






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


A__			:    Tuple         =				logging.getLogger(__name__)
A__			:    Any         =				list(MODEL_FOR_MASKED_LM_MAPPING.keys())
A__			:    int         =				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




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




				def        lowercase__     (  self		:					Tuple						):

								'''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    _UpperCAmelCase      :

				"""simple docstring"""




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

								'''simple docstring'''
								if self.train_file is not None:
												lowercase__     				=    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:
												lowercase__     				=    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    (		lowerCamelCase_	,     lowerCamelCase_       ):






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

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






def 						a    (		):






				'''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.

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

				# Detecting last checkpoint.
				lowercase__     				=    None
				if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
								lowercase__     				=    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'''	,     lowerCamelCase_       )

				# 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.
								lowercase__     				=    load_dataset(data_args.dataset_name	,     data_args.dataset_config_name       )
								if "validation" not in datasets.keys():
												lowercase__     				=    load_dataset(
												    data_args.dataset_name	,     data_args.dataset_config_name	,     split=F"""train[:{data_args.validation_split_percentage}%]"""	,     )
												lowercase__     				=    load_dataset(
												    data_args.dataset_name	,     data_args.dataset_config_name	,     split=F"""train[{data_args.validation_split_percentage}%:]"""	,     )
				else:
								lowercase__     				=    {}
								if data_args.train_file is not None:
												lowercase__     				=    data_args.train_file
								if data_args.validation_file is not None:
												lowercase__     				=    data_args.validation_file
								lowercase__     				=    data_args.train_file.split('''.'''       )[-1]
								if extension == "txt":
												lowercase__     				=    '''text'''
								lowercase__     				=    load_dataset(lowerCamelCase_	,     data_files=lowerCamelCase_       )
				# 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.
				lowercase__     				=    {
				    '''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:
								lowercase__     				=    AutoConfig.from_pretrained(model_args.config_name	,     **lowerCamelCase_       )
				elif model_args.model_name_or_path:
								lowercase__     				=    AutoConfig.from_pretrained(model_args.model_name_or_path	,     **lowerCamelCase_       )
				else:
								lowercase__     				=    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}"""       )

				lowercase__     				=    {
				    '''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:
								lowercase__     				=    AutoTokenizer.from_pretrained(model_args.tokenizer_name	,     **lowerCamelCase_       )
				elif model_args.model_name_or_path:
								lowercase__     				=    AutoTokenizer.from_pretrained(model_args.model_name_or_path	,     **lowerCamelCase_       )
				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:
								lowercase__     				=    AutoModelForMaskedLM.from_pretrained(
								    model_args.model_name_or_path	,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path       )	,     config=lowerCamelCase_	,     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'''       )
								lowercase__     				=    AutoModelForMaskedLM.from_config(lowerCamelCase_       )

				model.resize_token_embeddings(len(lowerCamelCase_       )       )

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

				lowercase__     				=    '''max_length''' if data_args.pad_to_max_length else False

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

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

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

				# Initialize our Trainer
				lowercase__     				=    Trainer(
				    model=lowerCamelCase_	,     args=lowerCamelCase_	,     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=lowerCamelCase_	,     data_collator=lowerCamelCase_	,     )

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

								lowercase__     				=    os.path.join(training_args.output_dir	,     '''train_results.txt'''       )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase_	,     '''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
				lowercase__     				=    {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''       )

								lowercase__     				=    trainer.evaluate()

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

								lowercase__     				=    os.path.join(training_args.output_dir	,     '''eval_results_mlm_wwm.txt'''       )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase_	,     '''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    (		lowerCamelCase_       ):






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


if __name__ == "__main__":
							main()












class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					str = "",						lowerCamelCase		:					bool = False						):

								'''simple docstring'''
								# Mapping from the first character of the prefix of the node
								lowercase__     				=    {}

								# A node will be a leaf if the tree contains its word
								lowercase__     				=    is_leaf

								lowercase__     				=    prefix




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    0
								for q, w in zip(self.prefix,						lowerCamelCase						):
												if q != w:
																break

												x += 1

								return self.prefix[:x], self.prefix[x:], word[x:]




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					list[str]						):

								'''simple docstring'''
								for word in words:
												self.insert(lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str						):

								'''simple docstring'''
								# Case 1: If the word is the prefix of the node
								# Solution: We set the current node as leaf
								if self.prefix == word:
												lowercase__     				=    True

