

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


a_         =      logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

a_         =      {
    '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
a_         =      {
    '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
}
a_         =      sorted(arg_to_scheduler.keys())
a_         =      '{' + ', '.join(arg_to_scheduler_choices) + '}'






class _UpperCamelCase							(							pl.LightningModule				):
   '''simple docstring'''







   def __init__(     self							:	Optional[int] , a							:	argparse.Namespace , a							:	Dict=None , a							:	List[Any]="base" , a							:	Optional[int]=None , a							:	Any=None , a							:	Tuple=None , **a							:	Union[str, Any] , )							->    List[str]:

    """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(a		)
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    0
    SCREAMING_SNAKE_CASE				: List[str]			       =    Path(self.hparams.output_dir		)
    SCREAMING_SNAKE_CASE				: str			       =    self.hparams.cache_dir if self.hparams.cache_dir else None
    if config is None:
     SCREAMING_SNAKE_CASE				: 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:
     SCREAMING_SNAKE_CASE				: PretrainedConfig			       =    config

    SCREAMING_SNAKE_CASE				: int			       =    ("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:
     SCREAMING_SNAKE_CASE				: str			       =    AutoTokenizer.from_pretrained(
         self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=a , )
    else:
     SCREAMING_SNAKE_CASE				: PreTrainedTokenizer			       =    tokenizer
    SCREAMING_SNAKE_CASE				: Dict			       =    MODEL_MODES[mode]
    if model is None:
     SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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:
     SCREAMING_SNAKE_CASE				: str			       =    model
   def 							__UpperCamelCase	(     self							:	str , *a							:	Dict , **a							:	str		)							->    List[str]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Tuple			       =    self.model_type.from_pretrained(*a , **a		)
   def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    int:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: str			       =    arg_to_scheduler[self.hparams.lr_scheduler]
    SCREAMING_SNAKE_CASE				: List[str]			       =    get_schedule_func(
        self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps()		)
    SCREAMING_SNAKE_CASE				: int			       =    {"scheduler": scheduler, "interval": "step", "frequency": 1}
    return scheduler
   def 							__UpperCamelCase	(     self							:	Tuple		)							->    Dict:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: int			       =    self.model
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    ["bias", "LayerNorm.weight"]
    SCREAMING_SNAKE_CASE				: List[Any]			       =    [
        {
            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)],
            "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:
     SCREAMING_SNAKE_CASE				: int			       =    Adafactor(
         a , lr=self.hparams.learning_rate , scale_parameter=a , relative_step=a		)

    else:
     SCREAMING_SNAKE_CASE				: Dict			       =    AdamW(
         a , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon		)
    SCREAMING_SNAKE_CASE				: Dict			       =    optimizer

    SCREAMING_SNAKE_CASE				: str			       =    self.get_lr_scheduler()

    return [optimizer], [scheduler]
   def 							__UpperCamelCase	(     self							:	List[str] , a							:	Any , a							:	List[str]		)							->    List[str]:

    """simple docstring"""

    return self.validation_step(a , a		)
   def 							__UpperCamelCase	(     self							:	Dict , a							:	int		)							->    Tuple:

    """simple docstring"""

    return self.validation_end(a		)
   def 							__UpperCamelCase	(     self							:	Tuple		)							->    int:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    max(1 , self.hparams.gpus		)  # TODO: consider num_tpu_cores
    SCREAMING_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							:	Union[str, Any] , a							:	Dict		)							->    Optional[Any]:

    """simple docstring"""

    if mode == "test":
     SCREAMING_SNAKE_CASE				: List[Any]			       =    len(self.test_dataloader().dataset		)
    else:
     SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.get_dataloader("train" , self.hparams.train_batch_size , shuffle=a		)
     SCREAMING_SNAKE_CASE				: Tuple			       =    len(self.train_dataloader().dataset		)
   def 							__UpperCamelCase	(     self							:	List[str] , a							:	str , a							:	int , a							:	bool = False		)							->    List[str]:

    """simple docstring"""

    raise NotImplementedError("You must implement this for your task"		)
   def 							__UpperCamelCase	(     self							:	Dict		)							->    List[Any]:

    """simple docstring"""

    return self.train_loader
   def 							__UpperCamelCase	(     self							:	int		)							->    Any:

    """simple docstring"""

    return self.get_dataloader("dev" , self.hparams.eval_batch_size , shuffle=a		)
   def 							__UpperCamelCase	(     self							:	Any		)							->    Optional[Any]:

