

'''simple docstring'''
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,
)


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




@dataclass
class 					_snake_case		:
			__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=10_24			,			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=1_28			,			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=1_42			,			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=1_42			,			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=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Source language id for translation."})
			__A  :	Optional[str]        =field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Target language id for translation."})
			__A  :	Optional[int]        =field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "# num_beams to use for evaluation."})
			__A  :	bool        =field(
			    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}			,			)




def  a__       (    _SCREAMING_SNAKE_CASE   :       Optional[Any]	,				_SCREAMING_SNAKE_CASE   :       List[Any]	,				_SCREAMING_SNAKE_CASE   :       str				)     ->					Union[str, Any]:


	"""simple docstring"""





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


def  a__       (    )     ->					Any:


	"""simple docstring"""





	UpperCAmelCase_     :      List[str]						=  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.
		UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
	else:
		UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      int						=  parser.parse_args_into_dataclasses()

	check_output_dir(_SCREAMING_SNAKE_CASE				)

	# 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"	,				_SCREAMING_SNAKE_CASE				)

	# 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.

	UpperCAmelCase_     :      List[str]						=  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	,				)

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

	UpperCAmelCase_     :      Dict						=  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	,				)
	UpperCAmelCase_     :      Union[str, Any]						=  AutoModelForSeqaSeqLM.from_pretrained(
	    model_args.model_name_or_path	,				from_tf=".ckpt" in model_args.model_name_or_path	,				config=_SCREAMING_SNAKE_CASE	,				cache_dir=model_args.cache_dir	,				)

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

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

	# set decoder_start_token_id for MBart
	if model.config.decoder_start_token_id is None and isinstance(_SCREAMING_SNAKE_CASE	,				(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(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				):
			UpperCAmelCase_     :      Dict						=  tokenizer.lang_code_to_id[data_args.tgt_lang]
		else:
			UpperCAmelCase_     :      List[Any]						=  tokenizer.convert_tokens_to_ids(data_args.tgt_lang				)

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

	UpperCAmelCase_     :      Dict						=  SeqaSeqDataset

	# Get datasets
	UpperCAmelCase_     :      Tuple						=  (
	    dataset_class(
	        _SCREAMING_SNAKE_CASE	,				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
	)
	UpperCAmelCase_     :      Dict						=  (
	    dataset_class(
	        _SCREAMING_SNAKE_CASE	,				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
	)
	UpperCAmelCase_     :      int						=  (
	    dataset_class(
	        _SCREAMING_SNAKE_CASE	,				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
	UpperCAmelCase_     :      Optional[Any]						=  (
	    build_compute_metrics_fn(data_args.task	,				_SCREAMING_SNAKE_CASE				) if training_args.predict_with_generate else None
	)
	UpperCAmelCase_     :      List[str]						=  SeqaSeqTrainer(
	    model=_SCREAMING_SNAKE_CASE	,				args=_SCREAMING_SNAKE_CASE	,				data_args=_SCREAMING_SNAKE_CASE	,				train_dataset=_SCREAMING_SNAKE_CASE	,				eval_dataset=_SCREAMING_SNAKE_CASE	,				data_collator=SeqaSeqDataCollator(
	        _SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				model.config.decoder_start_token_id	,				training_args.tpu_num_cores				)	,				compute_metrics=_SCREAMING_SNAKE_CASE	,				tokenizer=_SCREAMING_SNAKE_CASE	,				)

	UpperCAmelCase_     :      List[Any]						=  {}
	# Training
	if training_args.do_train:
		logger.info("*** Train ***"				)

		UpperCAmelCase_     :      Any						=  trainer.train(
		    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
		UpperCAmelCase_     :      int						=  train_result.metrics
		UpperCAmelCase_     :      Dict						=  data_args.n_train

		trainer.save_model()  # this also saves the tokenizer

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

			# 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 ***"				)

		UpperCAmelCase_     :      Union[str, Any]						=  trainer.evaluate(metric_key_prefix="val"				)
		UpperCAmelCase_     :      Optional[Any]						=  data_args.n_val
		UpperCAmelCase_     :      Union[str, Any]						=  round(metrics["val_loss"]	,				4				)

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

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

		UpperCAmelCase_     :      List[Any]						=  trainer.predict(test_dataset=_SCREAMING_SNAKE_CASE	,				metric_key_prefix="test"				)
		UpperCAmelCase_     :      List[str]						=  test_output.metrics
		UpperCAmelCase_     :      int						=  data_args.n_test

		if trainer.is_world_process_zero():
			UpperCAmelCase_     :      Optional[Any]						=  round(metrics["test_loss"]	,				4				)
			handle_metrics("test"	,				_SCREAMING_SNAKE_CASE	,				training_args.output_dir				)
			all_metrics.update(_SCREAMING_SNAKE_CASE				)

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

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

	return all_metrics


def  a__       (    _SCREAMING_SNAKE_CASE   :       str				)     ->					Optional[int]:


	"""simple docstring"""





	main()


if __name__ == "__main__":
					main()






"""simple docstring"""




# 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:
				SCREAMING_SNAKE_CASE_    			=			subparsers.add_parser('''test'''     )
		else:
				SCREAMING_SNAKE_CASE_    			=			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     ):
		SCREAMING_SNAKE_CASE_    			=			os.path.sep.join(__file__.split(os.path.sep     )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']     )

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

		SCREAMING_SNAKE_CASE_    			=			['''accelerate-launch'''] + test_args.split()
		SCREAMING_SNAKE_CASE_    			=			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			(			):
		SCREAMING_SNAKE_CASE_    			=			test_command_parser()
		SCREAMING_SNAKE_CASE_    			=			parser.parse_args()
		test_command(__a     )


if __name__ == "__main__":
						main()