCAmelCase	 =     getattr(_UpperCAmelCase							,     '_weight_quantizer'							,     _UpperCAmelCase					)
						if not hasattr(_UpperCAmelCase							,     'weight'					):
									continue
						if type(_UpperCAmelCase					) in ignore:
									continue
						if [True for s in ignore if type(_UpperCAmelCase					) is str and s in name]:
									continue
						_UpperCAmelCase	 =     F"Act:{input_q.extra_repr()}"
						_UpperCAmelCase	 =     F"Wgt:{weight_q.extra_repr()}"
						_UpperCAmelCase	 =     F"{name:{name_width}} {act_str} {wgt_str}"
						if len(_UpperCAmelCase					) <= line_width:
									logger.info(_UpperCAmelCase					)
						else:
									logger.info(F"{name:{name_width}} {act_str}"					)
									logger.info(F"{'  ':{name_width}} {wgt_str}"					)





def 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				Any:







			'''simple docstring'''
			_UpperCAmelCase	 =     0
			for name, mod in model.named_modules():
						if isinstance(_UpperCAmelCase							,     pytorch_quantization.nn.TensorQuantizer					):
									print(F"{name:80} {mod}"					)
									count += 1
			print(F"{count} TensorQuantizers found in model"					)





def 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     str					)       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     getattr(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)
			if quantizer_mod is not None:
						assert hasattr(_UpperCAmelCase							,     _UpperCAmelCase					)
						setattr(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)
			else:
						logger.warning(F"{name} has no {quantizer}"					)





def 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     Optional[Any]="both"							,     **_UpperCAmelCase       :     Union[str, Any]					)       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     F"Warning: changing {which} quantizers of {name:{qname_width}}"
			for k, v in kwargs.items():
						s += F" {k}={v}"
						if which in ["input", "both"]:
									set_quantizer(_UpperCAmelCase							,     _UpperCAmelCase							,     '_input_quantizer'							,     _UpperCAmelCase							,     _UpperCAmelCase					)
						if which in ["weight", "both"]:
									set_quantizer(_UpperCAmelCase							,     _UpperCAmelCase							,     '_weight_quantizer'							,     _UpperCAmelCase							,     _UpperCAmelCase					)
			logger.info(_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     int							,     **_UpperCAmelCase       :     Optional[Any]					)       ->				Optional[int]:







			'''simple docstring'''

			for name, mod in model.named_modules():
						if hasattr(_UpperCAmelCase							,     '_input_quantizer'					) or hasattr(_UpperCAmelCase							,     '_weight_quantizer'					):
									for n in names:
												if re.search(_UpperCAmelCase							,     _UpperCAmelCase					):
															set_quantizers(_UpperCAmelCase							,     _UpperCAmelCase							,     **_UpperCAmelCase					)
						elif name.endswith('_quantizer'					):
									for n in names:
												if re.search(_UpperCAmelCase							,     _UpperCAmelCase					):
															_UpperCAmelCase	 =     F"Warning: changing {name:{name_width}}"
															for k, v in kwargs.items():
																		s += F" {k}={v}"
																		setattr(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)
															logger.info(_UpperCAmelCase					)





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


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







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



			def 					_lowerCamelCase (		self     :						Any)     ->					Dict:



						"""simple docstring"""






						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     __lowerCAmelCase					:
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}		)
			UpperCamelCase           =  field(default=A       ,		metadata={'''help''': '''The input training data file (a text file).'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)
			UpperCamelCase           =  field(
			    default=5       ,		metadata={
			        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		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.'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The number of processes to use for the preprocessing.'''}       ,		)
			UpperCamelCase           =  field(
			    default=0.15       ,		metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		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 					_lowerCamelCase (		self     :						Dict)     ->					Union[str, Any]:



						"""simple docstring"""






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





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				Optional[Any]:







			'''simple docstring'''
			with open(_UpperCAmelCase							,     'r'							,     encoding='utf-8'					) as f:
						_UpperCAmelCase	 =     [json.loads(_UpperCAmelCase					) for line in f.read().splitlines() if (len(_UpperCAmelCase					) > 0 and not line.isspace())]
			assert len(_UpperCAmelCase					) == len(_UpperCAmelCase					)

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





def 							A			(  )       ->				Optional[Any]:







			'''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.

