



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,
)


__SCREAMING_SNAKE_CASE           =  logging.getLogger(__name__)

@dataclass
class 	lowerCamelCase_    :






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







@dataclass
class 	lowerCamelCase_    :






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







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

			A							:					Optional[int]              =						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.
						A       , A       , A							:					Union[str, Any]              =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
			else:
						A       , A       , A							:					int              =						parser.parse_args_into_dataclasses()

			check_output_dir(_lowerCamelCase    )

			# 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" ,    _lowerCamelCase    )

			# 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.

			A							:					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 ,    )

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

			A							:					Optional[Any]              =						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 ,    )
			A							:					List[Any]              =						AutoModelForSeqaSeqLM.from_pretrained(
			    model_args.model_name_or_path ,    from_tf=".ckpt" in model_args.model_name_or_path ,    config=_lowerCamelCase ,    cache_dir=model_args.cache_dir ,    )

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

			# set num_beams for evaluation
			if data_args.eval_beams is None:
						A							:					Union[str, Any]              =						model.config.num_beams

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

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

			A							:					List[Any]              =						SeqaSeqDataset

			# Get datasets
			A							:					Any              =						(
			    dataset_class(
			        _lowerCamelCase ,    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
			)
			A							:					str              =						(
			    dataset_class(
			        _lowerCamelCase ,    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
			)
			A							:					Union[str, Any]              =						(
			    dataset_class(
			        _lowerCamelCase ,    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
			A							:					Any              =						(
			    build_compute_metrics_fn(data_args.task ,    _lowerCamelCase    ) if training_args.predict_with_generate else None
			)
			A							:					List[Any]              =						SeqaSeqTrainer(
			    model=_lowerCamelCase ,    args=_lowerCamelCase ,    data_args=_lowerCamelCase ,    train_dataset=_lowerCamelCase ,    eval_dataset=_lowerCamelCase ,    data_collator=SeqaSeqDataCollator(
			        _lowerCamelCase ,    _lowerCamelCase ,    model.config.decoder_start_token_id ,    training_args.tpu_num_cores    ) ,    compute_metrics=_lowerCamelCase ,    tokenizer=_lowerCamelCase ,    )

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

						A							:					List[Any]              =						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
						A							:					List[str]              =						train_result.metrics
						A							:					Union[str, Any]              =						data_args.n_train

						trainer.save_model()  # this also saves the tokenizer

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

									# 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 ***"    )

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

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

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

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

						if trainer.is_world_process_zero():
									A							:					List[str]              =						round(metrics["test_loss"] ,    4    )
									handle_metrics("test" ,    _lowerCamelCase ,    training_args.output_dir    )
									all_metrics.update(_lowerCamelCase    )

									if training_args.predict_with_generate:
												A							:					List[Any]              =						tokenizer.batch_decode(
												    test_output.predictions ,    skip_special_tokens=_lowerCamelCase ,    clean_up_tokenization_spaces=_lowerCamelCase    )
												A							:					List[str]              =						lmap(str.strip ,    _lowerCamelCase    )
												write_txt_file(_lowerCamelCase ,    os.path.join(training_args.output_dir ,    "test_generations.txt"    )    )

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

			return all_metrics




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
		main()




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						RobertaPreLayerNormConfig.from_pretrained(
			    _lowerCamelCase ,    architectures=["RobertaPreLayerNormForMaskedLM"]    )

			# convert state_dict
			A							:					List[Any]              =						torch.load(hf_hub_download(repo_id=_lowerCamelCase ,    filename="pytorch_model.bin"    )    )
			A							:					Union[str, Any]              =						{}
			for tensor_key, tensor_value in original_state_dict.items():
						# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
						if tensor_key.startswith("roberta."    ):
									A							:					int              =						"roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

						# The original implementation contains weights which are not used, remove them from the state_dict
						if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
									continue

						A							:					Any              =						tensor_value

			A							:					Optional[int]              =						RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=_lowerCamelCase ,    config=_lowerCamelCase ,    state_dict=_lowerCamelCase    )
			model.save_pretrained(_lowerCamelCase    )

			# convert tokenizer
			A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(_lowerCamelCase    )
			tokenizer.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--checkpoint-repo""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)