code: 
  
  
  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)
  
  SCREAMING_SNAKE_CASE : Optional[Any]          =    logging.getLogger()
  SCREAMING_SNAKE_CASE : Optional[int]          =    torch.cuda.is_available()
  SCREAMING_SNAKE_CASE : Optional[Any]          =    {
      "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": 42,
      "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-0_8,
      "warmup_steps": 0,
      "max_epochs": 1,
      "train_batch_size": 2,
      "eval_batch_size": 2,
      "max_source_length": 12,
      "max_target_length": 12,
      "val_max_target_length": 12,
      "test_max_target_length": 12,
      "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		(							SCREAMING_SNAKE_CASE_				: Path	,	SCREAMING_SNAKE_CASE_				: list      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Optional[int]			=	"""\n""".join(SCREAMING_SNAKE_CASE_      )
  							Path(SCREAMING_SNAKE_CASE_      ).open("""w"""      ).writelines(SCREAMING_SNAKE_CASE_      )
  
  
  SCREAMING_SNAKE_CASE : Any          =    [" Sam ate lunch today.", "Sams lunch ingredients."]
  SCREAMING_SNAKE_CASE : List[Any]          =    ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  SCREAMING_SNAKE_CASE : Union[str, Any]          =    "patrickvonplaten/t5-tiny-random"
  SCREAMING_SNAKE_CASE : List[str]          =    "sshleifer/t5-tinier-random"
  SCREAMING_SNAKE_CASE : str          =    "sshleifer/bart-tiny-random"
  SCREAMING_SNAKE_CASE : int          =    "sshleifer/tiny-mbart"
  SCREAMING_SNAKE_CASE : Any          =    "sshleifer/tiny-marian-en-de"
  SCREAMING_SNAKE_CASE : str          =    "stas/tiny-wmt19-en-de"
  
  
  SCREAMING_SNAKE_CASE : Union[str, Any]          =    logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Union[str, Any]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							for split in ["train", "val", "test"]:
  														_dump_articles(os.path.join(SCREAMING_SNAKE_CASE_	,	F"""{split}.source"""      )	,	SCREAMING_SNAKE_CASE_      )
  														_dump_articles(os.path.join(SCREAMING_SNAKE_CASE_	,	F"""{split}.target"""      )	,	SCREAMING_SNAKE_CASE_      )
  							return tmp_dir
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  
  
  
  
  
  	@classmethod
  	def     A				(  cls							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								logging.disable(logging.CRITICAL							)  # remove noisy download output from tracebacks
  								return cls
  
  
  
  
  
  	@slow
  	@require_torch_gpu
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	list_models()
  								a_       :   int			=	"""sshleifer"""
  								a_       :   Optional[int]			=	[x.modelId for x in model_list if x.modelId.startswith(UpperCamelCase_							)]
  								a_       :   Tuple			=	["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
  								a_       :   str			=	[]
  								for m in model_ids:
  															if m in allowed_to_be_broken:
  																						continue
  															try:
  																						AutoConfig.from_pretrained(UpperCamelCase_							)
  															except Exception:
  																						failures.append(UpperCamelCase_							)
  								assert not failures, f"""The following models could not be loaded through AutoConfig: {failures}"""
  
  
  
  
  
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[str]			=	{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
  								self._test_distiller_cli(UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[str]			=	{
  								    """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""",
  								}
  								a_       :   int			=	self._test_distiller_cli(UpperCamelCase_				,    check_contents=UpperCamelCase_							)
  
  								a_       :   Dict			=	list(Path(model.output_dir							).glob("""*.ckpt"""							)							)
  								self.assertEqual(1				,    len(UpperCamelCase_							)							)
  								a_       :   Tuple			=	list(Path(model.output_dir							).glob("""**/*.bin"""							)							)
  								self.assertEqual(len(UpperCamelCase_							)				,    2							)
  								a_       :   List[str]			=	lmap(str.strip				,    Path(model.hparams.data_dir							).joinpath("""test.source"""							).open().readlines()							)
  								a_       :   Optional[int]			=	tempfile.mktemp()  # XXX: not being cleaned up
  								generate_summaries_or_translations(UpperCamelCase_				,    UpperCamelCase_				,    str(model.output_dir / """best_tfmr"""							)							)
  								self.assertTrue(Path(UpperCamelCase_							).exists()							)
  
