




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    				=		logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

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



class     a  (     pl.LightningModule    ):

     '''simple docstring'''





     def __init__(			self     :    Dict      ,					__snake_case     :    argparse.Namespace      ,					__snake_case     :    Optional[Any]=None      ,					__snake_case     :    str="base"      ,					__snake_case     :    Dict=None      ,					__snake_case     :    Any=None      ,					__snake_case     :    Any=None      ,					**__snake_case     :    Union[str, 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(__snake_case       )
         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=__snake_case      ,					**__snake_case      ,					)
         else:
             UpperCAmelCase_											=  config

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

         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=__snake_case      ,					)
         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=__snake_case      ,					)
         else:
             UpperCAmelCase_											=  model
     def 						lowerCamelCase_     (			self     :    List[Any]      ,					*__snake_case     :    Tuple      ,					**__snake_case     :    Optional[Any]       ):
         UpperCAmelCase_											=  self.model_type.from_pretrained(*__snake_case      ,					**__snake_case       )
     def 						lowerCamelCase_     (			self     :    Union[str, Any]       ):
         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 						lowerCamelCase_     (			self     :    Optional[Any]       ):
         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(
                 __snake_case      ,					lr=self.hparams.learning_rate      ,					scale_parameter=__snake_case      ,					relative_step=__snake_case       )

         else:
             UpperCAmelCase_											=  AdamW(
                 __snake_case      ,					lr=self.hparams.learning_rate      ,					eps=self.hparams.adam_epsilon       )
         UpperCAmelCase_											=  optimizer

         UpperCAmelCase_											=  self.get_lr_scheduler()

         return [optimizer], [scheduler]
     def 						lowerCamelCase_     (			self     :    str      ,					__snake_case     :    Optional[int]      ,					__snake_case     :    Optional[int]       ):
         return self.validation_step(__snake_case      ,					__snake_case       )
     def 						lowerCamelCase_     (			self     :    Any      ,					__snake_case     :    List[Any]       ):
         return self.validation_end(__snake_case       )
     def 						lowerCamelCase_     (			self     :    List[Any]       ):
         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 						lowerCamelCase_     (			self     :    Optional[int]      ,					__snake_case     :    str       ):
         if stage == "test":
             UpperCAmelCase_											=  len(self.test_dataloader().dataset       )
         else:
             UpperCAmelCase_											=  self.get_dataloader('''train'''      ,					self.hparams.train_batch_size      ,					shuffle=__snake_case       )
             UpperCAmelCase_											=  len(self.train_dataloader().dataset       )
     def 						lowerCamelCase_     (			self     :    Union[str, Any]      ,					__snake_case     :    str      ,					__snake_case     :    int      ,					__snake_case     :    bool = False       ):
         raise NotImplementedError('''You must implement this for your task'''       )
     def 						lowerCamelCase_     (			self     :    List[str]       ):
         return self.train_loader
     def 						lowerCamelCase_     (			self     :    List[str]       ):
         return self.get_dataloader('''dev'''      ,					self.hparams.eval_batch_size      ,					shuffle=__snake_case       )
     def 						lowerCamelCase_     (			self     :    int       ):
         return self.get_dataloader('''test'''      ,					self.hparams.eval_batch_size      ,					shuffle=__snake_case       )
     def 						lowerCamelCase_     (			self     :    Optional[int]      ,					__snake_case     :    List[str]       ):
         return os.path.join(
             self.hparams.data_dir      ,					'''cached_{}_{}_{}'''.format(
                 __snake_case      ,					list(filter(__snake_case      ,					self.hparams.model_name_or_path.split('''/'''       )       )       ).pop()      ,					str(self.hparams.max_seq_length       )      ,					)      ,					)
     @pl.utilities.rank_zero_only
     def 						lowerCamelCase_     (			self     :    Any      ,					__snake_case     :    Dict[str, Any]       ):
         UpperCAmelCase_											=  self.output_dir.joinpath('''best_tfmr'''       )
         UpperCAmelCase_											=  self.step_count
         self.model.save_pretrained(__snake_case       )
         self.tokenizer.save_pretrained(__snake_case       )




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



class     a  (     pl.Callback    ):

     '''simple docstring'''





     def 						lowerCamelCase_     (			self     :    Tuple      ,					__snake_case     :    Optional[Any]      ,					__snake_case     :    Optional[Any]       ):
         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    ):

