

"""simple docstring"""






import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


snake_case_   :   str        =       logging.getLogger(__name__)







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						'''summarization'''
			SCREAMING_SNAKE_CASE__     				=						['''loss''']
			SCREAMING_SNAKE_CASE__     				=						ROUGE_KEYS
			SCREAMING_SNAKE_CASE__     				=						'''rouge2'''







			def __init__(			self			: Any			,  lowercase			: List[Any]			,  **lowercase			: Tuple  ):





								'''simple docstring'''



								if hparams.sortish_sampler and hparams.gpus > 1:
													UpperCAmelCase			:   List[str]      =				False
								elif hparams.max_tokens_per_batch is not None:
													if hparams.gpus > 1:
																		raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training"  )
													if hparams.sortish_sampler:
																		raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"  )

								super().__init__(lowercase			,  num_labels=lowercase			,  mode=self.mode			,  **lowercase  )
								use_task_specific_params(self.model			,  "summarization"  )
								save_git_info(self.hparams.output_dir  )
								UpperCAmelCase			:   str      =				Path(self.output_dir  ) / "metrics.json"
								UpperCAmelCase			:   Any      =				Path(self.output_dir  ) / "hparams.pkl"
								pickle_save(self.hparams			,  self.hparams_save_path  )
								UpperCAmelCase			:   int      =				0
								UpperCAmelCase			:   List[str]      =				defaultdict(lowercase  )
								UpperCAmelCase			:   List[str]      =				self.config.model_type
								UpperCAmelCase			:   Tuple      =				self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

								UpperCAmelCase			:   dict      =				{
								    "data_dir": self.hparams.data_dir,
								    "max_source_length": self.hparams.max_source_length,
								    "prefix": self.model.config.prefix or "",
								}
								UpperCAmelCase			:   Union[str, Any]      =				{
								    "train": self.hparams.n_train,
								    "val": self.hparams.n_val,
								    "test": self.hparams.n_test,
								}
								UpperCAmelCase			:   Dict      =				{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

								UpperCAmelCase			:   Any      =				{
								    "train": self.hparams.max_target_length,
								    "val": self.hparams.val_max_target_length,
								    "test": self.hparams.test_max_target_length,
								}
								assert self.target_lens["train"] <= self.target_lens["val"], f"""target_lens: {self.target_lens}"""
								assert self.target_lens["train"] <= self.target_lens["test"], f"""target_lens: {self.target_lens}"""
								if self.hparams.freeze_embeds:
													freeze_embeds(self.model  )
								if self.hparams.freeze_encoder:
													freeze_params(self.model.get_encoder()  )
													assert_all_frozen(self.model.get_encoder()  )

								UpperCAmelCase			:   str      =				get_git_info()["repo_sha"]
								UpperCAmelCase			:   Optional[Any]      =				hparams.num_workers
								UpperCAmelCase			:   Optional[int]      =				None  # default to config
								if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer			,  lowercase  ):
													UpperCAmelCase			:   List[Any]      =				self.tokenizer.lang_code_to_id[hparams.tgt_lang]
													UpperCAmelCase			:   str      =				self.decoder_start_token_id
								UpperCAmelCase			:   Any      =				(
								    SeqaSeqDataset if hasattr(self.tokenizer			,  "prepare_seq2seq_batch"  ) else LegacySeqaSeqDataset
								)
								UpperCAmelCase			:   Tuple      =				False
								UpperCAmelCase			:   List[str]      =				self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
								if self.hparams.eval_max_gen_length is not None:
													UpperCAmelCase			:   Union[str, Any]      =				self.hparams.eval_max_gen_length
								else:
													UpperCAmelCase			:   str      =				self.model.config.max_length
								UpperCAmelCase			:   Union[str, Any]      =				self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric







			def 			__lowerCAmelCase				(			self			: str			,  lowercase			: Dict[str, torch.Tensor]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				{
								    k: self.tokenizer.batch_decode(v.tolist()  ) if "mask" not in k else v.shape for k, v in batch.items()
								}
								save_json(lowercase			,  Path(self.output_dir  ) / "text_batch.json"  )
								save_json({k: v.tolist() for k, v in batch.items()}			,  Path(self.output_dir  ) / "tok_batch.json"  )

