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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCamelCase_						:     Optional[int]	       =							logging.getLogger(__name__)




@dataclass
class   __lowercase						:
	_A       =   field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "Pretrained config name or path if not the same as model_name"}       )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}       )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   , )
	_A       =   field(default=__snake_case   , metadata={"help": "Whether tp freeze the encoder."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "Whether  to freeze the embeddings."}       )




@dataclass
class   __lowercase						:
	_A       =   field(
	    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}       )
	_A       =   field(
	    default="summarization"   , metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}   , )
	_A       =   field(
	    default=1024   , metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }   , )
	_A       =   field(
	    default=128   , metadata={
	        "help": (
	            "The maximum total sequence length for target text after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }   , )
	_A       =   field(
	    default=142   , metadata={
	        "help": (
	            "The maximum total sequence length for validation target text after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded. "
	            "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
	            "during ``evaluate`` and ``predict``."
	        )
	    }   , )
	_A       =   field(
	    default=142   , metadata={
	        "help": (
	            "The maximum total sequence length for test target text after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }   , )
	_A       =   field(default=-1   , metadata={"help": "# training examples. -1 means use all."}       )
	_A       =   field(default=-1   , metadata={"help": "# validation examples. -1 means use all."}       )
	_A       =   field(default=-1   , metadata={"help": "# test examples. -1 means use all."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "Source language id for translation."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "Target language id for translation."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "# num_beams to use for evaluation."}       )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}   , )



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   Any ,			_UpperCAmelCase  :   Tuple  )   ->			str:






						'''simple docstring'''

						logger.info(f"""***** {split} metrics *****"""  )
						for key in sorted(metrics.keys()  ):
												logger.info(f"""  {key} = {metrics[key]}"""  )
						save_json(_UpperCAmelCase ,			os.path.join(_UpperCAmelCase ,			f"""{split}_results.json"""  )  )



def 		UpperCamelCase     (       )   ->			Optional[Any]:






						'''simple docstring'''

						_lowercase       :	int           =   HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)  )

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

						check_output_dir(_UpperCAmelCase  )

						# 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.parallel_mode == ParallelMode.DISTRIBUTED  ) ,			training_args.fpaa ,			)
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
						# 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()
						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.

						_lowercase       :	Dict           =   AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,			cache_dir=model_args.cache_dir ,			)

						_lowercase       :	List[Any]           =   ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
						for p in extra_model_params:
												if getattr(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ):
																		assert hasattr(_UpperCAmelCase ,			_UpperCAmelCase  ), f"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
																		setattr(_UpperCAmelCase ,			_UpperCAmelCase ,			getattr(_UpperCAmelCase ,			_UpperCAmelCase  )  )

						_lowercase       :	Optional[Any]           =   AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,			cache_dir=model_args.cache_dir ,			)
						_lowercase       :	str           =   AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path ,			from_tf=".ckpt" in model_args.model_name_or_path ,			config=_UpperCAmelCase ,			cache_dir=model_args.cache_dir ,			)

						# use task specific params
						use_task_specific_params(_UpperCAmelCase ,			data_args.task  )

						# set num_beams for evaluation
						if data_args.eval_beams is None:
												_lowercase       :	str           =   model.config.num_beams

						# set decoder_start_token_id for MBart
						if model.config.decoder_start_token_id is None and isinstance(_UpperCAmelCase ,			(MBartTokenizer, MBartTokenizerFast)  ):
												assert (
												    data_args.tgt_lang is not None and data_args.src_lang is not None
												), "mBart requires --tgt_lang and --src_lang"
												if isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ):
																		_lowercase       :	Any           =   tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		_lowercase       :	Tuple           =   tokenizer.convert_tokens_to_ids(data_args.tgt_lang  )

						if model_args.freeze_embeds:
												freeze_embeds(_UpperCAmelCase  )
						if model_args.freeze_encoder:
												freeze_params(model.get_encoder()  )
												assert_all_frozen(model.get_encoder()  )

						_lowercase       :	Tuple           =   SeqaSeqDataset

						# Get datasets
						_lowercase       :	List[str]           =   (
						    dataset_class(
						        _UpperCAmelCase ,			type_path="train" ,			data_dir=data_args.data_dir ,			n_obs=data_args.n_train ,			max_target_length=data_args.max_target_length ,			max_source_length=data_args.max_source_length ,			prefix=model.config.prefix or "" ,			)
						    if training_args.do_train
						    else None
						)
						_lowercase       :	Tuple           =   (
						    dataset_class(
						        _UpperCAmelCase ,			type_path="val" ,			data_dir=data_args.data_dir ,			n_obs=data_args.n_val ,			max_target_length=data_args.val_max_target_length ,			max_source_length=data_args.max_source_length ,			prefix=model.config.prefix or "" ,			)
						    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
						    else None
						)
						_lowercase       :	List[Any]           =   (
						    dataset_class(
						        _UpperCAmelCase ,			type_path="test" ,			data_dir=data_args.data_dir ,			n_obs=data_args.n_test ,			max_target_length=data_args.test_max_target_length ,			max_source_length=data_args.max_source_length ,			prefix=model.config.prefix or "" ,			)
						    if training_args.do_predict
						    else None
						)

