'''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_			:			Dict    	=   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_			:			Any    	=   sorted(arg_to_scheduler.keys())
lowerCAmelCase_			:			List[str]    	=   "{" + ", ".join(arg_to_scheduler_choices) + "}"





class 				lowercase    (    pl.LightningModule			):


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

         self.save_hyperparameters(__UpperCAmelCase)
         lowercase_     			=   0
         lowercase_     			=   Path(self.hparams.output_dir)
         lowercase_     			=   self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
               lowercase_     			=   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=__UpperCAmelCase ,		**__UpperCAmelCase ,		)
         else:
               lowercase_     			=   config

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

         if tokenizer is None:
               lowercase_     			=   AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,		cache_dir=__UpperCAmelCase ,		)
         else:
               lowercase_     			=   tokenizer
         lowercase_     			=   MODEL_MODES[mode]
         if model is None:
               lowercase_     			=   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=__UpperCAmelCase ,		)
         else:
               lowercase_     			=   model


   def __UpperCAmelCase							( self					:						int ,		*__lowerCAmelCase					:						Tuple ,		**__lowerCAmelCase					:						str)   ->       Optional[Any]:
         lowercase_     			=   self.model_type.from_pretrained(*__UpperCAmelCase ,		**__UpperCAmelCase)


   def __UpperCAmelCase							( self					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   arg_to_scheduler[self.hparams.lr_scheduler]
         lowercase_     			=   get_schedule_func(
             self.opt ,		num_warmup_steps=self.hparams.warmup_steps ,		num_training_steps=self.total_steps())
         lowercase_     			=   {"scheduler": scheduler, "interval": "step", "frequency": 1}
         return scheduler


   def __UpperCAmelCase							( self					:						Optional[int])   ->       List[Any]:
         lowercase_     			=   self.model
         lowercase_     			=   ["bias", "LayerNorm.weight"]
         lowercase_     			=   [
             {
                 "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:
               lowercase_     			=   Adafactor(
                   __UpperCAmelCase ,		lr=self.hparams.learning_rate ,		scale_parameter=__UpperCAmelCase ,		relative_step=__UpperCAmelCase)

         else:
               lowercase_     			=   AdamW(
                   __UpperCAmelCase ,		lr=self.hparams.learning_rate ,		eps=self.hparams.adam_epsilon)
         lowercase_     			=   optimizer

         lowercase_     			=   self.get_lr_scheduler()

         return [optimizer], [scheduler]


   def __UpperCAmelCase							( self					:						List[Any] ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						Tuple)   ->       Tuple:
         return self.validation_step(__UpperCAmelCase ,		__UpperCAmelCase)


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						Tuple)   ->       List[str]:
         return self.validation_end(__UpperCAmelCase)


   def __UpperCAmelCase							( self					:						int)   ->       int:
         lowercase_     			=   max(1 ,		self.hparams.gpus)  # TODO: consider num_tpu_cores
         lowercase_     			=   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					:						List[Any] ,		__lowerCAmelCase					:						Optional[Any])   ->       Dict:
         if mode == "test":
               lowercase_     			=   len(self.test_dataloader().dataset)
         else:
               lowercase_     			=   self.get_dataloader("train" ,		self.hparams.train_batch_size ,		shuffle=__UpperCAmelCase)
               lowercase_     			=   len(self.train_dataloader().dataset)


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						Tuple ,		__lowerCAmelCase					:						Optional[int] ,		__lowerCAmelCase					:						List[str] = False)   ->       Dict:
         raise NotImplementedError("You must implement this for your task")


   def __UpperCAmelCase							( self					:						List[str])   ->       Any:
         return self.train_loader


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


   def __UpperCAmelCase							( self					:						List[Any])   ->       Tuple:
         return self.get_dataloader("test" ,		self.hparams.eval_batch_size ,		shuffle=__UpperCAmelCase)


   def __UpperCAmelCase							( self					:						Optional[Any] ,		__lowerCAmelCase					:						int)   ->       Tuple:
         return os.path.join(
             self.hparams.data_dir ,		"cached_{}_{}_{}".format(
                 __UpperCAmelCase ,		list(filter(__UpperCAmelCase ,		self.hparams.model_name_or_path.split("/"))).pop() ,		str(self.hparams.max_seq_length) ,		) ,		)


   @pl.utilities.rank_zero_only
   def __UpperCAmelCase							( self					:						str ,		__lowerCAmelCase					:						Tuple)   ->       None:
         lowercase_     			=   self.output_dir.joinpath("best_tfmr")
         lowercase_     			=   self.step_count
         self.model.save_pretrained(__UpperCAmelCase)
         self.tokenizer.save_pretrained(__UpperCAmelCase)