  								a_       :   Union[str, Any]			=	self.get_auto_remove_tmp_dir()
  								convert_pl_to_hf(ckpts[0]				,    transformer_ckpts[0].parent				,    UpperCamelCase_							)
  								assert os.path.exists(os.path.join(UpperCamelCase_				,    """pytorch_model.bin"""							)							)
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[str]			=	AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)
  								a_		,				a_       :   List[Any]			=	model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
  								a_       :   Any			=	torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]				,    dtype=torch.long				,    device=model.device							)
  								a_       :   Any			=	target_ids[:, :-1].contiguous()  # Why this line?
  								a_       :   Optional[int]			=	target_ids[:, 1:].clone()  # why clone?
  								a_       :   int			=	model(
  								    UpperCamelCase_				,    attention_mask=UpperCamelCase_				,    decoder_input_ids=UpperCamelCase_				,    labels=UpperCamelCase_				,    use_cache=UpperCamelCase_							).loss
  
  								a_       :   Optional[Any]			=	model(UpperCamelCase_				,    attention_mask=UpperCamelCase_				,    decoder_input_ids=UpperCamelCase_				,    use_cache=UpperCamelCase_							).logits
  
  								a_       :   Any			=	nn.functional.log_softmax(UpperCamelCase_				,    dim=-1							)
  								a_		,				a_       :   Dict			=	label_smoothed_nll_loss(
  								    UpperCamelCase_				,    UpperCamelCase_				,    0.1				,    ignore_index=model.config.pad_token_id							)
  								with self.assertRaises(UpperCamelCase_							):
  															# TODO: understand why this breaks
  															self.assertEqual(UpperCamelCase_				,    UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[Any]			=	{
  								    """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""",
  								}
  								a_       :   str			=	self._test_distiller_cli(UpperCamelCase_				,    check_contents=UpperCamelCase_							)
  								assert model.model.config.model_type == "mbart"
  
  								a_       :   Tuple			=	list(Path(model.output_dir							).glob("""*.ckpt"""							)							)
  								self.assertEqual(1				,    len(UpperCamelCase_							)							)
  								a_       :   Optional[int]			=	list(Path(model.output_dir							).glob("""**/*.bin"""							)							)
  								a_       :   Tuple			=	list(Path(model.output_dir							).glob("""best_tfmr/*"""							)							)
  								assert len(UpperCamelCase_							) > 2
  								self.assertEqual(len(UpperCamelCase_							)				,    2							)
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Tuple			=	{
  								    """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(UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	{
  								    """teacher""": T5_TINY,
  								    """student""": T5_TINIER,
  								    """model_name_or_path""": T5_TINIER,
  								    """tokenizer_name""": T5_TINIER,
  								}
  								self._test_distiller_cli(UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_=True							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Tuple			=	{
  								    """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(UpperCamelCase_							)
  								a_       :   dict			=	CHEAP_ARGS.copy()
  								a_       :   List[Any]			=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
  								a_       :   Optional[int]			=	self.get_auto_remove_tmp_dir()
  
  								args_d.update(data_dir=UpperCamelCase_				,    output_dir=UpperCamelCase_				,    **UpperCamelCase_							)
  								a_       :   Union[str, Any]			=	distill_main(argparse.Namespace(**UpperCamelCase_							)							)
  								if not check_contents:
  															return model
  								a_       :   List[Any]			=	os.listdir(UpperCamelCase_							)
  								a_       :   List[str]			=	{os.path.basename(UpperCamelCase_							) for p in contents}
  								a_       :   Dict			=	[p for p in contents if p.endswith("""ckpt"""							)]
  								assert len(UpperCamelCase_							) > 0
  
  								self.assertIn("""test_generations.txt"""				,    UpperCamelCase_							)
  								self.assertIn("""test_results.txt"""				,    UpperCamelCase_							)
  
  								a_       :   Union[str, Any]			=	load_json(model.metrics_save_path							)
  								a_       :   str			=	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}"""]				,    UpperCamelCase_							)
  								a_       :   Optional[int]			=	int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1							)
  								self.assertEqual(len(metrics["""val"""]							)				,    UpperCamelCase_							)
  								self.assertEqual(len(metrics["""test"""]							)				,    1							)
  								return model
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  
  
  
  
  
  	@parameterized.expand(
  	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]				,    )
  	def     A				(  self				,    UpperCamelCase_							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   dict			=	CHEAP_ARGS.copy()
  								a_       :   Optional[Any]			=	"""translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
  								a_       :   Union[str, Any]			=	0.1 if task == """translation""" else 0
  
  								a_       :   Tuple			=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
  								a_       :   Union[str, Any]			=	self.get_auto_remove_tmp_dir()
  								args_d.update(
  								    data_dir=UpperCamelCase_				,    model_name_or_path=UpperCamelCase_				,    tokenizer_name=UpperCamelCase_				,    train_batch_size=2				,    eval_batch_size=2				,    output_dir=UpperCamelCase_				,    do_predict=UpperCamelCase_				,    task=UpperCamelCase_				,    src_lang="""en_XX"""				,    tgt_lang="""ro_RO"""				,    freeze_encoder=UpperCamelCase_				,    freeze_embeds=UpperCamelCase_				,    )
  								assert "n_train" in args_d
  								a_       :   Tuple			=	argparse.Namespace(**UpperCamelCase_							)
  								a_       :   List[str]			=	main(UpperCamelCase_							)
  
  								a_       :   Any			=	module.model.get_input_embeddings()
  								assert not input_embeds.weight.requires_grad
  								if model == T5_TINY:
  															a_       :   int			=	module.model.lm_head
  															assert not lm_head.weight.requires_grad
  															assert (lm_head.weight == input_embeds.weight).all().item()
  								elif model == FSMT_TINY:
  															a_       :   Any			=	module.model.model
  															a_       :   Any			=	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:
  															a_       :   Dict			=	module.model.model
  															a_       :   Optional[Any]			=	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
  
  								a_       :   Optional[Any]			=	load_json(module.output_dir / """text_batch.json"""							)
  								assert isinstance(UpperCamelCase_				,    UpperCamelCase_							)
  								assert len(UpperCamelCase_							) >= 4
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   dict			=	CHEAP_ARGS.copy()
  
  								a_       :   Dict			=	"""summarization"""
  								a_       :   List[Any]			=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
  
  								args_d.update(
  								    data_dir=UpperCamelCase_				,    tokenizer_name=UpperCamelCase_				,    train_batch_size=2				,    eval_batch_size=2				,    do_predict=UpperCamelCase_				,    task=UpperCamelCase_				,    src_lang="""en_XX"""				,    tgt_lang="""ro_RO"""				,    freeze_encoder=UpperCamelCase_				,    freeze_embeds=UpperCamelCase_				,    )
  
  								# test models whose config includes the extra_model_args
  								a_       :   Union[str, Any]			=	BART_TINY
  								a_       :   int			=	self.get_auto_remove_tmp_dir()
  								a_       :   Dict			=	args_d.copy()
  								args_da.update(
  								    model_name_or_path=UpperCamelCase_				,    output_dir=UpperCamelCase_				,    )
  								a_       :   str			=	("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
  								for p in extra_model_params:
  															a_       :   Any			=	0.5
  								a_       :   Optional[int]			=	argparse.Namespace(**UpperCamelCase_							)
  								a_       :   Tuple			=	main(UpperCamelCase_							)
  								for p in extra_model_params:
  															assert getattr(model.config				,    UpperCamelCase_							) == 0.5, f"""failed to override the model config for param {p}"""
  
  								# test models whose config doesn't include the extra_model_args
  								a_       :   List[Any]			=	T5_TINY
  								a_       :   List[str]			=	self.get_auto_remove_tmp_dir()
  								a_       :   Union[str, Any]			=	args_d.copy()
  								args_da.update(
  								    model_name_or_path=UpperCamelCase_				,    output_dir=UpperCamelCase_				,    )
  								a_       :   Tuple			=	"""encoder_layerdrop"""
  								a_       :   Tuple			=	0.5
  								a_       :   Dict			=	argparse.Namespace(**UpperCamelCase_							)
  								with pytest.raises(UpperCamelCase_							) as excinfo:
  															a_       :   Optional[Any]			=	main(UpperCamelCase_							)
  								assert str(excinfo.value							) == f"""model config doesn't have a `{unsupported_param}` attribute"""
  
  
  
  
  