						# Initialize our Trainer
						_lowercase       :	Union[str, Any]           =   (
						    build_compute_metrics_fn(data_args.task ,			_UpperCAmelCase  ) if training_args.predict_with_generate else None
						)
						_lowercase       :	Tuple           =   SeqaSeqTrainer(
						    model=_UpperCAmelCase ,			args=_UpperCAmelCase ,			data_args=_UpperCAmelCase ,			train_dataset=_UpperCAmelCase ,			eval_dataset=_UpperCAmelCase ,			data_collator=SeqaSeqDataCollator(
						        _UpperCAmelCase ,			_UpperCAmelCase ,			model.config.decoder_start_token_id ,			training_args.tpu_num_cores  ) ,			compute_metrics=_UpperCAmelCase ,			tokenizer=_UpperCAmelCase ,			)

						_lowercase       :	str           =   {}
						# Training
						if training_args.do_train:
												logger.info("*** Train ***"  )

												_lowercase       :	List[Any]           =   trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
												_lowercase       :	List[Any]           =   train_result.metrics
												_lowercase       :	List[str]           =   data_args.n_train

												trainer.save_model()  # this also saves the tokenizer

												if trainer.is_world_process_zero():
																		handle_metrics("train" ,			_UpperCAmelCase ,			training_args.output_dir  )
																		all_metrics.update(_UpperCAmelCase  )

																		# 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"  )  )

																		# For convenience, we also re-save the tokenizer to the same directory,
																		# so that you can share your model easily on huggingface.co/models =)
																		tokenizer.save_pretrained(training_args.output_dir  )

    # Evaluation
						if training_args.do_eval:
												logger.info("*** Evaluate ***"  )

												_lowercase       :	Tuple           =   trainer.evaluate(metric_key_prefix="val"  )
												_lowercase       :	Any           =   data_args.n_val
												_lowercase       :	Dict           =   round(metrics["val_loss"] ,			4  )

												if trainer.is_world_process_zero():
																		handle_metrics("val" ,			_UpperCAmelCase ,			training_args.output_dir  )
																		all_metrics.update(_UpperCAmelCase  )

						if training_args.do_predict:
												logger.info("*** Predict ***"  )

												_lowercase       :	Optional[int]           =   trainer.predict(test_dataset=_UpperCAmelCase ,			metric_key_prefix="test"  )
												_lowercase       :	List[Any]           =   test_output.metrics
												_lowercase       :	List[Any]           =   data_args.n_test

												if trainer.is_world_process_zero():
																		_lowercase       :	List[Any]           =   round(metrics["test_loss"] ,			4  )
																		handle_metrics("test" ,			_UpperCAmelCase ,			training_args.output_dir  )
																		all_metrics.update(_UpperCAmelCase  )

																		if training_args.predict_with_generate:
																								_lowercase       :	Tuple           =   tokenizer.batch_decode(
																								    test_output.predictions ,			skip_special_tokens=_UpperCAmelCase ,			clean_up_tokenization_spaces=_UpperCAmelCase  )
																								_lowercase       :	Optional[Any]           =   lmap(str.strip ,			_UpperCAmelCase  )
																								write_txt_file(_UpperCAmelCase ,			os.path.join(training_args.output_dir ,			"test_generations.txt"  )  )

						if trainer.is_world_process_zero():
												save_json(_UpperCAmelCase ,			os.path.join(training_args.output_dir ,			"all_results.json"  )  )

						return all_metrics



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






						'''simple docstring'''

						main()


if __name__ == "__main__":
							main()






import functools
from typing import Any



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str]  )   ->			bool:






						'''simple docstring'''

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or len(_UpperCAmelCase  ) == 0:
												raise ValueError("the string should be not empty string"  )

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or not all(
						    isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) and len(_UpperCAmelCase  ) > 0 for item in words  ):
												raise ValueError("the words should be a list of non-empty strings"  )

						# Build trie
						_lowercase       :	dict[str, Any]           =   {}
						_lowercase       :	List[Any]           =   "WORD_KEEPER"

						for word in words:
												_lowercase       :	Optional[Any]           =   trie
												for c in word:
																		if c not in trie_node:
																								_lowercase       :	List[Any]           =   {}

																		_lowercase       :	Optional[int]           =   trie_node[c]

												_lowercase       :	str           =   True

						_lowercase       :	int           =   len(_UpperCAmelCase  )

						# Dynamic programming method
						@functools.cache
						def is_breakable(_UpperCAmelCase  :   int  ) -> bool:
												if index == len_string:
																		return True

												_lowercase       :	Union[str, Any]           =   trie
												for i in range(_UpperCAmelCase ,			_UpperCAmelCase  ):
																		_lowercase       :	List[str]           =   trie_node.get(string[i] ,			_UpperCAmelCase  )

																		if trie_node is None:
																								return False

																		if trie_node.get(_UpperCAmelCase ,			_UpperCAmelCase  ) and is_breakable(i + 1  ):
																								return True

												return False

						return is_breakable(0  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




