'''simple docstring'''




import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowercase						: List[str]						=						logging.getLogger(__name__)


_lowercase						: List[Any]						=						list(MODEL_WITH_LM_HEAD_MAPPING.keys())
_lowercase						: Union[str, Any]						=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={
			        '''help''': (
			            '''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
			            ''' scratch.'''
			        )
			    },     )
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_UpperCAmelCase)},     )
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},     )





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={'''help''': '''The input training data file (a text file).'''})
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={
			        '''help''': (
			            '''The input training data files (multiple files in glob format). '''
			            '''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
			        )
			    },     )
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''},     )
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''},     )
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''},     )
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''},     )

			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''})
			UpperCamelCase__				=       field(default=_UpperCAmelCase,     metadata={'''help''': '''Whether ot not to use whole word mask.'''})
			UpperCamelCase__				=       field(
			    default=0.15,     metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''})
			UpperCamelCase__				=       field(
			    default=1 / 6,     metadata={
			        '''help''': (
			            '''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
			            ''' modeling.'''
			        )
			    },     )
			UpperCamelCase__				=       field(
			    default=5,     metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''})

			UpperCamelCase__				=       field(
			    default=-1,     metadata={
			        '''help''': (
			            '''Optional input sequence length after tokenization.'''
			            '''The training dataset will be truncated in block of this size for training.'''
			            '''Default to the model max input length for single sentence inputs (take into account special tokens).'''
			        )
			    },     )
			UpperCamelCase__				=       field(
			    default=_UpperCAmelCase,     metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})




def 							lowerCamelCase	(      UpperCAmelCase__       :					DataTrainingArguments      ,      UpperCAmelCase__       :					PreTrainedTokenizer      ,      UpperCAmelCase__       :					bool = False      ,      UpperCAmelCase__       :					Optional[str] = None      ,      )			->      Tuple:
							def _dataset(UpperCAmelCase__       :					Tuple      ,      UpperCAmelCase__       :					Tuple=None ):
														if args.line_by_line:
																					if ref_path is not None:
																												if not args.whole_word_mask or not args.mlm:
																																			raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
																												return LineByLineWithRefDataset(
																												    tokenizer=UpperCAmelCase__      ,      file_path=UpperCAmelCase__      ,      block_size=args.block_size      ,      ref_path=UpperCAmelCase__      ,      )

																					return LineByLineTextDataset(tokenizer=UpperCAmelCase__      ,      file_path=UpperCAmelCase__      ,      block_size=args.block_size )
														else:
																					return TextDataset(
																					    tokenizer=UpperCAmelCase__      ,      file_path=UpperCAmelCase__      ,      block_size=args.block_size      ,      overwrite_cache=args.overwrite_cache      ,      cache_dir=UpperCAmelCase__      ,      )

							if evaluate:
														return _dataset(args.eval_data_file      ,      args.eval_ref_file )
							elif args.train_data_files:
														return ConcatDataset([_dataset(UpperCAmelCase__ ) for f in glob(args.train_data_files )] )
							else:
														return _dataset(args.train_data_file      ,      args.train_ref_file )




def 							lowerCamelCase	(      )			->      List[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.

							lowercase_					:			Optional[int]										=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
							lowercase_						,						lowercase_						,						lowercase_					:			Union[str, Any]										=       parser.parse_args_into_dataclasses()

							if data_args.eval_data_file is None and training_args.do_eval:
														raise ValueError(
														    """Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
														    """or remove the --do_eval argument.""" )
							if (
							    os.path.exists(training_args.output_dir )
							    and os.listdir(training_args.output_dir )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
														    """ --overwrite_output_dir to overcome.""" )

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

							# Set seed
							set_seed(training_args.seed )

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

							if model_args.config_name:
														lowercase_					:			List[str]										=       AutoConfig.from_pretrained(model_args.config_name      ,      cache_dir=model_args.cache_dir )
							elif model_args.model_name_or_path:
														lowercase_					:			Tuple										=       AutoConfig.from_pretrained(model_args.model_name_or_path      ,      cache_dir=model_args.cache_dir )
							else:
														lowercase_					:			Union[str, Any]										=       CONFIG_MAPPING[model_args.model_type]()
														logger.warning("""You are instantiating a new config instance from scratch.""" )