								UpperCAmelCase			:   Optional[int]      =				True
								return readable_batch







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: str			,  **lowercase			: Tuple  ):





								'''simple docstring'''



								return self.model(lowercase			,  **lowercase  )







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: List[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.tokenizer.batch_decode(
								    lowercase			,  skip_special_tokens=lowercase			,  clean_up_tokenization_spaces=lowercase  )
								return lmap(str.strip			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.tokenizer.pad_token_id
								UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				batch["input_ids"], batch["attention_mask"]
								UpperCAmelCase			:   Any      =				batch["labels"]
								if isinstance(self.model			,  lowercase  ):
													UpperCAmelCase			:   Dict      =				self.model._shift_right(lowercase  )
								else:
													UpperCAmelCase			:   Optional[int]      =				shift_tokens_right(lowercase			,  lowercase  )
								if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
													UpperCAmelCase			:   List[str]      =				decoder_input_ids
													self.save_readable_batch(lowercase  )

								UpperCAmelCase			:   Optional[int]      =				self(lowercase			,  attention_mask=lowercase			,  decoder_input_ids=lowercase			,  use_cache=lowercase  )
								UpperCAmelCase			:   Union[str, Any]      =				outputs["logits"]
								if self.hparams.label_smoothing == 0:
													# Same behavior as modeling_bart.py, besides ignoring pad_token_id
													UpperCAmelCase			:   List[Any]      =				nn.CrossEntropyLoss(ignore_index=lowercase  )

													assert lm_logits.shape[-1] == self.vocab_size
													UpperCAmelCase			:   Tuple      =				ce_loss_fct(lm_logits.view(-1			,  lm_logits.shape[-1]  )			,  tgt_ids.view(-1  )  )
								else:
													UpperCAmelCase			:   Tuple      =				nn.functional.log_softmax(lowercase			,  dim=-1  )
													UpperCAmelCase      ,							UpperCAmelCase			:   List[Any]      =				label_smoothed_nll_loss(
													    lowercase			,  lowercase			,  self.hparams.label_smoothing			,  ignore_index=lowercase  )
								return (loss,)







			@property
			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								return self.tokenizer.pad_token_id







			def 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: List[str]			,  lowercase			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self._step(lowercase  )

								UpperCAmelCase			:   List[Any]      =				dict(zip(self.loss_names			,  lowercase  )  )
								# tokens per batch
								UpperCAmelCase			:   Optional[int]      =				batch["input_ids"].ne(self.pad  ).sum() + batch["labels"].ne(self.pad  ).sum()
								UpperCAmelCase			:   Optional[int]      =				batch["input_ids"].shape[0]
								UpperCAmelCase			:   str      =				batch["input_ids"].eq(self.pad  ).sum()
								UpperCAmelCase			:   Any      =				batch["input_ids"].eq(self.pad  ).float().mean()
								# TODO(SS): make a wandb summary metric for this
								return {"loss": loss_tensors[0], "log": logs}







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: Optional[int]			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								return self._generative_step(lowercase  )







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: Optional[int]			,  lowercase			: List[str]="val"  ):





								'''simple docstring'''



								self.step_count += 1
								UpperCAmelCase			:   Optional[int]      =				{k: torch.stack([x[k] for x in outputs]  ).mean() for k in self.loss_names}
								UpperCAmelCase			:   List[str]      =				losses["loss"]
								UpperCAmelCase			:   List[str]      =				{
								    k: np.array([x[k] for x in outputs]  ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
								}
								UpperCAmelCase			:   Any      =				(
								    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
								)
								UpperCAmelCase			:   torch.FloatTensor      =				torch.tensor(lowercase  ).type_as(lowercase  )
								generative_metrics.update({k: v.item() for k, v in losses.items()}  )
								losses.update(lowercase  )
								UpperCAmelCase			:   str      =				{f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
								UpperCAmelCase			:   Any      =				self.step_count
								self.metrics[prefix].append(lowercase  )  # callback writes this to self.metrics_save_path
								UpperCAmelCase			:   Any      =				flatten_list([x["preds"] for x in outputs]  )
								return {
								    "log": all_metrics,
								    "preds": preds,
								    f"""{prefix}_loss""": loss,
								    f"""{prefix}_{self.val_metric}""": metric_tensor,
								}