			_UpperCAmelCase	 =     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.
						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
			else:
						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			_UpperCAmelCase	 =     None
			if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
						_UpperCAmelCase	 =     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.
						_UpperCAmelCase	 =     load_dataset(data_args.dataset_name							,     data_args.dataset_config_name					)
						if "validation" not in datasets.keys():
									_UpperCAmelCase	 =     load_dataset(
									    data_args.dataset_name							,     data_args.dataset_config_name							,     split=F"train[:{data_args.validation_split_percentage}%]"							,     )
									_UpperCAmelCase	 =     load_dataset(
									    data_args.dataset_name							,     data_args.dataset_config_name							,     split=F"train[{data_args.validation_split_percentage}%:]"							,     )
			else:
						_UpperCAmelCase	 =     {}
						if data_args.train_file is not None:
									_UpperCAmelCase	 =     data_args.train_file
						if data_args.validation_file is not None:
									_UpperCAmelCase	 =     data_args.validation_file
						_UpperCAmelCase	 =     data_args.train_file.split('.'					)[-1]
						if extension == "txt":
									_UpperCAmelCase	 =     'text'
						_UpperCAmelCase	 =     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.
			_UpperCAmelCase	 =     {
			    '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:
						_UpperCAmelCase	 =     AutoConfig.from_pretrained(model_args.config_name							,     **_UpperCAmelCase					)
			elif model_args.model_name_or_path:
						_UpperCAmelCase	 =     AutoConfig.from_pretrained(model_args.model_name_or_path							,     **_UpperCAmelCase					)
			else:
						_UpperCAmelCase	 =     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}"					)

			_UpperCAmelCase	 =     {
			    '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:
						_UpperCAmelCase	 =     AutoTokenizer.from_pretrained(model_args.tokenizer_name							,     **_UpperCAmelCase					)
			elif model_args.model_name_or_path:
						_UpperCAmelCase	 =     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:
						_UpperCAmelCase	 =     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'					)
						_UpperCAmelCase	 =     AutoModelForMaskedLM.from_config(_UpperCAmelCase					)

			model.resize_token_embeddings(len(_UpperCAmelCase					)					)

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

			_UpperCAmelCase	 =     'max_length' if data_args.pad_to_max_length else False

			def tokenize_function(_UpperCAmelCase       :     str					):
						# Remove empty lines
						_UpperCAmelCase	 =     [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					)

			_UpperCAmelCase	 =     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:
						_UpperCAmelCase	 =     add_chinese_references(tokenized_datasets['train']							,     data_args.train_ref_file					)
			if data_args.validation_ref_file is not None:
						_UpperCAmelCase	 =     add_chinese_references(
						    tokenized_datasets['validation']							,     data_args.validation_ref_file					)
			# If we have ref files, need to avoid it removed by trainer
			_UpperCAmelCase	 =     data_args.train_ref_file or data_args.validation_ref_file
			if has_ref:
						_UpperCAmelCase	 =     False

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

			# Initialize our Trainer
			_UpperCAmelCase	 =     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:
									_UpperCAmelCase	 =     last_checkpoint
						elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path					):
									_UpperCAmelCase	 =     model_args.model_name_or_path
						else:
									_UpperCAmelCase	 =     None
						_UpperCAmelCase	 =     trainer.train(resume_from_checkpoint=_UpperCAmelCase					)
						trainer.save_model()  # Saves the tokenizer too for easy upload

						_UpperCAmelCase	 =     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
			_UpperCAmelCase	 =     {}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'					)

						_UpperCAmelCase	 =     trainer.evaluate()

						_UpperCAmelCase	 =     math.exp(eval_output['eval_loss']					)
						_UpperCAmelCase	 =     perplexity

						_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     str					)       ->				Optional[Any]:







			'''simple docstring'''
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()
