





'''simple docstring'''


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


lowerCAmelCase :					Optional[int] 							=logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowerCAmelCase :					Union[str, 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
lowerCAmelCase :					Optional[Any] 							={
    '''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
}
lowerCAmelCase :					int 							=sorted(arg_to_scheduler.keys())
lowerCAmelCase :					Tuple 							='''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
class 				a_       (    pl.LightningModule							):





     def __init__(			self		: str     ,				lowercase		: argparse.Namespace     ,				lowercase		: Optional[Any]=None     ,				lowercase		: str="base"     ,				lowercase		: Any=None     ,				lowercase		: Tuple=None     ,				lowercase		: Tuple=None     ,				**lowercase		: Union[str, Any]     ,				):





        """simple docstring"""




        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(lowercase						)
        lowercase_      :Tuple         =			0
        lowercase_      :Any         =			Path(self.hparams.output_dir						)
        lowercase_      :int         =			self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
           lowercase_      :List[str]         =			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=lowercase     ,				**lowercase     ,				)
        else:
           lowercase_      :PretrainedConfig         =			config

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

        if tokenizer is None:
           lowercase_      :List[str]         =			AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,				cache_dir=lowercase     ,				)
        else:
           lowercase_      :PreTrainedTokenizer         =			tokenizer
        lowercase_      :Dict         =			MODEL_MODES[mode]
        if model is None:
           lowercase_      :Tuple         =			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=lowercase     ,				)
        else:
           lowercase_      :int         =			model





     def 							lowercase__				(			self		: List[str]     ,				*lowercase		: Union[str, Any]     ,				**lowercase		: Tuple						):





        """simple docstring"""




        lowercase_      :List[Any]         =			self.model_type.from_pretrained(*lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :str         =			arg_to_scheduler[self.hparams.lr_scheduler]
        lowercase_      :Tuple         =			get_schedule_func(
            self.opt     ,				num_warmup_steps=self.hparams.warmup_steps     ,				num_training_steps=self.total_steps()						)
        lowercase_      :str         =			{"scheduler": scheduler, "interval": "step", "frequency": 1}
        return scheduler





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :int         =			self.model
        lowercase_      :List[str]         =			["bias", "LayerNorm.weight"]
        lowercase_      :Dict         =			[
            {
                "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:
           lowercase_      :List[str]         =			Adafactor(
               lowercase     ,				lr=self.hparams.learning_rate     ,				scale_parameter=lowercase     ,				relative_step=lowercase						)

        else:
           lowercase_      :List[Any]         =			AdamW(
               lowercase     ,				lr=self.hparams.learning_rate     ,				eps=self.hparams.adam_epsilon						)
        lowercase_      :int         =			optimizer

        lowercase_      :Optional[Any]         =			self.get_lr_scheduler()

        return [optimizer], [scheduler]





     def 							lowercase__				(			self		: str     ,				lowercase		: Any     ,				lowercase		: str						):





        """simple docstring"""




        return self.validation_step(lowercase     ,				lowercase						)





     def 							lowercase__				(			self		: Dict     ,				lowercase		: str						):





        """simple docstring"""




        return self.validation_end(lowercase						)





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        lowercase_      :Dict         =			max(1     ,				self.hparams.gpus						)  # TODO: consider num_tpu_cores
        lowercase_      :Tuple         =			self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





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





        """simple docstring"""




        if stage == "test":
           lowercase_      :Tuple         =			len(self.test_dataloader().dataset						)
        else:
           lowercase_      :Optional[int]         =			self.get_dataloader("train"     ,				self.hparams.train_batch_size     ,				shuffle=lowercase						)
           lowercase_      :Tuple         =			len(self.train_dataloader().dataset						)





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: str     ,				lowercase		: int     ,				lowercase		: bool = False						):





        """simple docstring"""




        raise NotImplementedError("You must implement this for your task"						)





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        return self.train_loader