			def 			__lowerCAmelCase				(			self			: List[str]			,  lowercase			: Tuple			,  lowercase			: Any  ):





								'''simple docstring'''



								return calculate_rouge(lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: List[str]			,  lowercase			: dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				time.time()

								# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
								UpperCAmelCase			:   str      =				self.model.generate(
								    batch["input_ids"]			,  attention_mask=batch["attention_mask"]			,  use_cache=lowercase			,  decoder_start_token_id=self.decoder_start_token_id			,  num_beams=self.eval_beams			,  max_length=self.eval_max_length			,  )
								UpperCAmelCase			:   Dict      =				(time.time() - ta) / batch["input_ids"].shape[0]
								UpperCAmelCase			:   List[str]      =				self.ids_to_clean_text(lowercase  )
								UpperCAmelCase			:   List[str]      =				self.ids_to_clean_text(batch["labels"]  )
								UpperCAmelCase			:   Optional[Any]      =				self._step(lowercase  )
								UpperCAmelCase			:   Tuple      =				dict(zip(self.loss_names			,  lowercase  )  )
								UpperCAmelCase			:   Dict      =				self.calc_generative_metrics(lowercase			,  lowercase  )
								UpperCAmelCase			:   List[Any]      =				np.mean(lmap(lowercase			,  lowercase  )  )
								base_metrics.update(gen_time=lowercase			,  gen_len=lowercase			,  preds=lowercase			,  target=lowercase			,  **lowercase  )
								return base_metrics







			def 			__lowerCAmelCase				(			self			: str			,  lowercase			: Optional[Any]			,  lowercase			: Dict  ):





								'''simple docstring'''



								return self._generative_step(lowercase  )







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: List[str]  ):





								'''simple docstring'''



								return self.validation_epoch_end(lowercase			,  prefix="test"  )







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.n_obs[type_path]
								UpperCAmelCase			:   Dict      =				self.target_lens[type_path]
								UpperCAmelCase			:   List[Any]      =				self.dataset_class(
								    self.tokenizer			,  type_path=lowercase			,  n_obs=lowercase			,  max_target_length=lowercase			,  **self.dataset_kwargs			,  )
								return dataset







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: str			,  lowercase			: int			,  lowercase			: bool = False  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.get_dataset(lowercase  )

								if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
													UpperCAmelCase			:   List[Any]      =				dataset.make_sortish_sampler(lowercase			,  distributed=self.hparams.gpus > 1  )
													return DataLoader(
													    lowercase			,  batch_size=lowercase			,  collate_fn=dataset.collate_fn			,  shuffle=lowercase			,  num_workers=self.num_workers			,  sampler=lowercase			,  )

								elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
													UpperCAmelCase			:   Tuple      =				dataset.make_dynamic_sampler(
													    self.hparams.max_tokens_per_batch			,  distributed=self.hparams.gpus > 1  )
													return DataLoader(
													    lowercase			,  batch_sampler=lowercase			,  collate_fn=dataset.collate_fn			,  num_workers=self.num_workers			,  )
								else:
													return DataLoader(
													    lowercase			,  batch_size=lowercase			,  collate_fn=dataset.collate_fn			,  shuffle=lowercase			,  num_workers=self.num_workers			,  sampler=lowercase			,  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.get_dataloader("train"			,  batch_size=self.hparams.train_batch_size			,  shuffle=lowercase  )
								return dataloader







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								return self.get_dataloader("val"			,  batch_size=self.hparams.eval_batch_size  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								return self.get_dataloader("test"			,  batch_size=self.hparams.eval_batch_size  )