							if model_args.tokenizer_name:
														lowercase_					:			str										=       AutoTokenizer.from_pretrained(model_args.tokenizer_name      ,      cache_dir=model_args.cache_dir )
							elif model_args.model_name_or_path:
														lowercase_					:			int										=       AutoTokenizer.from_pretrained(model_args.model_name_or_path      ,      cache_dir=model_args.cache_dir )
							else:
														raise ValueError(
														    """You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
														    """ script, save it,and load it from here, using --tokenizer_name""" )

							if model_args.model_name_or_path:
														lowercase_					:			List[Any]										=       AutoModelWithLMHead.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      ,      )
							else:
														logger.info("""Training new model from scratch""" )
														lowercase_					:			Any										=       AutoModelWithLMHead.from_config(UpperCAmelCase__ )

							model.resize_token_embeddings(len(UpperCAmelCase__ ) )

							if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
														raise ValueError(
														    """BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
														    """--mlm flag (masked language modeling).""" )

							if data_args.block_size <= 0:
														lowercase_					:			int										=       tokenizer.max_len
														# Our input block size will be the max possible for the model
							else:
														lowercase_					:			Tuple										=       min(data_args.block_size      ,      tokenizer.max_len )

							# Get datasets

							lowercase_					:			List[str]										=       (
							    get_dataset(UpperCAmelCase__      ,      tokenizer=UpperCAmelCase__      ,      cache_dir=model_args.cache_dir ) if training_args.do_train else None
							)
							lowercase_					:			Tuple										=       (
							    get_dataset(UpperCAmelCase__      ,      tokenizer=UpperCAmelCase__      ,      evaluate=UpperCAmelCase__      ,      cache_dir=model_args.cache_dir )
							    if training_args.do_eval
							    else None
							)
							if config.model_type == "xlnet":
														lowercase_					:			Tuple										=       DataCollatorForPermutationLanguageModeling(
														    tokenizer=UpperCAmelCase__      ,      plm_probability=data_args.plm_probability      ,      max_span_length=data_args.max_span_length      ,      )
							else:
														if data_args.mlm and data_args.whole_word_mask:
																					lowercase_					:			Dict										=       DataCollatorForWholeWordMask(
																					    tokenizer=UpperCAmelCase__      ,      mlm_probability=data_args.mlm_probability )
														else:
																					lowercase_					:			Union[str, Any]										=       DataCollatorForLanguageModeling(
																					    tokenizer=UpperCAmelCase__      ,      mlm=data_args.mlm      ,      mlm_probability=data_args.mlm_probability )

    # Initialize our Trainer
							lowercase_					:			Optional[Any]										=       Trainer(
							    model=UpperCAmelCase__      ,      args=UpperCAmelCase__      ,      data_collator=UpperCAmelCase__      ,      train_dataset=UpperCAmelCase__      ,      eval_dataset=UpperCAmelCase__      ,      prediction_loss_only=UpperCAmelCase__      ,      )

							# Training
							if training_args.do_train:
														lowercase_					:			Union[str, Any]										=       (
														    model_args.model_name_or_path
														    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
														    else None
														)
														trainer.train(model_path=UpperCAmelCase__ )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
							lowercase_					:			List[Any]										=       {}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***""" )

														lowercase_					:			Optional[Any]										=       trainer.evaluate()

														lowercase_					:			int										=       math.exp(eval_output["""eval_loss"""] )
														lowercase_					:			List[str]										=       {"""perplexity""": perplexity}

														lowercase_					:			List[Any]										=       os.path.join(training_args.output_dir      ,      """eval_results_lm.txt""" )
														if trainer.is_world_master():
																					with open(UpperCAmelCase__      ,      """w""" ) as writer:
																												logger.info("""***** Eval results *****""" )
																												for key in sorted(result.keys() ):
																																			logger.info("""  %s = %s"""      ,      UpperCAmelCase__      ,      str(result[key] ) )
																																			writer.write("""%s = %s\n""" % (key, str(result[key] )) )

														results.update(UpperCAmelCase__ )

							return results




def 							lowerCamelCase	(      UpperCAmelCase__       :					int )			->      Optional[int]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
						main()






