





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__UpperCAmelCase	:       Optional[Any]    	=							logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

__UpperCAmelCase	:       List[Any]    	=							{
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__UpperCAmelCase	:       int    	=							{
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__UpperCAmelCase	:       Optional[Any]    	=							sorted(arg_to_scheduler.keys())
__UpperCAmelCase	:       Union[str, Any]    	=							"{" + ", ".join(arg_to_scheduler_choices) + "}"







class    __snake_case	(							pl.LightningModule      ):





       '''simple docstring'''







       def __init__(  self			:   Dict							,    A			:   argparse.Namespace							,    A			:   Optional[Any]=None							,    A			:   Optional[Any]="base"							,    A			:   List[str]=None							,    A			:   Union[str, Any]=None							,    A			:   List[str]=None							,    **A			:   int							,    ):
         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(A		)
         __snake_case:						Tuple			  =						0
         __snake_case:						Tuple			  =						Path(self.hparams.output_dir		)
         __snake_case:						Tuple			  =						self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
           __snake_case:						Union[str, Any]			  =						AutoConfig.from_pretrained(
               self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path							,    **({"""num_labels""": num_labels} if num_labels is not None else {})							,    cache_dir=A							,    **A							,    )
         else:
           __snake_case:						PretrainedConfig			  =						config

         __snake_case:						Any			  =						("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
         for p in extra_model_params:
           if getattr(self.hparams							,    A							,    A		):
             assert hasattr(self.config							,    A		), f'''model config doesn\'t have a `{p}` attribute'''
             setattr(self.config							,    A							,    getattr(self.hparams							,    A		)		)

         if tokenizer is None:
           __snake_case:						Tuple			  =						AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,    cache_dir=A							,    )
         else:
           __snake_case:						PreTrainedTokenizer			  =						tokenizer
         __snake_case:						Union[str, Any]			  =						MODEL_MODES[mode]
         if model is None:
           __snake_case:						List[str]			  =						self.model_type.from_pretrained(
               self.hparams.model_name_or_path							,    from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path		)							,    config=self.config							,    cache_dir=A							,    )
         else:
           __snake_case:						List[Any]			  =						model




       def 							UpperCAmelCase__			(  self			:   Tuple							,    *A			:   int							,    **A			:   Optional[int]		):
         __snake_case:						int			  =						self.model_type.from_pretrained(*A							,    **A		)




       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         __snake_case:						str			  =						arg_to_scheduler[self.hparams.lr_scheduler]
         __snake_case:						Optional[Any]			  =						get_schedule_func(
             self.opt							,    num_warmup_steps=self.hparams.warmup_steps							,    num_training_steps=self.total_steps()		)
         __snake_case:						str			  =						{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
         return scheduler




       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						List[str]			  =						self.model
         __snake_case:						Tuple			  =						["""bias""", """LayerNorm.weight"""]
         __snake_case:						Tuple			  =						[
             {
                 """params""": [
                     p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)
                 ],  # check this named paramters
                 """weight_decay""": self.hparams.weight_decay,
             },
             {
                 """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)],
                 """weight_decay""": 0.0,
             },
         ]
         if self.hparams.adafactor:
           __snake_case:						Optional[Any]			  =						Adafactor(
               A							,    lr=self.hparams.learning_rate							,    scale_parameter=A							,    relative_step=A		)

         else:
           __snake_case:						List[Any]			  =						AdamW(
               A							,    lr=self.hparams.learning_rate							,    eps=self.hparams.adam_epsilon		)
         __snake_case:						List[str]			  =						optimizer

         __snake_case:						Dict			  =						self.get_lr_scheduler()

         return [optimizer], [scheduler]




       def 							UpperCAmelCase__			(  self			:   Any							,    A			:   Tuple							,    A			:   int		):
         return self.validation_step(A							,    A		)




       def 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   Tuple		):
         return self.validation_end(A		)




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						str			  =						max(1							,    self.hparams.gpus		)  # TODO: consider num_tpu_cores
         __snake_case:						str			  =						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




       def 							UpperCAmelCase__			(  self			:   str							,    A			:   int		):
         if stage == "test":
           __snake_case:						str			  =						len(self.test_dataloader().dataset		)
         else:
           __snake_case:						Optional[Any]			  =						self.get_dataloader("""train"""							,    self.hparams.train_batch_size							,    shuffle=A		)
           __snake_case:						List[str]			  =						len(self.train_dataloader().dataset		)




       def 							UpperCAmelCase__			(  self			:   str							,    A			:   str							,    A			:   int							,    A			:   bool = False		):
         raise NotImplementedError("""You must implement this for your task"""		)




