





import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_UpperCAmelCase				       = logging.getLogger()
_UpperCAmelCase				       = torch.cuda.is_available()
_UpperCAmelCase				       = {
    'max_tokens_per_batch': None,
    'supervise_forward': True,
    'normalize_hidden': True,
    'label_smoothing': 0.2,
    'eval_max_gen_length': None,
    'eval_beams': 1,
    'val_metric': 'loss',
    'save_top_k': 1,
    'adafactor': True,
    'early_stopping_patience': 2,
    'logger_name': 'default',
    'length_penalty': 0.5,
    'cache_dir': '',
    'task': 'summarization',
    'num_workers': 2,
    'alpha_hid': 0,
    'freeze_embeds': True,
    'enc_only': False,
    'tgt_suffix': '',
    'resume_from_checkpoint': None,
    'sortish_sampler': True,
    'student_decoder_layers': 1,
    'val_check_interval': 1.0,
    'output_dir': '',
    'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    'no_teacher': False,
    'fp16_opt_level': 'O1',
    'gpus': 1 if CUDA_AVAILABLE else 0,
    'n_tpu_cores': 0,
    'max_grad_norm': 1.0,
    'do_train': True,
    'do_predict': True,
    'accumulate_grad_batches': 1,
    'server_ip': '',
    'server_port': '',
    'seed': 4_2,
    'model_name_or_path': 'sshleifer/bart-tiny-random',
    'config_name': '',
    'tokenizer_name': 'facebook/bart-large',
    'do_lower_case': False,
    'learning_rate': 0.3,
    'lr_scheduler': 'linear',
    'weight_decay': 0.0,
    'adam_epsilon': 1e-08,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 1_2,
    'max_target_length': 1_2,
    'val_max_target_length': 1_2,
    'test_max_target_length': 1_2,
    'fast_dev_run': False,
    'no_cache': False,
    'n_train': -1,
    'n_val': -1,
    'n_test': -1,
    'student_encoder_layers': 1,
    'freeze_encoder': False,
    'auto_scale_batch_size': False,
    'overwrite_output_dir': False,
    'student': None,
}




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						List[Any]:
       UpperCamelCase_      				=     "\n".join(UpperCamelCase_     )
       Path(UpperCamelCase_     ).open("w"     ).writelines(UpperCamelCase_     )


_UpperCAmelCase				       = [' Sam ate lunch today.', 'Sams lunch ingredients.']
_UpperCAmelCase				       = ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_UpperCAmelCase				       = 'patrickvonplaten/t5-tiny-random'
_UpperCAmelCase				       = 'sshleifer/t5-tinier-random'
_UpperCAmelCase				       = 'sshleifer/bart-tiny-random'
_UpperCAmelCase				       = 'sshleifer/tiny-mbart'
_UpperCAmelCase				       = 'sshleifer/tiny-marian-en-de'
_UpperCAmelCase				       = 'stas/tiny-wmt19-en-de'


_UpperCAmelCase				       = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						List[Any]:
       for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(UpperCamelCase_    ,						F'''{split}.source'''     )    ,						UpperCamelCase_     )
              _dump_articles(os.path.join(UpperCamelCase_    ,						F'''{split}.target'''     )    ,						UpperCamelCase_     )
       return tmp_dir



class 							_UpperCamelCase    (		lowerCAmelCase_   ):

       @classmethod
       def lowercase       (						cls:       Any						)				->       Union[str, Any]:




              """simple docstring"""


              logging.disable(logging.CRITICAL						)  # remove noisy download output from tracebacks
              return cls

       @slow
       @require_torch_gpu
       def lowercase       (						self:       List[str]						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     list_models()
              UpperCamelCase_      				=     "sshleifer"
              UpperCamelCase_      				=     [x.modelId for x in model_list if x.modelId.startswith(_SCREAMING_SNAKE_CASE						)]
              UpperCamelCase_      				=     ["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
              UpperCamelCase_      				=     []
              for m in model_ids:
                     if m in allowed_to_be_broken:
                            continue
                     try:
                            AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE						)
                     except Exception:
                            failures.append(_SCREAMING_SNAKE_CASE						)
              assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''

       def lowercase       (						self:       List[str]						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_      				=     {"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
              self._test_distiller_cli(_SCREAMING_SNAKE_CASE						)

       def lowercase       (						self:       Optional[int]						)				->       int:




