



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__   :					str 						=      logging.getLogger(__name__)



@dataclass
class    UpperCAmelCase__:

   '''simple docstring'''




   A     :	str												=     field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
   A     :	Optional[str]												=     field(
       default=lowerCamelCase  ,				metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
   A     :	Optional[str]												=     field(
       default=lowerCamelCase  ,				metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
   A     :	Optional[str]												=     field(
       default=lowerCamelCase  ,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,				)
   A     :	bool												=     field(default=lowerCamelCase  ,				metadata={"help": "Whether tp freeze the encoder."}						)
   A     :	bool												=     field(default=lowerCamelCase  ,				metadata={"help": "Whether  to freeze the embeddings."}						)



@dataclass
class    UpperCAmelCase__:

   '''simple docstring'''




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

def 	_lowerCAmelCase (    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 	_lowerCAmelCase (    ):
      # 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, 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__  =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
      else:
            lowercase__,						lowercase__,						lowercase__  =	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.

      lowercase__  =	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__  =	('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__						)						)

      lowercase__  =	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__  =	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:
            lowercase__  =	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__						):
                  lowercase__  =	tokenizer.lang_code_to_id[data_args.tgt_lang]
            else:
                  lowercase__  =	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()						)

      lowercase__  =	SeqaSeqDataset

      # Get datasets
      lowercase__  =	(
          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
      )
      lowercase__  =	(
          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
      )
      lowercase__  =	(
          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
      lowercase__  =	(
          build_compute_metrics_fn(data_args.task    ,	A__						) if training_args.predict_with_generate else None
      )
      lowercase__  =	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__    ,	)

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

            lowercase__  =	trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
            lowercase__  =	train_result.metrics
            lowercase__  =	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 ***'						)

            lowercase__  =	trainer.evaluate(metric_key_prefix='val'						)
            lowercase__  =	data_args.n_val
            lowercase__  =	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 ***'						)

            lowercase__  =	trainer.predict(test_dataset=A__    ,	metric_key_prefix='test'						)
            lowercase__  =	test_output.metrics
            lowercase__  =	data_args.n_test

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

                  if training_args.predict_with_generate:
                        lowercase__  =	tokenizer.batch_decode(
                            test_output.predictions    ,	skip_special_tokens=A__    ,	clean_up_tokenization_spaces=A__						)
                        lowercase__  =	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 	_lowerCAmelCase (    A__						):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
       main()





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 	_lowerCAmelCase (    A__=None						):
      if subparsers is not None:
            lowercase__  =	subparsers.add_parser('test'						)
      else:
            lowercase__  =	argparse.ArgumentParser('Accelerate test command'						)

      parser.add_argument(
          '--config_file'    ,	default=A__    ,	help=(
              'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
              'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
              'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
              'with \'huggingface\'.'
          )    ,	)

      if subparsers is not None:
            parser.set_defaults(func=A__						)
      return parser

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	os.path.sep.join(__file__.split(os.path.sep						)[:-2] + ['test_utils', 'scripts', 'test_script.py']						)

      if args.config_file is None:
            lowercase__  =	script_name
      else:
            lowercase__  =	F'''--config_file={args.config_file} {script_name}'''

      lowercase__  =	['accelerate-launch'] + test_args.split()
      lowercase__  =	execute_subprocess_async(A__    ,	env=os.environ.copy()						)
      if result.returncode == 0:
            print('Test is a success! You are ready for your distributed training!'						)

def 	_lowerCAmelCase (    ):
      lowercase__  =	test_command_parser()
      lowercase__  =	parser.parse_args()
      test_command(A__						)


if __name__ == "__main__":
       main()
