





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_				=			logging.getLogger(__name__)

class 		SCREAMING_SNAKE_CASE__				(       __A  ):
      A_						:				Optional[int]							=    "summarization"
      A_						:				Union[str, Any]							=    ["loss"]
      A_						:				List[Any]							=    ROUGE_KEYS
      A_						:				List[Any]							=    "rouge2"


      def __init__(self       :      int				,			a__       :      Any				,			**a__       :      List[Any]      ):





         """simple docstring"""



         if hparams.sortish_sampler and hparams.gpus > 1:
            __snake_case							       =   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__(_UpperCamelCase				,			num_labels=_UpperCamelCase				,			mode=self.mode				,			**_UpperCamelCase      )
         use_task_specific_params(self.model				,			'''summarization'''      )
         save_git_info(self.hparams.output_dir      )
         __snake_case							       =   Path(self.output_dir      ) / '''metrics.json'''
         __snake_case							       =   Path(self.output_dir      ) / '''hparams.pkl'''
         pickle_save(self.hparams				,			self.hparams_save_path      )
         __snake_case							       =   0
         __snake_case							       =   defaultdict(_UpperCamelCase      )
         __snake_case							       =   self.config.model_type
         __snake_case							       =   self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

         __snake_case							       =   {
             '''data_dir''': self.hparams.data_dir,
             '''max_source_length''': self.hparams.max_source_length,
             '''prefix''': self.model.config.prefix or '''''',
         }
         __snake_case							       =   {
             '''train''': self.hparams.n_train,
             '''val''': self.hparams.n_val,
             '''test''': self.hparams.n_test,
         }
         __snake_case							       =   {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

         __snake_case							       =   {
             '''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()      )

         __snake_case							       =   get_git_info()['''repo_sha''']
         __snake_case							       =   hparams.num_workers
         __snake_case							       =   None  # default to config
         if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer				,			_UpperCamelCase      ):
            __snake_case							       =   self.tokenizer.lang_code_to_id[hparams.tgt_lang]
            __snake_case							       =   self.decoder_start_token_id
         __snake_case							       =   (
             SeqaSeqDataset if hasattr(self.tokenizer				,			'''prepare_seq2seq_batch'''      ) else LegacySeqaSeqDataset
         )
         __snake_case							       =   False
         __snake_case							       =   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:
            __snake_case							       =   self.hparams.eval_max_gen_length
         else:
            __snake_case							       =   self.model.config.max_length
         __snake_case							       =   self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric


      def    a (self       :      List[Any]				,			a__       :      Dict[str, torch.Tensor]      ):





         """simple docstring"""



         __snake_case							       =   {
             k: self.tokenizer.batch_decode(v.tolist()      ) if '''mask''' not in k else v.shape for k, v in batch.items()
         }
         save_json(_UpperCamelCase				,			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'''      )

         __snake_case							       =   True
         return readable_batch


      def    a (self       :      Tuple				,			a__       :      Union[str, Any]				,			**a__       :      List[str]      ):





         """simple docstring"""



         return self.model(_UpperCamelCase				,			**_UpperCamelCase      )


      def    a (self       :      int				,			a__       :      List[int]      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer.batch_decode(
             _UpperCamelCase				,			skip_special_tokens=_UpperCamelCase				,			clean_up_tokenization_spaces=_UpperCamelCase      )
         return lmap(str.strip				,			_UpperCamelCase      )


      def    a (self       :      List[Any]				,			a__       :      dict      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer.pad_token_id
         __snake_case       ,	__snake_case							       =   batch['''input_ids'''], batch['''attention_mask''']
         __snake_case							       =   batch['''labels''']
         if isinstance(self.model				,			_UpperCamelCase      ):
            __snake_case							       =   self.model._shift_right(_UpperCamelCase      )
         else:
            __snake_case							       =   shift_tokens_right(_UpperCamelCase				,			_UpperCamelCase      )
         if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
            __snake_case							       =   decoder_input_ids
            self.save_readable_batch(_UpperCamelCase      )

         __snake_case							       =   self(_UpperCamelCase				,			attention_mask=_UpperCamelCase				,			decoder_input_ids=_UpperCamelCase				,			use_cache=_UpperCamelCase      )
         __snake_case							       =   outputs['''logits''']
         if self.hparams.label_smoothing == 0:
            # Same behavior as modeling_bart.py, besides ignoring pad_token_id
            __snake_case							       =   nn.CrossEntropyLoss(ignore_index=_UpperCamelCase      )

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


      @property
      def    a (self       :      Tuple      ):





         """simple docstring"""



         return self.tokenizer.pad_token_id


      def    a (self       :      Dict				,			a__       :      List[str]				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   self._step(_UpperCamelCase      )

         __snake_case							       =   dict(zip(self.loss_names				,			_UpperCamelCase      )      )
         # tokens per batch
         __snake_case							       =   batch['''input_ids'''].ne(self.pad      ).sum() + batch['''labels'''].ne(self.pad      ).sum()
         __snake_case							       =   batch['''input_ids'''].shape[0]
         __snake_case							       =   batch['''input_ids'''].eq(self.pad      ).sum()
         __snake_case							       =   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    a (self       :      List[Any]				,			a__       :      Optional[int]				,			a__       :      Dict      ):