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         return self.train_loader




       def 							UpperCAmelCase__			(  self			:   Dict		):
         return self.get_dataloader("""dev"""							,    self.hparams.eval_batch_size							,    shuffle=A		)




       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         return self.get_dataloader("""test"""							,    self.hparams.eval_batch_size							,    shuffle=A		)




       def 							UpperCAmelCase__			(  self			:   Dict							,    A			:   List[str]		):
         return os.path.join(
             self.hparams.data_dir							,    """cached_{}_{}_{}""".format(
                 A							,    list(filter(A							,    self.hparams.model_name_or_path.split("""/"""		)		)		).pop()							,    str(self.hparams.max_seq_length		)							,    )							,    )




       @pl.utilities.rank_zero_only
       def 							UpperCAmelCase__			(  self			:   Tuple							,    A			:   Dict[str, Any]		):
         __snake_case:						Optional[int]			  =						self.output_dir.joinpath("""best_tfmr"""		)
         __snake_case:						Optional[int]			  =						self.step_count
         self.model.save_pretrained(A		)
         self.tokenizer.save_pretrained(A		)






       @staticmethod
       def 							UpperCAmelCase__			(  A			:   List[Any]							,    A			:   List[str]		):
         parser.add_argument(
             """--model_name_or_path"""							,    default=A							,    type=A							,    required=A							,    help="""Path to pretrained model or model identifier from huggingface.co/models"""							,    )
         parser.add_argument(
             """--config_name"""							,    default=""""""							,    type=A							,    help="""Pretrained config name or path if not the same as model_name"""		)
         parser.add_argument(
             """--tokenizer_name"""							,    default=A							,    type=A							,    help="""Pretrained tokenizer name or path if not the same as model_name"""							,    )
         parser.add_argument(
             """--cache_dir"""							,    default=str(Path(A		).parent / """test_run""" / """cache"""		)							,    type=A							,    help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""							,    )
         parser.add_argument(
             """--encoder_layerdrop"""							,    type=A							,    help="""Encoder layer dropout probability (Optional). Goes into model.config"""							,    )
         parser.add_argument(
             """--decoder_layerdrop"""							,    type=A							,    help="""Decoder layer dropout probability (Optional). Goes into model.config"""							,    )
         parser.add_argument(
             """--dropout"""							,    type=A							,    help="""Dropout probability (Optional). Goes into model.config"""							,    )
         parser.add_argument(
             """--attention_dropout"""							,    type=A							,    help="""Attention dropout probability (Optional). Goes into model.config"""							,    )
         parser.add_argument("""--learning_rate"""							,    default=5E-5							,    type=A							,    help="""The initial learning rate for Adam."""		)
         parser.add_argument(
             """--lr_scheduler"""							,    default="""linear"""							,    choices=A							,    metavar=A							,    type=A							,    help="""Learning rate scheduler"""							,    )
         parser.add_argument("""--weight_decay"""							,    default=0.0							,    type=A							,    help="""Weight decay if we apply some."""		)
         parser.add_argument("""--adam_epsilon"""							,    default=1E-8							,    type=A							,    help="""Epsilon for Adam optimizer."""		)
         parser.add_argument("""--warmup_steps"""							,    default=0							,    type=A							,    help="""Linear warmup over warmup_steps."""		)
         parser.add_argument("""--num_workers"""							,    default=4							,    type=A							,    help="""kwarg passed to DataLoader"""		)
         parser.add_argument("""--num_train_epochs"""							,    dest="""max_epochs"""							,    default=3							,    type=A		)
         parser.add_argument("""--train_batch_size"""							,    default=32							,    type=A		)
         parser.add_argument("""--eval_batch_size"""							,    default=32							,    type=A		)
         parser.add_argument("""--adafactor"""							,    action="""store_true"""		)







class    __snake_case	(							pl.Callback      ):





       '''simple docstring'''







       def 							UpperCAmelCase__			(  self			:   str							,    A			:   List[str]							,    A			:   int		):
         if (
             trainer.is_global_zero and trainer.global_rank == 0
         ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
           pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class    __snake_case	(							pl.Callback      ):





       '''simple docstring'''







       def 							UpperCAmelCase__			(  self			:   Any							,    A			:   int							,    A			:   Tuple		):
         # print(pl_module.model.rag)
         for name, param in pl_module.model.rag.named_parameters():
           if param.grad is None:
             print(A		)







class    __snake_case	(							pl.Callback      ):