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   dict			=	CHEAP_ARGS.copy()
  
  								a_       :   Optional[int]			=	"""summarization"""
  								a_       :   Tuple			=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
  
  								a_       :   str			=	BART_TINY
  								a_       :   Optional[int]			=	self.get_auto_remove_tmp_dir()
  
  								args_d.update(
  								    data_dir=UpperCamelCase_				,    model_name_or_path=UpperCamelCase_				,    output_dir=UpperCamelCase_				,    tokenizer_name=UpperCamelCase_				,    train_batch_size=2				,    eval_batch_size=2				,    do_predict=UpperCamelCase_				,    task=UpperCamelCase_				,    src_lang="""en_XX"""				,    tgt_lang="""ro_RO"""				,    freeze_encoder=UpperCamelCase_				,    freeze_embeds=UpperCamelCase_				,    )
  
  								# emulate finetune.py
  								a_       :   int			=	argparse.ArgumentParser()
  								a_       :   Any			=	pl.Trainer.add_argparse_args(UpperCamelCase_							)
  								a_       :   List[str]			=	SummarizationModule.add_model_specific_args(UpperCamelCase_				,    os.getcwd()							)
  								a_       :   int			=	{"""--help""": True}
  
  								# --help test
  								with pytest.raises(UpperCamelCase_							) as excinfo:
  															with CaptureStdout() as cs:
  																						a_       :   Union[str, Any]			=	parser.parse_args(UpperCamelCase_							)
  															assert False, "--help is expected to sys.exit"
  								assert excinfo.type == SystemExit
  								a_       :   List[str]			=	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
  								a_       :   List[str]			=	"""non_existing_scheduler"""
  								a_       :   Optional[int]			=	{f"""--lr_scheduler={unsupported_param}"""}
  								with pytest.raises(UpperCamelCase_							) as excinfo:
  															with CaptureStderr() as cs:
  																						a_       :   int			=	parser.parse_args(UpperCamelCase_							)
  															assert False, "invalid argument is expected to sys.exit"
  								assert excinfo.type == SystemExit
  								a_       :   Tuple			=	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
  								a_       :   int			=	"""cosine"""
  								a_       :   Tuple			=	args_d.copy()
  								a_       :   List[str]			=	supported_param
  								a_       :   Any			=	argparse.Namespace(**UpperCamelCase_							)
  								a_       :   Optional[Any]			=	main(UpperCamelCase_							)
  								assert (
  								    getattr(model.hparams				,    """lr_scheduler"""							) == supported_param
  								), f"""lr_scheduler={supported_param} shouldn't fail"""
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  SCREAMING_SNAKE_CASE : Optional[Any]          =    {
      "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
  }
  
  try:
  	if not is_sentencepiece_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Optional[int]          =    ["CamembertTokenizer"]
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Any          =    ["CamembertTokenizerFast"]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Union[str, Any]          =    [
  	    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "CamembertForCausalLM",
  	    "CamembertForMaskedLM",
  	    "CamembertForMultipleChoice",
  	    "CamembertForQuestionAnswering",
  	    "CamembertForSequenceClassification",
  	    "CamembertForTokenClassification",
  	    "CamembertModel",
  	    "CamembertPreTrainedModel",
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : List[Any]          =    [
  	    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "TFCamembertForCausalLM",
  	    "TFCamembertForMaskedLM",
  	    "TFCamembertForMultipleChoice",
  	    "TFCamembertForQuestionAnswering",
  	    "TFCamembertForSequenceClassification",
  	    "TFCamembertForTokenClassification",
  	    "TFCamembertModel",
  	    "TFCamembertPreTrainedModel",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
  	try:
  		if not is_sentencepiece_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert import CamembertTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert_fast import CamembertTokenizerFast
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_camembert import (
  		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    CamembertForCausalLM,
  		    CamembertForMaskedLM,
  		    CamembertForMultipleChoice,
  		    CamembertForQuestionAnswering,
  		    CamembertForSequenceClassification,
  		    CamembertForTokenClassification,
  		    CamembertModel,
  		    CamembertPreTrainedModel,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_camembert import (
  		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFCamembertForCausalLM,
  		    TFCamembertForMaskedLM,
  		    TFCamembertForMultipleChoice,
  		    TFCamembertForQuestionAnswering,
  		    TFCamembertForSequenceClassification,
  		    TFCamembertForTokenClassification,
  		    TFCamembertModel,
  		    TFCamembertPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	SCREAMING_SNAKE_CASE : Any          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  