         """simple docstring"""



         return self._generative_step(_UpperCamelCase      )


      def    a (self       :      Optional[Any]				,			a__       :      str				,			a__       :      Optional[Any]="val"      ):





         """simple docstring"""



         self.step_count += 1
         __snake_case							       =   {k: torch.stack([x[k] for x in outputs]      ).mean() for k in self.loss_names}
         __snake_case							       =   losses['''loss''']
         __snake_case							       =   {
             k: np.array([x[k] for x in outputs]      ).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
         }
         __snake_case							       =   (
             generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
         )
         __snake_case							       =   torch.tensor(_UpperCamelCase      ).type_as(_UpperCamelCase      )
         generative_metrics.update({k: v.item() for k, v in losses.items()}      )
         losses.update(_UpperCamelCase      )
         __snake_case							       =   {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
         __snake_case							       =   self.step_count
         self.metrics[prefix].append(_UpperCamelCase      )  # callback writes this to self.metrics_save_path
         __snake_case							       =   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    a (self       :      int				,			a__       :      Optional[int]				,			a__       :      List[str]      ):





         """simple docstring"""



         return calculate_rouge(_UpperCamelCase				,			_UpperCamelCase      )


      def    a (self       :      str				,			a__       :      dict      ):





         """simple docstring"""



         __snake_case							       =   time.time()

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


      def    a (self       :      Tuple				,			a__       :      str				,			a__       :      Dict      ):





         """simple docstring"""



         return self._generative_step(_UpperCamelCase      )


      def    a (self       :      Optional[int]				,			a__       :      Optional[int]      ):





         """simple docstring"""



         return self.validation_epoch_end(_UpperCamelCase				,			prefix='''test'''      )


      def    a (self       :      Union[str, Any]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   self.n_obs[type_path]
         __snake_case							       =   self.target_lens[type_path]
         __snake_case							       =   self.dataset_class(
             self.tokenizer				,			type_path=_UpperCamelCase				,			n_obs=_UpperCamelCase				,			max_target_length=_UpperCamelCase				,			**self.dataset_kwargs				,			)
         return dataset


      def    a (self       :      Optional[int]				,			a__       :      str				,			a__       :      int				,			a__       :      bool = False      ):





         """simple docstring"""



         __snake_case							       =   self.get_dataset(_UpperCamelCase      )

         if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
            __snake_case							       =   dataset.make_sortish_sampler(_UpperCamelCase				,			distributed=self.hparams.gpus > 1      )
            return DataLoader(
                _UpperCamelCase				,			batch_size=_UpperCamelCase				,			collate_fn=dataset.collate_fn				,			shuffle=_UpperCamelCase				,			num_workers=self.num_workers				,			sampler=_UpperCamelCase				,			)

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


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   self.get_dataloader('''train'''				,			batch_size=self.hparams.train_batch_size				,			shuffle=_UpperCamelCase      )
         return dataloader


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





         """simple docstring"""



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


      def    a (self       :      Tuple      ):





         """simple docstring"""



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


      @staticmethod
      def    a (a__       :      List[Any]				,			a__       :      Optional[int]      ):





         """simple docstring"""



         BaseTransformer.add_model_specific_args(_UpperCamelCase				,			_UpperCamelCase      )
         add_generic_args(_UpperCamelCase				,			_UpperCamelCase      )
         parser.add_argument(
             '''--max_source_length'''				,			default=1024				,			type=_UpperCamelCase				,			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=_UpperCamelCase				,			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=142				,			type=_UpperCamelCase				,			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=142				,			type=_UpperCamelCase				,			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=_UpperCamelCase      )
         parser.add_argument('''--overwrite_output_dir'''				,			action='''store_true'''				,			default=_UpperCamelCase      )
         parser.add_argument('''--max_tokens_per_batch'''				,			type=_UpperCamelCase				,			default=_UpperCamelCase      )
         parser.add_argument('''--logger_name'''				,			type=_UpperCamelCase				,			choices=['''default''', '''wandb''', '''wandb_shared''']				,			default='''default'''      )
         parser.add_argument('''--n_train'''				,			type=_UpperCamelCase				,			default=-1				,			required=_UpperCamelCase				,			help='''# examples. -1 means use all.'''      )
         parser.add_argument('''--n_val'''				,			type=_UpperCamelCase				,			default=500				,			required=_UpperCamelCase				,			help='''# examples. -1 means use all.'''      )
         parser.add_argument('''--n_test'''				,			type=_UpperCamelCase				,			default=-1				,			required=_UpperCamelCase				,			help='''# examples. -1 means use all.'''      )
         parser.add_argument(
             '''--task'''				,			type=_UpperCamelCase				,			default='''summarization'''				,			required=_UpperCamelCase				,			help='''# examples. -1 means use all.'''      )
         parser.add_argument('''--label_smoothing'''				,			type=_UpperCamelCase				,			default=0.0				,			required=_UpperCamelCase      )
         parser.add_argument('''--src_lang'''				,			type=_UpperCamelCase				,			default=''''''				,			required=_UpperCamelCase      )
         parser.add_argument('''--tgt_lang'''				,			type=_UpperCamelCase				,			default=''''''				,			required=_UpperCamelCase      )
         parser.add_argument('''--eval_beams'''				,			type=_UpperCamelCase				,			default=_UpperCamelCase				,			required=_UpperCamelCase      )
         parser.add_argument(
             '''--val_metric'''				,			type=_UpperCamelCase				,			default=_UpperCamelCase				,			required=_UpperCamelCase				,			choices=['''bleu''', '''rouge2''', '''loss''', None]      )
         parser.add_argument('''--eval_max_gen_length'''				,			type=_UpperCamelCase				,			default=_UpperCamelCase				,			help='''never generate more than n tokens'''      )
         parser.add_argument('''--save_top_k'''				,			type=_UpperCamelCase				,			default=1				,			required=_UpperCamelCase				,			help='''How many checkpoints to save'''      )
         parser.add_argument(
             '''--early_stopping_patience'''				,			type=_UpperCamelCase				,			default=-1				,			required=_UpperCamelCase				,			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 		SCREAMING_SNAKE_CASE__				(       __A  ):
      A_						:				Optional[int]							=    "translation"
      A_						:				Dict							=    ["loss"]
      A_						:				str							=    ["bleu"]
      A_						:				List[str]							=    "bleu"