								# Case 2: The node has no edges that have a prefix to the word
								# Solution: We create an edge from the current node to a new one
								# containing the word
								elif word[0] not in self.nodes:
												lowercase__     				=    RadixNode(prefix=lowerCamelCase,						is_leaf=lowerCamelCase						)

								else:
												lowercase__     				=    self.nodes[word[0]]
												lowercase__				,		lowercase__				,		lowercase__     				=    incoming_node.match(
												    lowerCamelCase						)

												# Case 3: The node prefix is equal to the matching
												# Solution: We insert remaining word on the next node
												if remaining_prefix == "":
																self.nodes[matching_string[0]].insert(lowerCamelCase						)

												# Case 4: The word is greater equal to the matching
												# Solution: Create a node in between both nodes, change
												# prefixes and add the new node for the remaining word
												else:
																lowercase__     				=    remaining_prefix

																lowercase__     				=    self.nodes[matching_string[0]]
																lowercase__     				=    RadixNode(lowerCamelCase,						lowerCamelCase						)
																lowercase__     				=    aux_node

																if remaining_word == "":
																				lowercase__     				=    True
																else:
																				self.nodes[matching_string[0]].insert(lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    self.nodes.get(word[0],						lowerCamelCase						)
								if not incoming_node:
												return False
								else:
												lowercase__				,		lowercase__				,		lowercase__     				=    incoming_node.match(
												    lowerCamelCase						)
												# If there is remaining prefix, the word can't be on the tree
												if remaining_prefix != "":
																return False
												# This applies when the word and the prefix are equal
												elif remaining_word == "":
																return incoming_node.is_leaf
												# We have word remaining so we check the next node
												else:
																return incoming_node.find(lowerCamelCase						)




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    self.nodes.get(word[0],						lowerCamelCase						)
								if not incoming_node:
												return False
								else:
												lowercase__				,		lowercase__				,		lowercase__     				=    incoming_node.match(
												    lowerCamelCase						)
												# If there is remaining prefix, the word can't be on the tree
												if remaining_prefix != "":
																return False
												# We have word remaining so we check the next node
												elif remaining_word != "":
																return incoming_node.delete(lowerCamelCase						)
												else:
																# If it is not a leaf, we don't have to delete
																if not incoming_node.is_leaf:
																				return False
																else:
																				# We delete the nodes if no edges go from it
																				if len(incoming_node.nodes						) == 0:
																								del self.nodes[word[0]]
																								# We merge the current node with its only child
																								if len(self.nodes						) == 1 and not self.is_leaf:
																												lowercase__     				=    list(self.nodes.values()						)[0]
																												lowercase__     				=    merging_node.is_leaf
																												self.prefix += merging_node.prefix
																												lowercase__     				=    merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																				elif len(incoming_node.nodes						) > 1:
																								lowercase__     				=    False
																				# If there is 1 edge, we merge it with its child
																				else:
																								lowercase__     				=    list(incoming_node.nodes.values()						)[0]
																								lowercase__     				=    merging_node.is_leaf
																								incoming_node.prefix += merging_node.prefix
																								lowercase__     				=    merging_node.nodes

																				return True




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					int = 0						):

								'''simple docstring'''
								if self.prefix != "":
												print('''-''' * height,						self.prefix,						'''  (leaf)''' if self.is_leaf else ''''''						)

								for value in self.nodes.values():
												value.print_tree(height + 1						)






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    '''banana bananas bandana band apple all beast'''.split()
				lowercase__     				=    RadixNode()
				root.insert_many(lowerCamelCase_       )

				assert all(root.find(lowerCamelCase_       ) for word in words       )
				assert not root.find('''bandanas'''       )
				assert not root.find('''apps'''       )
				root.delete('''all'''       )
				assert not root.find('''all'''       )
				root.delete('''banana'''       )
				assert not root.find('''banana'''       )
				assert root.find('''bananas'''       )

				return True






def 						a    (		):






				'''simple docstring'''
				assert test_trie()






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    RadixNode()
				lowercase__     				=    '''banana bananas bandanas bandana band apple all beast'''.split()
				root.insert_many(lowerCamelCase_       )

				print('''Words:'''	,     lowerCamelCase_       )
				print('''Tree:'''       )
				root.print_tree()


if __name__ == "__main__":
							main()