       '''simple docstring'''







       def 							UpperCAmelCase__			(  self			:   int							,    A			:   Tuple							,    A			:   Dict		):
         __snake_case:						Optional[int]			  =						trainer.lr_schedulers[0]["""scheduler"""]
         __snake_case:						Dict			  =						{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
         pl_module.logger.log_metrics(A		)




       def 							UpperCAmelCase__			(  self			:   Dict							,    A			:   pl.Trainer							,    A			:   pl.LightningModule		):
         rank_zero_info("""***** Validation results *****"""		)
         __snake_case:						Tuple			  =						trainer.callback_metrics
         # Log results
         for key in sorted(A		):
           if key not in ["log", "progress_bar"]:
             rank_zero_info("""{} = {}\n""".format(A							,    str(metrics[key]		)		)		)






       def 							UpperCAmelCase__			(  self			:   Tuple							,    A			:   pl.Trainer							,    A			:   pl.LightningModule		):
         rank_zero_info("""***** Test results *****"""		)
         __snake_case:						Union[str, Any]			  =						trainer.callback_metrics
         # Log and save results to file
         __snake_case:						Optional[int]			  =						os.path.join(pl_module.hparams.output_dir							,    """test_results.txt"""		)
         with open(A							,    """w"""		) as writer:
           for key in sorted(A		):
             if key not in ["log", "progress_bar"]:
               rank_zero_info("""{} = {}\n""".format(A							,    str(metrics[key]		)		)		)
               writer.write("""{} = {}\n""".format(A							,    str(metrics[key]		)		)		)







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   None:
  #  To allow all pl args uncomment the following line
  #  parser = pl.Trainer.add_argparse_args(parser)
  parser.add_argument(
      """--output_dir""" , default=str(Path(SCREAMING_SNAKE_CASE__).parent / """test_run""" / """model_checkpoints""") , type=SCREAMING_SNAKE_CASE__ , help="""The output directory where the model predictions and checkpoints will be written.""" , )
  parser.add_argument(
      """--fp16""" , action="""store_true""" , help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""" , )

  parser.add_argument(
      """--fp16_opt_level""" , type=SCREAMING_SNAKE_CASE__ , default="""O2""" , help=(
          """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
          """See details at https://nvidia.github.io/apex/amp.html"""
      ) , )
  parser.add_argument("""--n_tpu_cores""" , dest="""tpu_cores""" , type=SCREAMING_SNAKE_CASE__)
  parser.add_argument("""--max_grad_norm""" , dest="""gradient_clip_val""" , default=1.0 , type=SCREAMING_SNAKE_CASE__ , help="""Max gradient norm""")
  parser.add_argument("""--do_train""" , action="""store_true""" , help="""Whether to run training.""")
  parser.add_argument("""--do_predict""" , action="""store_true""" , help="""Whether to run predictions on the test set.""")
  parser.add_argument(
      """--gradient_accumulation_steps""" , dest="""accumulate_grad_batches""" , type=SCREAMING_SNAKE_CASE__ , default=1 , help="""Number of updates steps to accumulate before performing a backward/update pass.""" , )
  parser.add_argument("""--seed""" , type=SCREAMING_SNAKE_CASE__ , default=42 , help="""random seed for initialization""")
  parser.add_argument(
      """--data_dir""" , default=str(Path(SCREAMING_SNAKE_CASE__).parent / """test_run""" / """dummy-train-data""") , type=SCREAMING_SNAKE_CASE__ , help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task.""" , )







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=[] , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ , )       ->   Union[str, Any]:
  pl.seed_everything(args.seed)

  # init model
  __snake_case:						List[str]			  =						Path(model.hparams.output_dir)
  odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__)

  # add custom checkpoints
  if checkpoint_callback is None:
    __snake_case:						Union[str, Any]			  =						pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir , prefix="""checkpoint""" , monitor="""val_loss""" , mode="""min""" , save_top_k=1)
  if early_stopping_callback:
    extra_callbacks.append(SCREAMING_SNAKE_CASE__)
  if logging_callback is None:
    __snake_case:						Union[str, Any]			  =						LoggingCallback()

  __snake_case:						Optional[int]			  =						{}

  if args.fpaa:
    __snake_case:						str			  =						16

  if args.gpus > 1:
    __snake_case:						Dict			  =						"""auto"""
    __snake_case:						int			  =						"""ddp"""

  __snake_case:						List[Any]			  =						args.accumulate_grad_batches
  __snake_case:						Union[str, Any]			  =						None
  __snake_case:						Optional[int]			  =						"""auto"""

  __snake_case:						Any			  =						pl.Trainer.from_argparse_args(
      SCREAMING_SNAKE_CASE__ , weights_summary=SCREAMING_SNAKE_CASE__ , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=SCREAMING_SNAKE_CASE__ , val_check_interval=1 , num_sanity_val_steps=2 , **SCREAMING_SNAKE_CASE__ , )

  if args.do_train:
    trainer.fit(SCREAMING_SNAKE_CASE__)

  else:
    print("""RAG modeling tests with new set functions successfuly executed!""")
  return trainer













from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline






