





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


lowercase     :			List[Any]     						=	logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

lowercase     :			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
lowercase     :			str     						=	{
    """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
}
lowercase     :			Optional[int]     						=	sorted(arg_to_scheduler.keys())
lowercase     :			int     						=	"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class 			__A(    pl.LightningModule			):

     def __init__(   self,							A,							A=None,							A="base",							A=None,							A=None,							A=None,							**A,							):





            """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   )
            _UpperCamelCase														=    0
            _UpperCamelCase														=    Path(self.hparams.output_dir   )
            _UpperCamelCase														=    self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                   _UpperCamelCase														=    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:
                   _UpperCamelCase														=    config

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

     def 						_UpperCamelCase  (   self,							*A,							**A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model_type.from_pretrained(*A,							**A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model
            _UpperCamelCase														=    ['''bias''', '''LayerNorm.weight''']
            _UpperCamelCase														=    [
                {
                    '''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:
                   _UpperCamelCase														=    Adafactor(
                       A,							lr=self.hparams.learning_rate,							scale_parameter=A,							relative_step=A   )

            else:
                   _UpperCamelCase														=    AdamW(
                       A,							lr=self.hparams.learning_rate,							eps=self.hparams.adam_epsilon   )
            _UpperCamelCase														=    optimizer

            _UpperCamelCase														=    self.get_lr_scheduler()

            return [optimizer], [scheduler]

     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            return self.validation_step(A,							A   )

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            return self.validation_end(A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    max(1,							self.hparams.gpus   )  # TODO: consider num_tpu_cores
            _UpperCamelCase														=    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,							A   ):





            """simple docstring"""

            if mode == "test":
                   _UpperCamelCase														=    len(self.test_dataloader().dataset   )
            else:
                   _UpperCamelCase														=    self.get_dataloader('''train''',							self.hparams.train_batch_size,							shuffle=A   )
                   _UpperCamelCase														=    len(self.train_dataloader().dataset   )

     def 						_UpperCamelCase  (   self,							A,							A,							A = False   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self.train_loader

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self.get_dataloader('''dev''',							self.hparams.eval_batch_size,							shuffle=A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self.get_dataloader('''test''',							self.hparams.eval_batch_size,							shuffle=A   )

     def 						_UpperCamelCase  (   self,							A   ):





            """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,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.output_dir.joinpath('''best_tfmr'''   )
            _UpperCamelCase														=    self.step_count
            self.model.save_pretrained(A   )
            self.tokenizer.save_pretrained(A   )



     @staticmethod
     def 						_UpperCamelCase  (   A,							A   ):





            """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 			__A(    pl.Callback			):

     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    trainer.lr_schedulers[0]['''scheduler''']
            _UpperCamelCase														=    {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
            pl_module.logger.log_metrics(A   )

     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            rank_zero_info('''***** Validation results *****'''   )
            _UpperCamelCase														=    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,							A,							A   ):





            """simple docstring"""

            rank_zero_info('''***** Test results *****'''   )
            _UpperCamelCase														=    trainer.callback_metrics
            # Log and save results to file
            _UpperCamelCase														=    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     ,							lowerCAmelCase       ):
       #  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=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      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase=None     ,							lowerCAmelCase=True     ,							lowerCAmelCase=[]     ,							lowerCAmelCase=None     ,							lowerCAmelCase=None     ,							**lowerCAmelCase     ,							):
       pl.seed_everything(args.seed       )

       # init model
       _UpperCamelCase														=    Path(model.hparams.output_dir       )
       odir.mkdir(exist_ok=lowerCAmelCase       )

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

       _UpperCamelCase														=    {}

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

       if args.gpus > 1:
              _UpperCamelCase														=    '''ddp'''

       _UpperCamelCase														=    args.accumulate_grad_batches
       _UpperCamelCase														=    extra_train_kwargs.get('''accelerator'''     ,							lowerCAmelCase       )
       _UpperCamelCase														=    extra_train_kwargs.get('''profiler'''     ,							lowerCAmelCase       )

       _UpperCamelCase														=    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 json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase=None       ):
       _UpperCamelCase														=    None
       # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
       if str(weight.dtype       ) == "torch.bfloat16":
              # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
              _UpperCamelCase														=    weight.view(torch.intaa       )
              _UpperCamelCase														=    '''bfloat16'''
       _UpperCamelCase														=    weight.cpu().numpy()
       _UpperCamelCase														=    os.path.join(lowerCAmelCase     ,							f'''{weight_name}.dat'''       )
       if index is not None:
              if dtype is None:
                     _UpperCamelCase														=    str(array.dtype       )
              _UpperCamelCase														=    {'''dtype''': dtype, '''shape''': list(array.shape       )}
       if array.ndim == 0:
              _UpperCamelCase														=    array[None]
       _UpperCamelCase														=    np.memmap(lowerCAmelCase     ,							dtype=array.dtype     ,							mode='''w+'''     ,							shape=array.shape       )
       _UpperCamelCase														=    array[:]
       file_array.flush()
       return index