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        return self.get_dataloader("dev"     ,				self.hparams.eval_batch_size     ,				shuffle=lowercase						)





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Dict     ,				lowercase		: str						):





        """simple docstring"""




        return os.path.join(
            self.hparams.data_dir     ,				"cached_{}_{}_{}".format(
                lowercase     ,				list(filter(lowercase     ,				self.hparams.model_name_or_path.split("/"						)						)						).pop()     ,				str(self.hparams.max_seq_length						)     ,				)     ,				)





     @pl.utilities.rank_zero_only
     def 							lowercase__				(			self		: str     ,				lowercase		: Dict[str, Any]						):





        """simple docstring"""




        lowercase_      :str         =			self.output_dir.joinpath("best_tfmr"						)
        lowercase_      :Optional[int]         =			self.step_count
        self.model.save_pretrained(lowercase						)
        self.tokenizer.save_pretrained(lowercase						)







     @staticmethod
     def 							lowercase__				(			lowercase		: Dict     ,				lowercase		: Any						):





        """simple docstring"""




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







class 				a_       (    pl.Callback							):





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





        """simple docstring"""




        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 				a_       (    pl.Callback							):





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





        """simple docstring"""




        for name, param in pl_module.model.rag.named_parameters():
           if param.grad is None:
              print(lowercase						)







class 				a_       (    pl.Callback							):





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





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			trainer.lr_schedulers[0]["scheduler"]
        lowercase_      :Optional[Any]         =			{F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
        pl_module.logger.log_metrics(lowercase						)





     def 							lowercase__				(			self		: int     ,				lowercase		: pl.Trainer     ,				lowercase		: pl.LightningModule						):





        """simple docstring"""




        rank_zero_info("***** Validation results *****"						)
        lowercase_      :Union[str, Any]         =			trainer.callback_metrics
        # Log results
        for key in sorted(lowercase						):
           if key not in ["log", "progress_bar"]:
              rank_zero_info("{} = {}\n".format(lowercase     ,				str(metrics[key]						)						)						)







     def 							lowercase__				(			self		: Optional[Any]     ,				lowercase		: pl.Trainer     ,				lowercase		: pl.LightningModule						):





        """simple docstring"""




        rank_zero_info("***** Test results *****"						)
        lowercase_      :Tuple         =			trainer.callback_metrics
        # Log and save results to file
        lowercase_      :Dict         =			os.path.join(pl_module.hparams.output_dir     ,				"test_results.txt"						)
        with open(lowercase     ,				"w"						) as writer:
           for key in sorted(lowercase						):
              if key not in ["log", "progress_bar"]:
                 rank_zero_info("{} = {}\n".format(lowercase     ,				str(metrics[key]						)						)						)
                 writer.write("{} = {}\n".format(lowercase     ,				str(metrics[key]						)						)						)







def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Dict			):
   #  To allow all pl args uncomment the following line
   #  parser = pl.Trainer.add_argparse_args(parser)
   parser.add_argument(
       "--output_dir"     ,default=str(Path(__lowerCamelCase			).parent / "test_run" / "model_checkpoints"			)     ,type=__lowerCamelCase     ,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=__lowerCamelCase     ,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=__lowerCamelCase			)
   parser.add_argument("--max_grad_norm"     ,dest="gradient_clip_val"     ,default=1.0     ,type=__lowerCamelCase     ,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=__lowerCamelCase     ,default=1     ,help="Number of updates steps to accumulate before performing a backward/update pass."     ,)
   parser.add_argument("--seed"     ,type=__lowerCamelCase     ,default=42     ,help="random seed for initialization"			)
   parser.add_argument(
       "--data_dir"     ,default=str(Path(__lowerCamelCase			).parent / "test_run" / "dummy-train-data"			)     ,type=__lowerCamelCase     ,help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."     ,)


def 							UpperCAmelCase_  (						__lowerCamelCase : BaseTransformer     ,__lowerCamelCase : argparse.Namespace     ,__lowerCamelCase : Union[str, Any]=None     ,__lowerCamelCase : str=True     ,__lowerCamelCase : str=[]     ,__lowerCamelCase : str=None     ,__lowerCamelCase : str=None     ,**__lowerCamelCase : Optional[Any]     ,):
   pl.seed_everything(args.seed			)