      def __init__(self       :      Optional[Any]				,			a__       :      Any				,			**a__       :      Any      ):





         """simple docstring"""



         super().__init__(_UpperCamelCase				,			**_UpperCamelCase      )
         __snake_case							       =   hparams.src_lang
         __snake_case							       =   hparams.tgt_lang


      def    a (self       :      List[Any]				,			a__       :      Optional[Any]				,			a__       :      Optional[Any]      ):





         """simple docstring"""



         return calculate_bleu(_UpperCamelCase				,			_UpperCamelCase      )






def lowerCamelCase__    (							snake_case_	:  Any       ,      snake_case_	:  str=None       )      ->				List[Any]:
   Path(args.output_dir       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
   check_output_dir(SCREAMING_SNAKE_CASE__       ,      expected_items=3       )

   if model is None:
      if "summarization" in args.task:
         __snake_case							       =   SummarizationModule(SCREAMING_SNAKE_CASE__       )
      else:
         __snake_case							       =   TranslationModule(SCREAMING_SNAKE_CASE__       )
   __snake_case							       =   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'''       )
   ):
      __snake_case							       =   True  # don't pollute wandb logs unnecessarily
   elif args.logger_name == "wandb":
      from pytorch_lightning.loggers import WandbLogger

      __snake_case							       =   os.environ.get('''WANDB_PROJECT'''       ,      SCREAMING_SNAKE_CASE__       )
      __snake_case							       =   WandbLogger(name=model.output_dir.name       ,      project=SCREAMING_SNAKE_CASE__       )

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

      __snake_case							       =   WandbLogger(name=model.output_dir.name       ,      project=f"""hf_{dataset}"""       )

   if args.early_stopping_patience >= 0:
      __snake_case							       =   get_early_stopping_callback(model.val_metric       ,      args.early_stopping_patience       )
   else:
      __snake_case							       =   False

   __snake_case							       =   args.val_metric == '''loss'''
   __snake_case							       =   generic_train(
       SCREAMING_SNAKE_CASE__       ,      SCREAMING_SNAKE_CASE__       ,      logging_callback=SeqaSeqLoggingCallback()       ,      checkpoint_callback=get_checkpoint_callback(
           args.output_dir       ,      model.val_metric       ,      args.save_top_k       ,      SCREAMING_SNAKE_CASE__       )       ,      early_stopping_callback=SCREAMING_SNAKE_CASE__       ,      logger=SCREAMING_SNAKE_CASE__       ,      )
   pickle_save(model.hparams       ,      model.output_dir / '''hparams.pkl'''       )
   if not args.do_predict:
      return model

   __snake_case							       =   ''''''
   __snake_case							       =   sorted(glob.glob(os.path.join(args.output_dir       ,      '''*.ckpt'''       )       ,      recursive=SCREAMING_SNAKE_CASE__       )       )
   if checkpoints:
      __snake_case							       =   checkpoints[-1]
      __snake_case							       =   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_				=			argparse.ArgumentParser()
    snake_case_				=			pl.Trainer.add_argparse_args(parser)
    snake_case_				=			SummarizationModule.add_model_specific_args(parser, os.getcwd())

    snake_case_				=			parser.parse_args()

    main(args)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''XGLMTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''XGLMTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''XGLMForCausalLM''',
					    '''XGLMModel''',
					    '''XGLMPreTrainedModel''',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxXGLMForCausalLM''',
					    '''FlaxXGLMModel''',
					    '''FlaxXGLMPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFXGLMForCausalLM''',
					    '''TFXGLMModel''',
					    '''TFXGLMPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm import XGLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm_fast import XGLMTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xglm import (
										    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXGLMForCausalLM,
										    TFXGLMModel,
										    TFXGLMPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)