
'''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      _lowerCAmelCase       :







							'''simple docstring'''

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


@dataclass
class      _lowerCAmelCase       :







							'''simple docstring'''

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






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE ):
							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    __SCREAMING_SNAKE_CASE (    ):
							_snake_case  =    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.
														_snake_case,				_snake_case,				_snake_case  =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
							else:
														_snake_case,				_snake_case,				_snake_case  =    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.

							_snake_case  =    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				,   )

							_snake_case  =    ("""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 ) )

							_snake_case  =    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				,   )
							_snake_case  =    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:
														_snake_case  =    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 ):
																					_snake_case  =    tokenizer.lang_code_to_id[data_args.tgt_lang]
														else:
																					_snake_case  =    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() )

							_snake_case  =    SeqaSeqDataset

							# Get datasets
							_snake_case  =    (
							    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
							)
							_snake_case  =    (
							    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
							)
							_snake_case  =    (
							    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
							_snake_case  =    (
							    build_compute_metrics_fn(data_args.task				,   _SCREAMING_SNAKE_CASE ) if training_args.predict_with_generate else None
							)
							_snake_case  =    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				,   )

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

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

														_snake_case  =    trainer.evaluate(metric_key_prefix="""val""" )
														_snake_case  =    data_args.n_val
														_snake_case  =    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 ***""" )

														_snake_case  =    trainer.predict(test_dataset=_SCREAMING_SNAKE_CASE				,   metric_key_prefix="""test""" )
														_snake_case  =    test_output.metrics
														_snake_case  =    data_args.n_test

														if trainer.is_world_process_zero():
																					_snake_case  =    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:
																												_snake_case  =    tokenizer.batch_decode(
																												    test_output.predictions				,   skip_special_tokens=_SCREAMING_SNAKE_CASE				,   clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE )
																												_snake_case  =    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    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):
							main()


if __name__ == "__main__":
				main()

import warnings

from .generation import TFGenerationMixin







class 							lowerCAmelCase__   ( __lowercase						):
						# warning at import time
						warnings.warn(
						    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
						    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,						__lowercase		,						)