'''simple docstring'''




import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler





class     __magic_name__						:







			def __init__(	self				:  str				,			lowercase_				:  Dict    ):
										if isinstance(lowercase_				,			lowercase_    ):
																	# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																	# modified it, it will not be accepted here again, since `auto` values would have been overridden
																	lowercase_					:			List[Any]										=       deepcopy(lowercase_    )
										elif os.path.exists(lowercase_    ):
																	with io.open(lowercase_				,			"""r"""				,			encoding="""utf-8"""    ) as f:
																								lowercase_					:			Union[str, Any]										=       json.load(lowercase_    )
										else:
																	try:
																								lowercase_					:			int										=       baseaa.urlsafe_baadecode(lowercase_    ).decode("""utf-8"""    )
																								lowercase_					:			str										=       json.loads(lowercase_    )
																	except (UnicodeDecodeError, AttributeError, ValueError):
																								raise ValueError(
																								    f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''    )

										lowercase_					:			Any										=       config

										self.set_stage_and_offload()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										# zero stage - this is done as early as possible, before model is created, to allow
										# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
										# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
										lowercase_					:			Tuple										=       self.get_value("""zero_optimization.stage"""				,			-1    )

										# offload
										lowercase_					:			str										=       False
										if self.is_zeroa() or self.is_zeroa():
																	lowercase_					:			Dict										=       set(["""cpu""", """nvme"""]    )
																	lowercase_					:			List[Any]										=       set(
																	    [
																	        self.get_value("""zero_optimization.offload_optimizer.device"""    ),
																	        self.get_value("""zero_optimization.offload_param.device"""    ),
																	    ]    )
																	if len(offload_devices & offload_devices_valid    ) > 0:
																								lowercase_					:			Tuple										=       True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Any    ):
										lowercase_					:			Optional[Any]										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Tuple										=       ds_key_long.split("""."""    )
										lowercase_					:			Union[str, Any]										=       nodes.pop()
										for node in nodes:
																	lowercase_					:			List[str]										=       config.get(lowercase_    )
																	if config is None:
																								return None, ds_key

										return config, ds_key







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  List[str]				,			lowercase_				:  List[str]=None    ):
										lowercase_						,						lowercase_					:			List[Any]										=       self.find_config_node(lowercase_    )
										if config is None:
																	return default
										return config.get(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[int]				,			lowercase_				:  int=False    ):
										lowercase_					:			int										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Dict										=       ds_key_long.split("""."""    )
										for node in nodes:
																	lowercase_					:			List[Any]										=       config
																	lowercase_					:			Dict										=       config.get(lowercase_    )
																	if config is None:
																								if must_exist:
																															raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''    )
																								else:
																															return

        # if found remove it
										if parent_config is not None:
																	parent_config.pop(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  Tuple    ):
										lowercase_					:			str										=       self.get_value(lowercase_    )
										return False if value is None else bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Union[str, Any]										=       self.get_value(lowercase_    )
										return False if value is None else not bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										return self._stage == 2







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										return self._stage == 3







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return self._offload





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Any										=       engine







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  int				,			**lowercase_				:  str    ):
										# runs backpropagation and handles mixed precision
										self.engine.backward(lowercase_				,			**lowercase_    )

										# Deepspeed's `engine.step` performs the following operations:
										# - gradient accumulation check
										# - gradient clipping
										# - optimizer step
										# - zero grad
										# - checking overflow
										# - lr_scheduler step (only if engine.lr_scheduler is not None)
										self.engine.step()
										# and this plugin overrides the above calls with no-ops when Accelerate runs under
										# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
										# training loop that works transparently under many training regimes.





class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			device_placement=lowercase_				,			scaler=lowercase_    )
										lowercase_					:			Any										=       hasattr(self.optimizer				,			"""overflow"""    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Tuple=None    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										if self.__has_overflow__:
																	return self.optimizer.overflow
										return False





class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Dict				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]=0.0_01				,			lowercase_				:  List[str]=0				,			**lowercase_				:  List[Any]    ):
										lowercase_					:			str										=       params
										lowercase_					:			List[Any]										=       lr
										lowercase_					:			int										=       weight_decay
										lowercase_					:			Union[str, Any]										=       kwargs





class     __magic_name__						:







			def __init__(	self				:  Tuple				,			lowercase_				:  Optional[Any]				,			lowercase_				:  List[str]=None				,			lowercase_				:  int=0				,			**lowercase_				:  int    ):
										lowercase_					:			Union[str, Any]										=       optimizer
										lowercase_					:			List[str]										=       total_num_steps
										lowercase_					:			Dict										=       warmup_num_steps
										lowercase_					:			Dict										=       kwargs





