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							=       logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

UpperCAmelCase							=       {
    "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							=       {
    "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							=       sorted(arg_to_scheduler.keys())
UpperCAmelCase							=       "{" + ", ".join(arg_to_scheduler_choices) + "}"



class 					snake_case__       (				pl.LightningModule							):



   def __init__(				self   :   str			,	A__   :   argparse.Namespace			,	A__   :   Tuple=None			,	A__   :   Dict="base"			,	A__   :   List[Any]=None			,	A__   :   Optional[Any]=None			,	A__   :   List[Any]=None			,	**A__   :   Dict			,	)							->     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__   )
         snake_case_     :			List[str]       =       0
         snake_case_     :			Optional[int]       =       Path(self.hparams.output_dir   )
         snake_case_     :			Optional[int]       =       self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
               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:
               snake_case_     :			PretrainedConfig       =       config

         snake_case_     :			Union[str, 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_     :			Any       =       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   :   int			,	*A__   :   Optional[Any]			,	**A__   :   Any   )							->     List[Any]:




         '''simple docstring'''




         snake_case_     :			Any       =       self.model_type.from_pretrained(*A__			,	**A__   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]   )							->     Optional[Any]:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   int   )							->     List[Any]:




         '''simple docstring'''




         snake_case_     :			Tuple       =       self.model
         snake_case_     :			Optional[Any]       =       ["bias", "LayerNorm.weight"]
         snake_case_     :			Union[str, 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:
               snake_case_     :			List[str]       =       Adafactor(
                   A__			,	lr=self.hparams.learning_rate			,	scale_parameter=A__			,	relative_step=A__   )

         else:
               snake_case_     :			Optional[int]       =       AdamW(
                   A__			,	lr=self.hparams.learning_rate			,	eps=self.hparams.adam_epsilon   )
         snake_case_     :			int       =       optimizer

         snake_case_     :			Union[str, Any]       =       self.get_lr_scheduler()

         return [optimizer], [scheduler]



   def 					UpperCAmelCase__       (				self   :   Any			,	A__   :   str			,	A__   :   List[Any]   )							->     Dict:




         '''simple docstring'''




         return self.validation_step(A__			,	A__   )



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   List[str]   )							->     Tuple:




         '''simple docstring'''




         return self.validation_end(A__   )



   def 					UpperCAmelCase__       (				self   :   str   )							->     int:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       max(1			,	self.hparams.gpus   )  # TODO: consider num_tpu_cores
         snake_case_     :			List[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__   :   Optional[Any]   )							->     List[Any]:




         '''simple docstring'''




         if mode == "test":
               snake_case_     :			Dict       =       len(self.test_dataloader().dataset   )
         else:
               snake_case_     :			List[str]       =       self.get_dataloader("train"			,	self.hparams.train_batch_size			,	shuffle=A__   )
               snake_case_     :			Any       =       len(self.train_dataloader().dataset   )



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   str			,	A__   :   int			,	A__   :   bool = False   )							->     Union[str, Any]:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   Optional[Any]   )							->     Optional[int]:




         '''simple docstring'''




         return self.train_loader



   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[Any]:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   Optional[int]   )							->     str:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   str			,	A__   :   Dict   )							->     Any:




         '''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   :   Optional[Any]			,	A__   :   Dict[str, Any]   )							->     None:




         '''simple docstring'''




         snake_case_     :			Optional[Any]       =       self.output_dir.joinpath("best_tfmr"   )
         snake_case_     :			Tuple       =       self.step_count
         self.model.save_pretrained(A__   )
         self.tokenizer.save_pretrained(A__   )



   @staticmethod
   def 					UpperCAmelCase__       (				A__   :   Optional[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 					snake_case__       (				pl.Callback							):



   def 					UpperCAmelCase__       (				self   :   int			,	A__   :   List[Any]			,	A__   :   List[str]   )							->     int:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   List[Any]			,	A__   :   pl.Trainer			,	A__   :   pl.LightningModule   )							->     List[Any]:




         '''simple docstring'''




         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   :   Union[str, Any]			,	A__   :   pl.Trainer			,	A__   :   pl.LightningModule   )							->     Tuple:




         '''simple docstring'''




         rank_zero_info("***** Test results *****"   )
         snake_case_     :			int       =       trainer.callback_metrics
         # Log and save results to file
         snake_case_     :			str       =       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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Any ,	lowerCAmelCase_: Union[str, Any]     ):
      #  To allow all pl args uncomment the following line
      #  parser = pl.Trainer.add_argparse_args(parser)
      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=4_2 ,	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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: BaseTransformer ,	lowerCAmelCase_: argparse.Namespace ,	lowerCAmelCase_: Dict=None ,	lowerCAmelCase_: Dict=True ,	lowerCAmelCase_: List[Any]=[] ,	lowerCAmelCase_: Union[str, Any]=None ,	lowerCAmelCase_: str=None ,	**lowerCAmelCase_: Optional[int] ,	):
      pl.seed_everything(args.seed     )

      # init model
      snake_case_     :			str       =       Path(model.hparams.output_dir     )
      odir.mkdir(exist_ok=lowerCAmelCase_     )

      # add custom checkpoints
      if checkpoint_callback is None:
            snake_case_     :			Optional[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(lowerCAmelCase_     )
      if logging_callback is None:
            snake_case_     :			int       =       LoggingCallback()

      snake_case_     :			Optional[int]       =       {}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            snake_case_     :			int       =       1_6
            snake_case_     :			int       =       args.fpaa_opt_level

      if args.gpus > 1:
            snake_case_     :			List[Any]       =       "ddp"

      snake_case_     :			str       =       args.accumulate_grad_batches
      snake_case_     :			Optional[int]       =       extra_train_kwargs.get("accelerator" ,	lowerCAmelCase_     )
      snake_case_     :			str       =       extra_train_kwargs.get("profiler" ,	lowerCAmelCase_     )

      snake_case_     :			Any       =       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
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: bool = False     ):
      if device_specific:
            seed += AcceleratorState().process_index
      random.seed(lowerCAmelCase_     )
      np.random.seed(lowerCAmelCase_     )
      torch.manual_seed(lowerCAmelCase_     )
      if is_xpu_available():
            torch.xpu.manual_seed_all(lowerCAmelCase_     )
      elif is_npu_available():
            torch.npu.manual_seed_all(lowerCAmelCase_     )
      else:
            torch.cuda.manual_seed_all(lowerCAmelCase_     )
      # ^^ safe to call this function even if cuda is not available
      if is_tpu_available():
            xm.set_rng_state(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[RNGType] = None ,	lowerCAmelCase_: Optional[torch.Generator] = None     ):
      # Get the proper rng state
      if rng_type == RNGType.TORCH:
            snake_case_     :			List[Any]       =       torch.get_rng_state()
      elif rng_type == RNGType.CUDA:
            snake_case_     :			Dict       =       torch.cuda.get_rng_state()
      elif rng_type == RNGType.XLA:
            assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
            snake_case_     :			Tuple       =       torch.tensor(xm.get_rng_state()     )
      elif rng_type == RNGType.NPU:
            assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
            snake_case_     :			List[str]       =       torch.npu.get_rng_state()
      elif rng_type == RNGType.XPU:
            assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
            snake_case_     :			List[str]       =       torch.xpu.get_rng_state()
      elif rng_type == RNGType.GENERATOR:
            assert generator is not None, "Need a generator to synchronize its seed."
            snake_case_     :			Optional[Any]       =       generator.get_state()

      # Broadcast the rng state from device 0 to other devices
      snake_case_     :			List[Any]       =       AcceleratorState()
      if state.distributed_type == DistributedType.TPU:
            snake_case_     :			int       =       rng_state.to(xm.xla_device()     )
            xm.collective_broadcast([rng_state]     )
            xm.mark_step()
            snake_case_     :			List[str]       =       rng_state.cpu()
      elif (
          state.distributed_type in CUDA_DISTRIBUTED_TYPES
          or state.distributed_type == DistributedType.MULTI_NPU
          or state.distributed_type == DistributedType.MULTI_XPU
      ):
            snake_case_     :			Dict       =       rng_state.to(state.device     )
            torch.distributed.broadcast(lowerCAmelCase_ ,	0     )
            snake_case_     :			Dict       =       rng_state.cpu()
      elif state.distributed_type == DistributedType.MULTI_CPU:
            torch.distributed.broadcast(lowerCAmelCase_ ,	0     )

      # Set the broadcast rng state
      if rng_type == RNGType.TORCH:
            torch.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.CUDA:
            torch.cuda.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.NPU:
            torch.npu.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.XPU:
            torch.xpu.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.XLA:
            xm.set_rng_state(rng_state.item()     )
      elif rng_type == RNGType.GENERATOR:
            generator.set_state(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Union[str, RNGType]] ,	lowerCAmelCase_: Optional[torch.Generator] = None     ):
      for rng_type in rng_types:
            synchronize_rng_state(RNGType(lowerCAmelCase_     ) ,	generator=lowerCAmelCase_     )



