


'''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,
)


_lowercase       :      str	=	logging.getLogger(__name__)






@dataclass
class       _UpperCamelCase    :

				"""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       _UpperCamelCase    :

				"""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=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 		= 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 		= 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 		= 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 		= 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   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     Tuple						) ->   Optional[int]:





	"""simple docstring"""







	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   _lowerCAmelCase				(   ) ->   str:





	"""simple docstring"""







	A								=				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								=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
	else:
		A								=				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								=				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								=				("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								=				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								=				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								=				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								=				tokenizer.lang_code_to_id[data_args.tgt_lang]
		else:
			A								=				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								=				SeqaSeqDataset

	# Get datasets
	A								=				(
	    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								=				(
	    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								=				(
	    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								=				(
	    build_compute_metrics_fn(data_args.task							,					_lowerCamelCase						) if training_args.predict_with_generate else None
	)
	A								=				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								=				{}
	# Training
	if training_args.do_train:
		logger.info("""*** Train ***"""						)

		A								=				trainer.train(
		    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
		A								=				train_result.metrics
		A								=				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								=				trainer.evaluate(metric_key_prefix="""val"""						)
		A								=				data_args.n_val
		A								=				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								=				trainer.predict(test_dataset=_lowerCamelCase							,					metric_key_prefix="""test"""						)
		A								=				test_output.metrics
		A								=				data_args.n_test

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

			if training_args.predict_with_generate:
				A								=				tokenizer.batch_decode(
				    test_output.predictions							,					skip_special_tokens=_lowerCamelCase							,					clean_up_tokenization_spaces=_lowerCamelCase						)
				A								=				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   _lowerCAmelCase				(   UpperCamelCase__:     List[Any]						) ->   Optional[int]:





	"""simple docstring"""







	main()


if __name__ == "__main__":
				main()







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any=5						) ->   Optional[Any]:





	"""simple docstring"""







	assert masked_input.count("""<mask>"""						) == 1
	A								=				torch.tensor(tokenizer.encode(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__						)						).unsqueeze(0						)  # Batch size 1
	A								=				model(UpperCamelCase__						)[0]  # The last hidden-state is the first element of the output tuple
	A								=				(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	A								=				logits[0, masked_index, :]
	A								=				logits.softmax(dim=0						)
	A					,			A								=				prob.topk(k=UpperCamelCase__							,					dim=0						)
	A								=				""" """.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(UpperCamelCase__						)						)]						)
	A								=				tokenizer.mask_token
	A								=				[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """						)						):
		A								=				predicted_token_bpe.replace("""\u2581"""							,					""" """						)
		if " {0}".format(UpperCamelCase__						) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(""" {0}""".format(UpperCamelCase__						)							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(UpperCamelCase__							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
	return topk_filled_outputs


_lowercase       :      Optional[int]	=	CamembertTokenizer.from_pretrained("camembert-base")
_lowercase       :      int	=	CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_lowercase       :      Optional[int]	=	"Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))



