




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,
)


_A     =			logging.getLogger(__name__)


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







@dataclass
class  A		:
		__snake_case									=   field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}     )
		__snake_case									=   field(
		    default='summarization'      ,				metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}      ,				)
		__snake_case									=   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.'
		        )
		    }      ,				)
		__snake_case									=   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.'
		        )
		    }      ,				)
		__snake_case									=   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``.'
		        )
		    }      ,				)
		__snake_case									=   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.'
		        )
		    }      ,				)
		__snake_case									=   field(default=-1      ,				metadata={'help': '# training examples. -1 means use all.'}     )
		__snake_case									=   field(default=-1      ,				metadata={'help': '# validation examples. -1 means use all.'}     )
		__snake_case									=   field(default=-1      ,				metadata={'help': '# test examples. -1 means use all.'}     )
		__snake_case									=   field(default=__UpperCAmelCase      ,				metadata={'help': 'Source language id for translation.'}     )
		__snake_case									=   field(default=__UpperCAmelCase      ,				metadata={'help': 'Target language id for translation.'}     )
		__snake_case									=   field(default=__UpperCAmelCase      ,				metadata={'help': '# num_beams to use for evaluation.'}     )
		__snake_case									=   field(
		    default=__UpperCAmelCase      ,				metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}      ,				)







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





def __UpperCamelCase			(       ):
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						lowerCAmelCase_      			=  HfArgumentParser((ModelArguments, DataTrainingArguments, 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.
												lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_      			=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
						else:
												lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_      			=  parser.parse_args_into_dataclasses()

						check_output_dir(_A  )

						# 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''' ,  _A  )

						# 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.

						lowerCAmelCase_      			=  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 ,  )

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

						lowerCAmelCase_      			=  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 ,  )
						lowerCAmelCase_      			=  AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path ,  from_tf='''.ckpt''' in model_args.model_name_or_path ,  config=_A ,  cache_dir=model_args.cache_dir ,  )

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

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

						# set decoder_start_token_id for MBart
						if model.config.decoder_start_token_id is None and isinstance(_A ,  (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(_A ,  _A  ):
																		lowerCAmelCase_      			=  tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		lowerCAmelCase_      			=  tokenizer.convert_tokens_to_ids(data_args.tgt_lang  )

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

						lowerCAmelCase_      			=  SeqaSeqDataset

						# Get datasets
						lowerCAmelCase_      			=  (
						    dataset_class(
						        _A ,  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
						)
						lowerCAmelCase_      			=  (
						    dataset_class(
						        _A ,  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
						)
						lowerCAmelCase_      			=  (
						    dataset_class(
						        _A ,  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
						lowerCAmelCase_      			=  (
						    build_compute_metrics_fn(data_args.task ,  _A  ) if training_args.predict_with_generate else None
						)
						lowerCAmelCase_      			=  SeqaSeqTrainer(
						    model=_A ,  args=_A ,  data_args=_A ,  train_dataset=_A ,  eval_dataset=_A ,  data_collator=SeqaSeqDataCollator(
						        _A ,  _A ,  model.config.decoder_start_token_id ,  training_args.tpu_num_cores  ) ,  compute_metrics=_A ,  tokenizer=_A ,  )

						lowerCAmelCase_      			=  {}
						# Training
						if training_args.do_train:
												logger.info('''*** Train ***'''  )

												lowerCAmelCase_      			=  trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
												lowerCAmelCase_      			=  train_result.metrics
												lowerCAmelCase_      			=  data_args.n_train

												trainer.save_model()  # this also saves the tokenizer

												if trainer.is_world_process_zero():
																		handle_metrics('''train''' ,  _A ,  training_args.output_dir  )
																		all_metrics.update(_A  )

																		# 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 ***'''  )

												lowerCAmelCase_      			=  trainer.evaluate(metric_key_prefix='''val'''  )
												lowerCAmelCase_      			=  data_args.n_val
												lowerCAmelCase_      			=  round(metrics['''val_loss'''] ,  4  )

												if trainer.is_world_process_zero():
																		handle_metrics('''val''' ,  _A ,  training_args.output_dir  )
																		all_metrics.update(_A  )

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

												lowerCAmelCase_      			=  trainer.predict(test_dataset=_A ,  metric_key_prefix='''test'''  )
												lowerCAmelCase_      			=  test_output.metrics
												lowerCAmelCase_      			=  data_args.n_test

												if trainer.is_world_process_zero():
																		lowerCAmelCase_      			=  round(metrics['''test_loss'''] ,  4  )
																		handle_metrics('''test''' ,  _A ,  training_args.output_dir  )
																		all_metrics.update(_A  )

																		if training_args.predict_with_generate:
																								lowerCAmelCase_      			=  tokenizer.batch_decode(
																								    test_output.predictions ,  skip_special_tokens=_A ,  clean_up_tokenization_spaces=_A  )
																								lowerCAmelCase_      			=  lmap(str.strip ,  _A  )
																								write_txt_file(_A ,  os.path.join(training_args.output_dir ,  '''test_generations.txt'''  )  )

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

						return all_metrics





def __UpperCamelCase			(       _A  ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()










from functools import lru_cache





@lru_cache
def __UpperCamelCase			(       _A  ):
						if num < 0:
												raise ValueError('''Number should not be negative.'''  )

						return 1 if num in (0, 1) else num * factorial(num - 1  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