			@staticmethod
			def 			__lowerCAmelCase				(			lowercase			: Any			,  lowercase			: List[Any]  ):





								'''simple docstring'''



								BaseTransformer.add_model_specific_args(lowercase			,  lowercase  )
								add_generic_args(lowercase			,  lowercase  )
								parser.add_argument(
								    "--max_source_length"			,  default=10_24			,  type=lowercase			,  help=(
								        "The maximum total input sequence length after tokenization. Sequences longer "
								        "than this will be truncated, sequences shorter will be padded."
								    )			,  )
								parser.add_argument(
								    "--max_target_length"			,  default=56			,  type=lowercase			,  help=(
								        "The maximum total input sequence length after tokenization. Sequences longer "
								        "than this will be truncated, sequences shorter will be padded."
								    )			,  )
								parser.add_argument(
								    "--val_max_target_length"			,  default=1_42			,  type=lowercase			,  help=(
								        "The maximum total input sequence length after tokenization. Sequences longer "
								        "than this will be truncated, sequences shorter will be padded."
								    )			,  )
								parser.add_argument(
								    "--test_max_target_length"			,  default=1_42			,  type=lowercase			,  help=(
								        "The maximum total input sequence length after tokenization. Sequences longer "
								        "than this will be truncated, sequences shorter will be padded."
								    )			,  )
								parser.add_argument("--freeze_encoder"			,  action="store_true"  )
								parser.add_argument("--freeze_embeds"			,  action="store_true"  )
								parser.add_argument("--sortish_sampler"			,  action="store_true"			,  default=lowercase  )
								parser.add_argument("--overwrite_output_dir"			,  action="store_true"			,  default=lowercase  )
								parser.add_argument("--max_tokens_per_batch"			,  type=lowercase			,  default=lowercase  )
								parser.add_argument("--logger_name"			,  type=lowercase			,  choices=["default", "wandb", "wandb_shared"]			,  default="default"  )
								parser.add_argument("--n_train"			,  type=lowercase			,  default=-1			,  required=lowercase			,  help="# examples. -1 means use all."  )
								parser.add_argument("--n_val"			,  type=lowercase			,  default=5_00			,  required=lowercase			,  help="# examples. -1 means use all."  )
								parser.add_argument("--n_test"			,  type=lowercase			,  default=-1			,  required=lowercase			,  help="# examples. -1 means use all."  )
								parser.add_argument(
								    "--task"			,  type=lowercase			,  default="summarization"			,  required=lowercase			,  help="# examples. -1 means use all."  )
								parser.add_argument("--label_smoothing"			,  type=lowercase			,  default=0.0			,  required=lowercase  )
								parser.add_argument("--src_lang"			,  type=lowercase			,  default=""			,  required=lowercase  )
								parser.add_argument("--tgt_lang"			,  type=lowercase			,  default=""			,  required=lowercase  )
								parser.add_argument("--eval_beams"			,  type=lowercase			,  default=lowercase			,  required=lowercase  )
								parser.add_argument(
								    "--val_metric"			,  type=lowercase			,  default=lowercase			,  required=lowercase			,  choices=["bleu", "rouge2", "loss", None]  )
								parser.add_argument("--eval_max_gen_length"			,  type=lowercase			,  default=lowercase			,  help="never generate more than n tokens"  )
								parser.add_argument("--save_top_k"			,  type=lowercase			,  default=1			,  required=lowercase			,  help="How many checkpoints to save"  )
								parser.add_argument(
								    "--early_stopping_patience"			,  type=lowercase			,  default=-1			,  required=lowercase			,  help=(
								        "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
								        " val_check_interval will effect it."
								    )			,  )
								return parser







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						'''translation'''
			SCREAMING_SNAKE_CASE__     				=						['''loss''']
			SCREAMING_SNAKE_CASE__     				=						['''bleu''']
			SCREAMING_SNAKE_CASE__     				=						'''bleu'''







			def __init__(			self			: Tuple			,  lowercase			: int			,  **lowercase			: Tuple  ):