              """simple docstring"""


              UpperCamelCase_      				=     {
                  "student_encoder_layers": 2,
                  "student_decoder_layers": 1,
                  "max_epochs": 4,
                  "val_check_interval": 0.25,
                  "alpha_hid": 2.0,
                  "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED",
              }
              UpperCamelCase_      				=     self._test_distiller_cli(_SCREAMING_SNAKE_CASE    ,      check_contents=_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     list(Path(model.output_dir						).glob("*.ckpt"						)						)
              self.assertEqual(1    ,      len(_SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase_      				=     list(Path(model.output_dir						).glob("**/*.bin"						)						)
              self.assertEqual(len(_SCREAMING_SNAKE_CASE						)    ,      2						)
              UpperCamelCase_      				=     lmap(str.strip    ,      Path(model.hparams.data_dir						).joinpath("test.source"						).open().readlines()						)
              UpperCamelCase_      				=     tempfile.mktemp()  # XXX: not being cleaned up
              generate_summaries_or_translations(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      str(model.output_dir / "best_tfmr"						)						)
              self.assertTrue(Path(_SCREAMING_SNAKE_CASE						).exists()						)

              UpperCamelCase_      				=     self.get_auto_remove_tmp_dir()
              convert_pl_to_hf(ckpts[0]    ,      transformer_ckpts[0].parent    ,      _SCREAMING_SNAKE_CASE						)
              assert os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,      "pytorch_model.bin"						)						)

       def lowercase       (						self:       List[Any]						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_      				=     AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_				,       UpperCamelCase_      				=     model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
              UpperCamelCase_      				=     torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]    ,      dtype=torch.long    ,      device=model.device						)
              UpperCamelCase_      				=     target_ids[:, :-1].contiguous()  # Why this line?
              UpperCamelCase_      				=     target_ids[:, 1:].clone()  # why clone?
              UpperCamelCase_      				=     model(
                  _SCREAMING_SNAKE_CASE    ,      attention_mask=_SCREAMING_SNAKE_CASE    ,      decoder_input_ids=_SCREAMING_SNAKE_CASE    ,      labels=_SCREAMING_SNAKE_CASE    ,      use_cache=_SCREAMING_SNAKE_CASE						).loss

              UpperCamelCase_      				=     model(_SCREAMING_SNAKE_CASE    ,      attention_mask=_SCREAMING_SNAKE_CASE    ,      decoder_input_ids=_SCREAMING_SNAKE_CASE    ,      use_cache=_SCREAMING_SNAKE_CASE						).logits

              UpperCamelCase_      				=     nn.functional.log_softmax(_SCREAMING_SNAKE_CASE    ,      dim=-1						)
              UpperCamelCase_				,       UpperCamelCase_      				=     label_smoothed_nll_loss(
                  _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      0.1    ,      ignore_index=model.config.pad_token_id						)
              with self.assertRaises(_SCREAMING_SNAKE_CASE						):
                     # TODO: understand why this breaks
                     self.assertEqual(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

       def lowercase       (						self:       Tuple						)				->       Any:




              """simple docstring"""


              UpperCamelCase_      				=     {
                  "student_encoder_layers": 2,
                  "student_decoder_layers": 1,
                  "num_train_epochs": 4,
                  "val_check_interval": 0.25,
                  "alpha_hid": 2.0,
                  "task": "translation",
                  "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED",
                  "tokenizer_name": MBART_TINY,
                  "teacher": MBART_TINY,
                  "src_lang": "en_XX",
                  "tgt_lang": "ro_RO",
              }
              UpperCamelCase_      				=     self._test_distiller_cli(_SCREAMING_SNAKE_CASE    ,      check_contents=_SCREAMING_SNAKE_CASE						)
              assert model.model.config.model_type == "mbart"