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





class 				lowercase    (    pl.Callback			):


   def __UpperCAmelCase							( self					:						str ,		__lowerCAmelCase					:						List[str] ,		__lowerCAmelCase					:						List[str])   ->       Dict:
         lowercase_     			=   trainer.lr_schedulers[0]["scheduler"]
         lowercase_     			=   {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr())}
         pl_module.logger.log_metrics(__UpperCAmelCase)


   def __UpperCAmelCase							( self					:						str ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						str)   ->       int:
         rank_zero_info("***** Validation results *****")
         lowercase_     			=   trainer.callback_metrics
         # Log results
         for key in sorted(__UpperCAmelCase):
               if key not in ["log", "progress_bar"]:
                     rank_zero_info("{} = {}\n".format(__UpperCAmelCase ,		str(metrics[key])))


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




def __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    List[str]					)						->				Any:
      '''simple docstring'''


      parser.add_argument(
          "--output_dir"			,		default=_lowerCAmelCase			,		type=_lowerCAmelCase			,		required=_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=_lowerCAmelCase			,		type=_lowerCAmelCase			,		required=_lowerCAmelCase			,		help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."			,		)






def __a			(						__lowerCamelCase      :    BaseTransformer			,		__lowerCamelCase      :    argparse.Namespace			,		__lowerCamelCase      :    List[Any]=None			,		__lowerCamelCase      :    str=True			,		__lowerCamelCase      :    Union[str, Any]=[]			,		__lowerCamelCase      :    Optional[int]=None			,		__lowerCamelCase      :    int=None			,		**__lowerCamelCase      :    Any			,		)						->				Optional[int]:
      '''simple docstring'''


      pl.seed_everything(args.seed					)

      # init model
      lowercase_     			=   Path(model.hparams.output_dir					)
      odir.mkdir(exist_ok=_lowerCAmelCase					)

      # add custom checkpoints
      if checkpoint_callback is None:
            lowercase_     			=   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_     			=   LoggingCallback()

      lowercase_     			=   {}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            lowercase_     			=   16
            lowercase_     			=   args.fpaa_opt_level

      if args.gpus > 1:
            lowercase_     			=   "ddp"

      lowercase_     			=   args.accumulate_grad_batches
      lowercase_     			=   extra_train_kwargs.get("accelerator"			,		_lowerCAmelCase					)
      lowercase_     			=   extra_train_kwargs.get("profiler"			,		_lowerCAmelCase					)

      lowercase_     			=   pl.Trainer.from_argparse_args(
          _lowerCAmelCase			,		weights_summary=_lowerCAmelCase			,		callbacks=[logging_callback] + extra_callbacks			,		logger=_lowerCAmelCase			,		checkpoint_callback=_lowerCAmelCase			,		**_lowerCAmelCase			,		)

      if args.do_train:
            trainer.fit(_lowerCAmelCase					)

      return trainer










"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       list[list[int]]							):






     """simple docstring"""

     _a     						=					[list(_lowerCAmelCase							) for row in matrix]
     if matrix and isinstance(_lowerCAmelCase,  _lowerCAmelCase							):
          if isinstance(matrix[0],  _lowerCAmelCase							):
               _a     						=					0
               for row in matrix:
                    if prev_len == 0:
                         _a     						=					len(_lowerCAmelCase							)
                         _a     						=					True
                    else:
                         _a     						=					prev_len == len(_lowerCAmelCase							)
          else:
               _a     						=					True
     else:
          _a     						=					False

     return result
def    A_ ( _lowerCAmelCase  :       list[list[int]]							):






     """simple docstring"""

     if check_matrix(_lowerCAmelCase							) and len(_lowerCAmelCase							) > 0:
          _a     						=					[list(_lowerCAmelCase							) for row in a]
          _a     						=					len(_lowerCAmelCase							)
          if isinstance(a[0],  _lowerCAmelCase							):
               _a     						=					len(a[0]							)
          else:
               for dat in a:
                    print(_lowerCAmelCase							)
               return

          # horizotal printing increasing
          for i in range(0,  _lowerCAmelCase							):
               print(a[0][i]							)
          # vertical printing down
          for i in range(1,  _lowerCAmelCase							):
               print(a[i][mat_col - 1]							)
          # horizotal printing decreasing
          if mat_row > 1:
               for i in range(mat_col - 2,  -1,  -1							):
                    print(a[mat_row - 1][i]							)
        # vertical printing up
          for i in range(mat_row - 2,  0,  -1							):
               print(a[i][0]							)
          _a     						=					[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
          if len(_lowerCAmelCase							) > 0:
               spiral_print_clockwise(_lowerCAmelCase							)
          else:
               return
     else:
          print('''Not a valid matrix'''							)
          return
def    A_ ( _lowerCAmelCase  :       list[list]							):






     """simple docstring"""

     if matrix:
          return list(matrix.pop(0							)							) + spiral_traversal(list(zip(*_lowerCAmelCase							)							)[::-1]							)
     else:
          return []


# driver code
if __name__ == "__main__":
   import doctest

   doctest.testmod()

   __snake_case												=							[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
   spiral_print_clockwise(a)