     '''simple docstring'''





     def 						lowerCamelCase_     (			self     :    Optional[Any]      ,					__snake_case     :    Optional[int]      ,					__snake_case     :    Optional[Any]       ):
         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(__snake_case       )
     def 						lowerCamelCase_     (			self     :    Optional[int]      ,					__snake_case     :    pl.Trainer      ,					__snake_case     :    pl.LightningModule       ):
         rank_zero_info('''***** Validation results *****'''       )
         UpperCAmelCase_											=  trainer.callback_metrics
         # Log results
         for key in sorted(__snake_case       ):
             if key not in ["log", "progress_bar"]:
                 rank_zero_info('''{} = {}\n'''.format(__snake_case      ,					str(metrics[key]       )       )       )




     def 						lowerCamelCase_     (			self     :    Optional[int]      ,					__snake_case     :    pl.Trainer      ,					__snake_case     :    pl.LightningModule       ):
         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(__snake_case      ,					'''w'''       ) as writer:
             for key in sorted(__snake_case       ):
                 if key not in ["log", "progress_bar"]:
                     rank_zero_info('''{} = {}\n'''.format(__snake_case      ,					str(metrics[key]       )       )       )
                     writer.write('''{} = {}\n'''.format(__snake_case      ,					str(metrics[key]       )       )       )




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		str ,  __UpperCamelCase    :		Dict   )						->							None:
    #  To allow all pl args uncomment the following line
    #  parser = pl.Trainer.add_argparse_args(parser)
    parser.add_argument(
        '''--output_dir''' ,  default=__UpperCamelCase ,  type=__UpperCamelCase ,  required=__UpperCamelCase ,  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=__UpperCamelCase ,  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=__UpperCamelCase   )
    parser.add_argument('''--max_grad_norm''' ,  dest='''gradient_clip_val''' ,  default=1.0 ,  type=__UpperCamelCase ,  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=__UpperCamelCase ,  default=1 ,  help='''Number of updates steps to accumulate before performing a backward/update pass.''' ,  )
    parser.add_argument('''--seed''' ,  type=__UpperCamelCase ,  default=42 ,  help='''random seed for initialization'''   )
    parser.add_argument(
        '''--data_dir''' ,  default=__UpperCamelCase ,  type=__UpperCamelCase ,  required=__UpperCamelCase ,  help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' ,  )




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		BaseTransformer ,  __UpperCamelCase    :		argparse.Namespace ,  __UpperCamelCase    :		List[Any]=None ,  __UpperCamelCase    :		Tuple=True ,  __UpperCamelCase    :		Dict=None ,  __UpperCamelCase    :		int=[] ,  __UpperCamelCase    :		Union[str, Any]=None ,  __UpperCamelCase    :		List[str]=None ,  **__UpperCamelCase    :		Optional[Any] ,  )						->							Union[str, Any]:
    pl.seed_everything(args.seed   )

    # init model
    UpperCAmelCase_											=  Path(model.hparams.output_dir   )
    odir.mkdir(exist_ok=__UpperCamelCase   )

    # 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(__UpperCamelCase   )
    if logging_callback is None:
        UpperCAmelCase_											=  LoggingCallback()

    UpperCAmelCase_											=  {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        UpperCAmelCase_											=  16
        # train_params["amp_level"] = args.fp16_opt_level

    if args.gpus > 1:
        UpperCAmelCase_											=  '''auto'''  # "ddp"
        UpperCAmelCase_											=  '''ddp'''

    UpperCAmelCase_											=  args.accumulate_grad_batches
    UpperCAmelCase_											=  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
    UpperCAmelCase_											=  '''auto'''

    UpperCAmelCase_											=  pl.Trainer.from_argparse_args(
        __UpperCamelCase ,  weights_summary=__UpperCamelCase ,  callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()] ,  logger=__UpperCamelCase ,  **__UpperCamelCase ,  )

    if args.do_train:
        trainer.fit(__UpperCamelCase   )

    return trainer











import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils



class     a  (     unittest.TestCase    ):

     '''simple docstring'''





     @patch('''diffusers.utils.hub_utils.get_full_repo_name'''       )
     def 						lowerCamelCase_     (			self     :    List[Any]      ,					__snake_case     :    Mock       ):
         UpperCAmelCase_											=  '''full_repo_name'''
         with TemporaryDirectory() as tmpdir:
             # Dummy args values
             UpperCAmelCase_											=  Mock()
             UpperCAmelCase_											=  tmpdir
             UpperCAmelCase_											=  0
             UpperCAmelCase_											=  '''hub_token'''
             UpperCAmelCase_											=  '''dataset_name'''
             UpperCAmelCase_											=  0.01
             UpperCAmelCase_											=  10_00_00
             UpperCAmelCase_											=  1_00_00
             UpperCAmelCase_											=  0.01
             UpperCAmelCase_											=  0.02
             UpperCAmelCase_											=  0.03
             UpperCAmelCase_											=  0.0_005
             UpperCAmelCase_											=  0.000_001
             UpperCAmelCase_											=  1
             UpperCAmelCase_											=  10
             UpperCAmelCase_											=  0.001
             UpperCAmelCase_											=  0.1
             UpperCAmelCase_											=  0.2
             UpperCAmelCase_											=  True

             # Model card mush be rendered and saved
             diffusers.utils.hub_utils.create_model_card(__snake_case      ,					model_name='''model_name'''       )
             self.assertTrue((Path(__snake_case       ) / '''README.md''').is_file()       )