								'''simple docstring'''



								super().__init__(lowercase			,  **lowercase  )
								UpperCAmelCase			:   Tuple      =				hparams.src_lang
								UpperCAmelCase			:   Any      =				hparams.tgt_lang







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: str			,  lowercase			: List[Any]  ):





								'''simple docstring'''



								return calculate_bleu(lowercase			,  lowercase  )



def   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		List[Any]=None	):




					'''simple docstring'''






					Path(args.output_dir	).mkdir(exist_ok=_lowercase	)
					check_output_dir(_lowercase							,      expected_items=3	)

					if model is None:
										if "summarization" in args.task:
															UpperCAmelCase			:   SummarizationModule      =				SummarizationModule(_lowercase	)
										else:
															UpperCAmelCase			:   SummarizationModule      =				TranslationModule(_lowercase	)
					UpperCAmelCase			:   List[str]      =				Path(args.data_dir	).name
					if (
					    args.logger_name == "default"
					    or args.fast_dev_run
					    or str(args.output_dir	).startswith("/tmp"	)
					    or str(args.output_dir	).startswith("/var"	)
					):
										UpperCAmelCase			:   Any      =				True  # don't pollute wandb logs unnecessarily
					elif args.logger_name == "wandb":
										from pytorch_lightning.loggers import WandbLogger

										UpperCAmelCase			:   Union[str, Any]      =				os.environ.get("WANDB_PROJECT"							,      _lowercase	)
										UpperCAmelCase			:   Dict      =				WandbLogger(name=model.output_dir.name							,      project=_lowercase	)

					elif args.logger_name == "wandb_shared":
										from pytorch_lightning.loggers import WandbLogger

										UpperCAmelCase			:   str      =				WandbLogger(name=model.output_dir.name							,      project=F"""hf_{dataset}"""	)

					if args.early_stopping_patience >= 0:
										UpperCAmelCase			:   List[Any]      =				get_early_stopping_callback(model.val_metric							,      args.early_stopping_patience	)
					else:
										UpperCAmelCase			:   int      =				False

					UpperCAmelCase			:   str      =				args.val_metric == "loss"
					UpperCAmelCase			:   pl.Trainer      =				generic_train(
					    _lowercase							,      _lowercase							,      logging_callback=SeqaSeqLoggingCallback()							,      checkpoint_callback=get_checkpoint_callback(
					        args.output_dir							,      model.val_metric							,      args.save_top_k							,      _lowercase	)							,      early_stopping_callback=_lowercase							,      logger=_lowercase							,      )
					pickle_save(model.hparams							,      model.output_dir / "hparams.pkl"	)
					if not args.do_predict:
										return model

					UpperCAmelCase			:   Optional[Any]      =				""
					UpperCAmelCase			:   List[Any]      =				sorted(glob.glob(os.path.join(args.output_dir							,      "*.ckpt"	)							,      recursive=_lowercase	)	)
					if checkpoints:
										UpperCAmelCase			:   int      =				checkpoints[-1]
										UpperCAmelCase			:   Tuple      =				checkpoints[-1]
					trainer.logger.log_hyperparams(model.hparams	)

					# test() without a model tests using the best checkpoint automatically
					trainer.test()
					return model


if __name__ == "__main__":
	snake_case_   :   List[str]        =       argparse.ArgumentParser()
	snake_case_   :   Tuple        =       pl.Trainer.add_argparse_args(parser)
	snake_case_   :   List[Any]        =       SummarizationModule.add_model_specific_args(parser, os.getcwd())

	snake_case_   :   int        =       parser.parse_args()

	main(args)





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_   :   Dict        =       {
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   List[Any]        =       ["""MvpTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   Any        =       [
	    """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MvpForCausalLM""",
	    """MvpForConditionalGeneration""",
	    """MvpForQuestionAnswering""",
	    """MvpForSequenceClassification""",
	    """MvpModel""",
	    """MvpPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	snake_case_   :   List[str]        =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


