





"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__     			=			logging.getLogger(__name__)


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		return (preds == labels).mean()





@dataclass
class        _lowerCamelCase      :
						UpperCAmelCase_ =			field(
						    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
						UpperCAmelCase_ =			field(
						    default=_lowercase  ,			metadata={"help": "Pretrained config name or path if not the same as model_name"} )
						UpperCAmelCase_ =			field(
						    default=_lowercase  ,			metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
						UpperCAmelCase_ =			field(
						    default=_lowercase  ,			metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,			)





@dataclass
class        _lowerCamelCase      :
						UpperCAmelCase_ =			field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
						UpperCAmelCase_ =			field(metadata={"help": "Should contain the data files for the task."} )
						UpperCAmelCase_ =			field(
						    default=128  ,			metadata={
						        "help": (
						            "The maximum total input sequence length after tokenization. Sequences longer "
						            "than this will be truncated, sequences shorter will be padded."
						        )
						    }  ,			)
						UpperCAmelCase_ =			field(
						    default=_lowercase  ,			metadata={"help": "Overwrite the cached training and evaluation sets"} )



def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
		UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir    )
		    and os.listdir(training_args.output_dir    )
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
				    " --overwrite_output_dir to overcome."    )

		# 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.local_rank != -1    )  ,	training_args.fpaa  ,	)
		# 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()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("Training/evaluation parameters %s"  ,	_SCREAMING_SNAKE_CASE    )

		# Set seed
		set_seed(training_args.seed    )

		try:
				UpperCamelCase   =	processors[data_args.task_name]()
				UpperCamelCase   =	processor.get_labels()
				UpperCamelCase   =	len(_SCREAMING_SNAKE_CASE    )
		except KeyError:
				raise ValueError("Task not found: %s" % (data_args.task_name)    )

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		UpperCamelCase   =	AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,	num_labels=_SCREAMING_SNAKE_CASE  ,	finetuning_task=data_args.task_name  ,	cache_dir=model_args.cache_dir  ,	)
		UpperCamelCase   =	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   =	AutoModelForMultipleChoice.from_pretrained(
		    model_args.model_name_or_path  ,	from_tf=bool(".ckpt" in model_args.model_name_or_path    )  ,	config=_SCREAMING_SNAKE_CASE  ,	cache_dir=model_args.cache_dir  ,	)

		# Get datasets
		UpperCamelCase   =	(
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir  ,	tokenizer=_SCREAMING_SNAKE_CASE  ,	task=data_args.task_name  ,	max_seq_length=data_args.max_seq_length  ,	overwrite_cache=data_args.overwrite_cache  ,	mode=Split.train  ,	)
		    if training_args.do_train
		    else None
		)
		UpperCamelCase   =	(
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir  ,	tokenizer=_SCREAMING_SNAKE_CASE  ,	task=data_args.task_name  ,	max_seq_length=data_args.max_seq_length  ,	overwrite_cache=data_args.overwrite_cache  ,	mode=Split.dev  ,	)
		    if training_args.do_eval
		    else None
		)

		def compute_metrics(_SCREAMING_SNAKE_CASE    ) -> Dict:
				UpperCamelCase   =	np.argmax(p.predictions  ,	axis=1    )
				return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE  ,	p.label_ids    )}

		# Data collator
		UpperCamelCase   =	DataCollatorWithPadding(_SCREAMING_SNAKE_CASE  ,	pad_to_multiple_of=8    ) if training_args.fpaa else None

		# Initialize our Trainer
		UpperCamelCase   =	Trainer(
		    model=_SCREAMING_SNAKE_CASE  ,	args=_SCREAMING_SNAKE_CASE  ,	train_dataset=_SCREAMING_SNAKE_CASE  ,	eval_dataset=_SCREAMING_SNAKE_CASE  ,	compute_metrics=_SCREAMING_SNAKE_CASE  ,	data_collator=_SCREAMING_SNAKE_CASE  ,	)

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
		UpperCamelCase   =	{}
		if training_args.do_eval:
				logger.info("*** Evaluate ***"    )

				UpperCamelCase   =	trainer.evaluate()

				UpperCamelCase   =	os.path.join(training_args.output_dir  ,	"eval_results.txt"    )
				if trainer.is_world_master():
						with open(_SCREAMING_SNAKE_CASE  ,	"w"    ) as writer:
								logger.info("***** Eval results *****"    )
								for key, value in result.items():
										logger.info("  %s = %s"  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
										writer.write("%s = %s\n" % (key, value)    )

								results.update(_SCREAMING_SNAKE_CASE    )

		return results


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		main()


if __name__ == "__main__":
						main()











"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[
		    "encoder.version",
		    "decoder.version",
		    "model.encoder.version",
		    "model.decoder.version",
		    "decoder.output_projection.weight",
		    "_float_tensor",
		    "encoder.embed_positions._float_tensor",
		    "decoder.embed_positions._float_tensor",
		]
		for k in ignore_keys:
				state_dict.pop(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   ,				UpperCamelCase   =	emb.weight.shape
		UpperCamelCase   =	nn.Linear(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	bias=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	emb.weight.data
		return lin_layer


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )
		UpperCamelCase   =	mam_aaa["args"] or mam_aaa["cfg"]["model"]
		UpperCamelCase   =	mam_aaa["model"]
		remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	state_dict["encoder.embed_tokens.weight"].shape[0]

		UpperCamelCase   =	MaMaaaConfig(
		    vocab_size=_SCREAMING_SNAKE_CASE  ,	max_position_embeddings=1_024  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="relu"  ,	)

		UpperCamelCase   =	state_dict["decoder.embed_tokens.weight"]
		UpperCamelCase   =	MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )
		model.model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	make_linear_from_emb(model.model.shared    )

		return model


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						lowerCAmelCase__     			=			parser.parse_args()
						lowerCAmelCase__     			=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
						model.save_pretrained(args.pytorch_dump_folder_path)