              UpperCamelCase_      				=     list(Path(model.output_dir						).glob("*.ckpt"						)						)
              self.assertEqual(1    ,      len(_SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase_      				=     list(Path(model.output_dir						).glob("**/*.bin"						)						)
              UpperCamelCase_      				=     list(Path(model.output_dir						).glob("best_tfmr/*"						)						)
              assert len(_SCREAMING_SNAKE_CASE						) > 2
              self.assertEqual(len(_SCREAMING_SNAKE_CASE						)    ,      2						)

       def lowercase       (						self:       Optional[int]						)				->       Dict:




              """simple docstring"""


              UpperCamelCase_      				=     {
                  "student_encoder_layers": 1,
                  "student_decoder_layers": 1,
                  "alpha_hid": 2.0,
                  "teacher": T5_TINY,
                  "model_name_or_path": T5_TINY,
                  "tokenizer_name": T5_TINY,
              }
              self._test_distiller_cli(_SCREAMING_SNAKE_CASE						)

       def lowercase       (						self:       Optional[Any]						)				->       List[str]:




              """simple docstring"""


              UpperCamelCase_      				=     {
                  "teacher": T5_TINY,
                  "student": T5_TINIER,
                  "model_name_or_path": T5_TINIER,
                  "tokenizer_name": T5_TINIER,
              }
              self._test_distiller_cli(_SCREAMING_SNAKE_CASE						)



       def lowercase       (						self:       List[str]    ,      _SCREAMING_SNAKE_CASE:       Optional[int]    ,      _SCREAMING_SNAKE_CASE:       Dict=True						)				->       Any:




              """simple docstring"""


              UpperCamelCase_      				=     {
                  "label_smoothing": 0.0,
                  "early_stopping_patience": -1,
                  "train_batch_size": 1,
                  "eval_batch_size": 2,
                  "max_epochs": 2,
                  "alpha_mlm": 0.2,
                  "alpha_ce": 0.8,
                  "do_predict": True,
                  "model_name_or_path": "sshleifer/tinier_bart",
                  "teacher": CHEAP_ARGS["model_name_or_path"],
                  "val_check_interval": 0.5,
              }
              default_updates.update(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     CHEAP_ARGS.copy()
              UpperCamelCase_      				=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
              UpperCamelCase_      				=     self.get_auto_remove_tmp_dir()

              args_d.update(data_dir=_SCREAMING_SNAKE_CASE    ,      output_dir=_SCREAMING_SNAKE_CASE    ,      **_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     distill_main(argparse.Namespace(**_SCREAMING_SNAKE_CASE						)						)
              if not check_contents:
                     return model
              UpperCamelCase_      				=     os.listdir(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     {os.path.basename(_SCREAMING_SNAKE_CASE						) for p in contents}
              UpperCamelCase_      				=     [p for p in contents if p.endswith("ckpt"						)]
              assert len(_SCREAMING_SNAKE_CASE						) > 0

              self.assertIn("test_generations.txt"    ,      _SCREAMING_SNAKE_CASE						)
              self.assertIn("test_results.txt"    ,      _SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     load_json(model.metrics_save_path						)
              UpperCamelCase_      				=     metrics["val"][-1]
              self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]    ,      0.01						)
              self.assertGreaterEqual(1.0    ,      last_step_stats["val_avg_gen_time"]						)
              self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']    ,      _SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1						)
              self.assertEqual(len(metrics["val"]						)    ,      _SCREAMING_SNAKE_CASE						)
              self.assertEqual(len(metrics["test"]						)    ,      1						)
              return model



class 							_UpperCamelCase    (		lowerCAmelCase_   ):

       @parameterized.expand(
           [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,      )
       def lowercase       (						self:       Any    ,      _SCREAMING_SNAKE_CASE:       Dict						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     CHEAP_ARGS.copy()
              UpperCamelCase_      				=     "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
              UpperCamelCase_      				=     0.1 if task == "translation" else 0