   # init model
   lowercase_      :Dict         =			Path(model.hparams.output_dir			)
   odir.mkdir(exist_ok=__lowerCamelCase			)

   # add custom checkpoints
   if checkpoint_callback is None:
      lowercase_      :int         =			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(__lowerCamelCase			)
   if logging_callback is None:
      lowercase_      :Optional[int]         =			LoggingCallback()

   lowercase_      :Optional[Any]         =			{}

   if args.fpaa:
      lowercase_      :List[str]         =			16

   if args.gpus > 1:
      lowercase_      :List[Any]         =			"auto"
      lowercase_      :Dict         =			"ddp"

   lowercase_      :Optional[Any]         =			args.accumulate_grad_batches
   lowercase_      :List[Any]         =			None
   lowercase_      :Any         =			"auto"

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

   if args.do_train:
      trainer.fit(__lowerCamelCase			)

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









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase :					Tuple 							=logging.get_logger(__name__)

lowerCAmelCase :					str 							={
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}
class 				a_       (    _lowerCAmelCase							):
     __A								=      "cvt"





     def __init__(			self		: Tuple     ,				lowercase		: str=3     ,				lowercase		: str=[7, 3, 3]     ,				lowercase		: List[str]=[4, 2, 2]     ,				lowercase		: Dict=[2, 1, 1]     ,				lowercase		: int=[64, 192, 384]     ,				lowercase		: Dict=[1, 3, 6]     ,				lowercase		: Dict=[1, 2, 10]     ,				lowercase		: Any=[4.0, 4.0, 4.0]     ,				lowercase		: Tuple=[0.0, 0.0, 0.0]     ,				lowercase		: List[str]=[0.0, 0.0, 0.0]     ,				lowercase		: List[str]=[0.0, 0.0, 0.1]     ,				lowercase		: Any=[True, True, True]     ,				lowercase		: Any=[False, False, True]     ,				lowercase		: Optional[Any]=["dw_bn", "dw_bn", "dw_bn"]     ,				lowercase		: int=[3, 3, 3]     ,				lowercase		: str=[1, 1, 1]     ,				lowercase		: List[Any]=[2, 2, 2]     ,				lowercase		: Tuple=[1, 1, 1]     ,				lowercase		: Optional[Any]=[1, 1, 1]     ,				lowercase		: str=0.02     ,				lowercase		: str=1e-1_2     ,				**lowercase		: str     ,				):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_      :List[Any]         =			num_channels
        lowercase_      :Union[str, Any]         =			patch_sizes
        lowercase_      :Tuple         =			patch_stride
        lowercase_      :List[Any]         =			patch_padding
        lowercase_      :List[Any]         =			embed_dim
        lowercase_      :Union[str, Any]         =			num_heads
        lowercase_      :Any         =			depth
        lowercase_      :str         =			mlp_ratio
        lowercase_      :List[str]         =			attention_drop_rate
        lowercase_      :List[Any]         =			drop_rate
        lowercase_      :Union[str, Any]         =			drop_path_rate
        lowercase_      :Any         =			qkv_bias
        lowercase_      :Dict         =			cls_token
        lowercase_      :int         =			qkv_projection_method
        lowercase_      :Union[str, Any]         =			kernel_qkv
        lowercase_      :Optional[Any]         =			padding_kv
        lowercase_      :Optional[Any]         =			stride_kv
        lowercase_      :Dict         =			padding_q
        lowercase_      :Any         =			stride_q
        lowercase_      :Dict         =			initializer_range
        lowercase_      :Optional[Any]         =			layer_norm_eps


