



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


@dataclass
class 	lowerCamelCase_							:
						_lowerCAmelCase     :   str	     =				field(
						    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}			)
						_lowerCAmelCase     :   Optional[str]	     =				field(
						    default='summarization'    ,				metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}    ,				)
						_lowerCAmelCase     :   Optional[int]	     =				field(
						    default=1_0_2_4    ,				metadata={
						        'help': (
						            'The maximum total input sequence length after tokenization. Sequences longer '
						            'than this will be truncated, sequences shorter will be padded.'
						        )
						    }    ,				)
						_lowerCAmelCase     :   Optional[int]	     =				field(
						    default=1_2_8    ,				metadata={
						        'help': (
						            'The maximum total sequence length for target text after tokenization. Sequences longer '
						            'than this will be truncated, sequences shorter will be padded.'
						        )
						    }    ,				)
						_lowerCAmelCase     :   Optional[int]	     =				field(
						    default=1_4_2    ,				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``.'
						        )
						    }    ,				)
						_lowerCAmelCase     :   Optional[int]	     =				field(
						    default=1_4_2    ,				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.'
						        )
						    }    ,				)
						_lowerCAmelCase     :   Optional[int]	     =				field(default=-1    ,				metadata={'help': '# training examples. -1 means use all.'}			)
						_lowerCAmelCase     :   Optional[int]	     =				field(default=-1    ,				metadata={'help': '# validation examples. -1 means use all.'}			)
						_lowerCAmelCase     :   Optional[int]	     =				field(default=-1    ,				metadata={'help': '# test examples. -1 means use all.'}			)
						_lowerCAmelCase     :   Optional[str]	     =				field(default=snake_case_    ,				metadata={'help': 'Source language id for translation.'}			)
						_lowerCAmelCase     :   Optional[str]	     =				field(default=snake_case_    ,				metadata={'help': 'Target language id for translation.'}			)
						_lowerCAmelCase     :   Optional[int]	     =				field(default=snake_case_    ,				metadata={'help': '# num_beams to use for evaluation.'}			)
						_lowerCAmelCase     :   bool	     =				field(
						    default=snake_case_    ,				metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}    ,				)


def 					UpperCAmelCase   (						A  :				List[Any]					,		A  :				int					,		A  :				Union[str, Any]				):
							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.

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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.
														SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Tuple     					=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
							else:
														SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[Any]     					=			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.

							SCREAMING_SNAKE_CASE					:      int     					=			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					,		)

							SCREAMING_SNAKE_CASE					:      str     					=			('''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				)				)

							SCREAMING_SNAKE_CASE					:      int     					=			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					,		)
							SCREAMING_SNAKE_CASE					:      List[str]     					=			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:
														SCREAMING_SNAKE_CASE					:      Any     					=			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				):
																					SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer.lang_code_to_id[data_args.tgt_lang]
														else:
																					SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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()				)

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			SeqaSeqDataset

							# Get datasets
							SCREAMING_SNAKE_CASE					:      int     					=			(
							    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
							)
							SCREAMING_SNAKE_CASE					:      str     					=			(
							    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
							)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			(
							    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
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			(
							    build_compute_metrics_fn(data_args.task					,		A				) if training_args.predict_with_generate else None
							)
							SCREAMING_SNAKE_CASE					:      List[str]     					=			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					,		)

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

														SCREAMING_SNAKE_CASE					:      List[str]     					=			trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			train_result.metrics
														SCREAMING_SNAKE_CASE					:      Tuple     					=			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 ***'''				)

														SCREAMING_SNAKE_CASE					:      str     					=			trainer.evaluate(metric_key_prefix='''val'''				)
														SCREAMING_SNAKE_CASE					:      Any     					=			data_args.n_val
														SCREAMING_SNAKE_CASE					:      List[str]     					=			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 ***'''				)

														SCREAMING_SNAKE_CASE					:      List[str]     					=			trainer.predict(test_dataset=A					,		metric_key_prefix='''test'''				)
														SCREAMING_SNAKE_CASE					:      Tuple     					=			test_output.metrics
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			data_args.n_test

														if trainer.is_world_process_zero():
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			round(metrics['''test_loss''']					,		4				)
																					handle_metrics('''test'''					,		A					,		training_args.output_dir				)
																					all_metrics.update(A				)

																					if training_args.predict_with_generate:
																												SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.batch_decode(
																												    test_output.predictions					,		skip_special_tokens=A					,		clean_up_tokenization_spaces=A				)
																												SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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  :				List[Any]				):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()









'''simple docstring'''



import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowerCAmelCase_     :       Optional[Any]								=						'.'


if __name__ == "__main__":
							lowerCAmelCase_     :       Optional[Any]								=						os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
							lowerCAmelCase_     :       Dict								=						[]
							lowerCAmelCase_     :       Any								=						[]
							with open(doctest_file_path) as fp:
														for line in fp:
																					lowerCAmelCase_     :       List[str]								=						line.strip()
																					lowerCAmelCase_     :       Optional[Any]								=						os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														lowerCAmelCase_     :       Dict								=						'\n'.join(non_existent_paths)
														raise ValueError(f'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
							if all_paths != sorted(all_paths):
														raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')