    """simple docstring"""

    return self.get_dataloader("test" , self.hparams.eval_batch_size , shuffle=a		)
   def 							__UpperCamelCase	(     self							:	List[Any] , a							:	Union[str, Any]		)							->    str:

    """simple docstring"""

    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							:	Union[str, Any] , a							:	Dict[str, Any]		)							->    None:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: List[str]			       =    self.output_dir.joinpath("best_tfmr"		)
    SCREAMING_SNAKE_CASE				: int			       =    self.step_count
    self.model.save_pretrained(a		)
    self.tokenizer.save_pretrained(a		)






   @staticmethod
   def 							__UpperCamelCase	(     a							:	int , a							:	List[Any]		)							->    Optional[Any]:

    """simple docstring"""

    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="" , 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 _UpperCamelCase							(							pl.Callback				):
   '''simple docstring'''







   def 							__UpperCamelCase	(     self							:	Dict , a							:	List[str] , a							:	Union[str, Any]		)							->    List[Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Tuple			       =    trainer.lr_schedulers[0]["scheduler"]
    SCREAMING_SNAKE_CASE				: List[Any]			       =    {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
    pl_module.logger.log_metrics(a		)
   def 							__UpperCamelCase	(     self							:	Any , a							:	pl.Trainer , a							:	pl.LightningModule		)							->    str:

    """simple docstring"""

    rank_zero_info("***** Validation results *****"		)
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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							:	int , a							:	pl.Trainer , a							:	pl.LightningModule		)							->    Tuple:

    """simple docstring"""

    rank_zero_info("***** Test results *****"		)
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    trainer.callback_metrics
    # Log and save results to file
    SCREAMING_SNAKE_CASE				: 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 	lowerCamelCase__	(   _a							,     _a):
 #  To allow all pl args uncomment the following line
 #  parser = pl.Trainer.add_argparse_args(parser)
 parser.add_argument(
     "--output_dir"							,     default=_a							,     type=_a							,     required=_a							,     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=_a							,     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=_a)
 parser.add_argument("--max_grad_norm"							,     dest="gradient_clip_val"							,     default=1.0							,     type=_a							,     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=_a							,     default=1							,     help="Number of updates steps to accumulate before performing a backward/update pass."							,     )
 parser.add_argument("--seed"							,     type=_a							,     default=42							,     help="random seed for initialization")
 parser.add_argument(
     "--data_dir"							,     default=_a							,     type=_a							,     required=_a							,     help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."							,     )


def 	lowerCamelCase__	(   _a							,     _a							,     _a=None							,     _a=True							,     _a=[]							,     _a=None							,     _a=None							,     **_a							,     ):
 pl.seed_everything(args.seed)

 # init model
 SCREAMING_SNAKE_CASE				: Any			       =    Path(model.hparams.output_dir)
 odir.mkdir(exist_ok=_a)

 # add custom checkpoints
 if checkpoint_callback is None:
  SCREAMING_SNAKE_CASE				: List[str]			       =    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(_a)
 if logging_callback is None:
  SCREAMING_SNAKE_CASE				: str			       =    LoggingCallback()

 SCREAMING_SNAKE_CASE				: Any			       =    {}

 # TODO: remove with PyTorch 1.6 since pl uses native amp
 if args.fpaa:
  SCREAMING_SNAKE_CASE				: List[str]			       =    16
  SCREAMING_SNAKE_CASE				: Optional[Any]			       =    args.fpaa_opt_level

 if args.gpus > 1:
  SCREAMING_SNAKE_CASE				: Optional[Any]			       =    "ddp"

 SCREAMING_SNAKE_CASE				: List[str]			       =    args.accumulate_grad_batches
 SCREAMING_SNAKE_CASE				: Dict			       =    extra_train_kwargs.get("accelerator"							,     _a)
 SCREAMING_SNAKE_CASE				: List[str]			       =    extra_train_kwargs.get("profiler"							,     _a)

 SCREAMING_SNAKE_CASE				: List[Any]			       =    pl.Trainer.from_argparse_args(
     _a							,     weights_summary=_a							,     callbacks=[logging_callback] + extra_callbacks							,     logger=_a							,     checkpoint_callback=_a							,     **_a							,     )

 if args.do_train:
  trainer.fit(_a)

 return trainer


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_         =      {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

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