              UpperCamelCase_      				=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
              UpperCamelCase_      				=     self.get_auto_remove_tmp_dir()
              args_d.update(
                  data_dir=_SCREAMING_SNAKE_CASE    ,      model_name_or_path=_SCREAMING_SNAKE_CASE    ,      tokenizer_name=_SCREAMING_SNAKE_CASE    ,      train_batch_size=2    ,      eval_batch_size=2    ,      output_dir=_SCREAMING_SNAKE_CASE    ,      do_predict=_SCREAMING_SNAKE_CASE    ,      task=_SCREAMING_SNAKE_CASE    ,      src_lang="en_XX"    ,      tgt_lang="ro_RO"    ,      freeze_encoder=_SCREAMING_SNAKE_CASE    ,      freeze_embeds=_SCREAMING_SNAKE_CASE    ,      )
              assert "n_train" in args_d
              UpperCamelCase_      				=     argparse.Namespace(**_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     main(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     module.model.get_input_embeddings()
              assert not input_embeds.weight.requires_grad
              if model == T5_TINY:
                     UpperCamelCase_      				=     module.model.lm_head
                     assert not lm_head.weight.requires_grad
                     assert (lm_head.weight == input_embeds.weight).all().item()
              elif model == FSMT_TINY:
                     UpperCamelCase_      				=     module.model.model
                     UpperCamelCase_      				=     fsmt.decoder.embed_positions
                     assert not embed_pos.weight.requires_grad
                     assert not fsmt.decoder.embed_tokens.weight.requires_grad
                     # check that embeds are not the same
                     assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
              else:
                     UpperCamelCase_      				=     module.model.model
                     UpperCamelCase_      				=     bart.decoder.embed_positions
                     assert not embed_pos.weight.requires_grad
                     assert not bart.shared.weight.requires_grad
                     # check that embeds are the same
                     assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
                     assert bart.decoder.embed_tokens == bart.shared

              UpperCamelCase_      				=     load_json(module.output_dir / "text_batch.json"						)
              assert isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)
              assert len(_SCREAMING_SNAKE_CASE						) >= 4

       def lowercase       (						self:       Optional[Any]						)				->       Dict:




              """simple docstring"""


              UpperCamelCase_      				=     CHEAP_ARGS.copy()

              UpperCamelCase_      				=     "summarization"
              UpperCamelCase_      				=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)

              args_d.update(
                  data_dir=_SCREAMING_SNAKE_CASE    ,      tokenizer_name=_SCREAMING_SNAKE_CASE    ,      train_batch_size=2    ,      eval_batch_size=2    ,      do_predict=_SCREAMING_SNAKE_CASE    ,      task=_SCREAMING_SNAKE_CASE    ,      src_lang="en_XX"    ,      tgt_lang="ro_RO"    ,      freeze_encoder=_SCREAMING_SNAKE_CASE    ,      freeze_embeds=_SCREAMING_SNAKE_CASE    ,      )

              # test models whose config includes the extra_model_args
              UpperCamelCase_      				=     BART_TINY
              UpperCamelCase_      				=     self.get_auto_remove_tmp_dir()
              UpperCamelCase_      				=     args_d.copy()
              args_da.update(
                  model_name_or_path=_SCREAMING_SNAKE_CASE    ,      output_dir=_SCREAMING_SNAKE_CASE    ,      )
              UpperCamelCase_      				=     ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
              for p in extra_model_params:
                     UpperCamelCase_      				=     0.5
              UpperCamelCase_      				=     argparse.Namespace(**_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     main(_SCREAMING_SNAKE_CASE						)
              for p in extra_model_params:
                     assert getattr(model.config    ,      _SCREAMING_SNAKE_CASE						) == 0.5, f'''failed to override the model config for param {p}'''

              # test models whose config doesn't include the extra_model_args
              UpperCamelCase_      				=     T5_TINY
              UpperCamelCase_      				=     self.get_auto_remove_tmp_dir()
              UpperCamelCase_      				=     args_d.copy()
              args_da.update(
                  model_name_or_path=_SCREAMING_SNAKE_CASE    ,      output_dir=_SCREAMING_SNAKE_CASE    ,      )
              UpperCamelCase_      				=     "encoder_layerdrop"
              UpperCamelCase_      				=     0.5
              UpperCamelCase_      				=     argparse.Namespace(**_SCREAMING_SNAKE_CASE						)
              with pytest.raises(_SCREAMING_SNAKE_CASE						) as excinfo:
                     UpperCamelCase_      				=     main(_SCREAMING_SNAKE_CASE						)
              assert str(excinfo.value						) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''



       def lowercase       (						self:       Union[str, Any]						)				->       str:




              """simple docstring"""


              UpperCamelCase_      				=     CHEAP_ARGS.copy()

              UpperCamelCase_      				=     "summarization"
              UpperCamelCase_      				=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)

              UpperCamelCase_      				=     BART_TINY
              UpperCamelCase_      				=     self.get_auto_remove_tmp_dir()

              args_d.update(
                  data_dir=_SCREAMING_SNAKE_CASE    ,      model_name_or_path=_SCREAMING_SNAKE_CASE    ,      output_dir=_SCREAMING_SNAKE_CASE    ,      tokenizer_name=_SCREAMING_SNAKE_CASE    ,      train_batch_size=2    ,      eval_batch_size=2    ,      do_predict=_SCREAMING_SNAKE_CASE    ,      task=_SCREAMING_SNAKE_CASE    ,      src_lang="en_XX"    ,      tgt_lang="ro_RO"    ,      freeze_encoder=_SCREAMING_SNAKE_CASE    ,      freeze_embeds=_SCREAMING_SNAKE_CASE    ,      )

              # emulate finetune.py
              UpperCamelCase_      				=     argparse.ArgumentParser()
              UpperCamelCase_      				=     pl.Trainer.add_argparse_args(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     SummarizationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE    ,      os.getcwd()						)
              UpperCamelCase_      				=     {"--help": True}

              # --help test
              with pytest.raises(_SCREAMING_SNAKE_CASE						) as excinfo:
                     with CaptureStdout() as cs:
                            UpperCamelCase_      				=     parser.parse_args(_SCREAMING_SNAKE_CASE						)
                     assert False, "--help is expected to sys.exit"
              assert excinfo.type == SystemExit
              UpperCamelCase_      				=     lightning_base.arg_to_scheduler_metavar
              assert expected in cs.out, "--help is expected to list the supported schedulers"

              # --lr_scheduler=non_existing_scheduler test
              UpperCamelCase_      				=     "non_existing_scheduler"
              UpperCamelCase_      				=     {f'''--lr_scheduler={unsupported_param}'''}
              with pytest.raises(_SCREAMING_SNAKE_CASE						) as excinfo:
                     with CaptureStderr() as cs:
                            UpperCamelCase_      				=     parser.parse_args(_SCREAMING_SNAKE_CASE						)
                     assert False, "invalid argument is expected to sys.exit"
              assert excinfo.type == SystemExit
              UpperCamelCase_      				=     f'''invalid choice: \'{unsupported_param}\''''
              assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

              # --lr_scheduler=existing_scheduler test
              UpperCamelCase_      				=     "cosine"
              UpperCamelCase_      				=     args_d.copy()
              UpperCamelCase_      				=     supported_param
              UpperCamelCase_      				=     argparse.Namespace(**_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     main(_SCREAMING_SNAKE_CASE						)
              assert (
                  getattr(model.hparams    ,      "lr_scheduler"						) == supported_param
              ), f'''lr_scheduler={supported_param} shouldn\'t fail'''








from typing import TYPE_CHECKING

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


_UpperCAmelCase				       = {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MPNetForMaskedLM',
         'MPNetForMultipleChoice',
         'MPNetForQuestionAnswering',
         'MPNetForSequenceClassification',
         'MPNetForTokenClassification',
         'MPNetLayer',
         'MPNetModel',
         'MPNetPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMPNetEmbeddings',
         'TFMPNetForMaskedLM',
         'TFMPNetForMultipleChoice',
         'TFMPNetForQuestionAnswering',
         'TFMPNetForSequenceClassification',
         'TFMPNetForTokenClassification',
         'TFMPNetMainLayer',
         'TFMPNetModel',
         'TFMPNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
     from .tokenization_mpnet import MPNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mpnet_fast import MPNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mpnet import (
              MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetLayer,
              MPNetModel,
              MPNetPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mpnet import (
              TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMPNetEmbeddings,
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetMainLayer,
              TFMPNetModel,
              TFMPNetPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase				       = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