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    tuple(weight_info['''shape''']       )
       if shape == ():
              # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
              _UpperCamelCase														=    (1,)

       _UpperCamelCase														=    weight_info['''dtype''']
       if dtype == "bfloat16":
              # NumPy does not support bfloat16 so this was saved as a int16
              _UpperCamelCase														=    '''int16'''

       _UpperCamelCase														=    np.memmap(lowerCAmelCase     ,							dtype=lowerCAmelCase     ,							shape=lowerCAmelCase     ,							mode='''r'''       )

       if len(weight_info['''shape''']       ) == 0:
              _UpperCamelCase														=    weight[0]
       _UpperCamelCase														=    torch.tensor(lowerCAmelCase       )
       if weight_info["dtype"] == "bfloat16":
              _UpperCamelCase														=    weight.view(torch.bfloataa       )

       return weight


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       if index is None or len(lowerCAmelCase       ) == 0:
              # Nothing to save
              return

       _UpperCamelCase														=    os.path.join(lowerCAmelCase     ,							'''index.json'''       )
       if os.path.isfile(lowerCAmelCase       ):
              with open(lowerCAmelCase     ,							'''r'''     ,							encoding='''utf-8'''       ) as f:
                     _UpperCamelCase														=    json.load(lowerCAmelCase       )
       else:
              _UpperCamelCase														=    {}
       current_index.update(lowerCAmelCase       )

       with open(lowerCAmelCase     ,							'''w'''     ,							encoding='''utf-8'''       ) as f:
              json.dump(lowerCAmelCase     ,							lowerCAmelCase     ,							indent=2       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       os.makedirs(lowerCAmelCase     ,							exist_ok=lowerCAmelCase       )
       _UpperCamelCase														=    {}
       for name, parameter in state_dict.items():
              _UpperCamelCase														=    offload_weight(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							index=lowerCAmelCase       )

       # Update index
       save_offload_index(lowerCAmelCase     ,							lowerCAmelCase       )

class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    dataset
            _UpperCamelCase														=    prefix

     def __getitem__(   self,							A   ):





            """simple docstring"""

            return self.dataset[F'''{self.prefix}{key}''']

     def __iter__(   self   ):





            """simple docstring"""

            return iter([key for key in self.dataset if key.startswith(self.prefix   )]   )



     def __len__(   self   ):





            """simple docstring"""

            return len(self.dataset   )






class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							A = None,							A = None,							A = None,							A=None,							):





            """simple docstring"""

            if state_dict is None and save_folder is None:
                   raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''   )

            _UpperCamelCase														=    {} if state_dict is None else state_dict
            _UpperCamelCase														=    save_folder
            if index is None and save_folder is not None:
                   with open(os.path.join(A,							'''index.json'''   )   ) as f:
                          _UpperCamelCase														=    json.load(A   )
            _UpperCamelCase														=    {} if index is None else index
            _UpperCamelCase														=    list(self.state_dict.keys()   )
            self.all_keys.extend([key for key in self.index if key not in self.all_keys]   )
            _UpperCamelCase														=    device

     def __getitem__(   self,							A   ):





            """simple docstring"""

            if key in self.state_dict:
                   return self.state_dict[key]
            _UpperCamelCase														=    self.index[key]
            if weight_info.get('''safetensors_file'''   ) is not None:
                   if not is_safetensors_available():
                          raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''   )

                   from safetensors import safe_open

                   _UpperCamelCase														=    '''cpu''' if self.device is None else self.device
                   with safe_open(weight_info['''safetensors_file'''],							framework='''pt''',							device=A   ) as f:
                          _UpperCamelCase														=    f.get_tensor(weight_info.get('''weight_name''',							A   )   )

                   if "dtype" in weight_info:
                          return tensor.to(getattr(A,							weight_info['''dtype''']   )   )
                   else:
                          return tensor

            _UpperCamelCase														=    os.path.join(self.save_folder,							F'''{key}.dat'''   )
            return load_offloaded_weight(A,							A   )

     def __iter__(   self   ):





            """simple docstring"""

            return iter(self.all_keys   )



     def __len__(   self   ):





            """simple docstring"""

            return len(self.all_keys   )






def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    {}
       for module_name in submodule_names:
              # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
              # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
              result.update(
                  {
                      key: param
                      for key, param in state_dict.items()
                      if key == module_name or key.startswith(module_name + '''.'''       )
                  }       )
       return result






