




'''simple docstring'''



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: Optional[Any]	    =     {
           '''sld_warmup_steps''': 15,
           '''sld_guidance_scale''': 20,
           '''sld_threshold''': 0.0,
           '''sld_momentum_scale''': 0.0,
           '''sld_mom_beta''': 0.0,
       }
       __lowercase					: Dict	    =     {
           '''sld_warmup_steps''': 10,
           '''sld_guidance_scale''': 1000,
           '''sld_threshold''': 0.01,
           '''sld_momentum_scale''': 0.3,
           '''sld_mom_beta''': 0.4,
       }
       __lowercase					: Tuple	    =     {
           '''sld_warmup_steps''': 7,
           '''sld_guidance_scale''': 2000,
           '''sld_threshold''': 0.025,
           '''sld_momentum_scale''': 0.5,
           '''sld_mom_beta''': 0.7,
       }
       __lowercase					: Union[str, Any]	    =     {
           '''sld_warmup_steps''': 0,
           '''sld_guidance_scale''': 5000,
           '''sld_threshold''': 1.0,
           '''sld_momentum_scale''': 0.5,
           '''sld_mom_beta''': 0.7,
       }





@dataclass
class       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: Union[List[PIL.Image.Image], np.ndarray]
       __lowercase					: Optional[List[bool]]
       __lowercase					: Optional[Union[List[PIL.Image.Image], np.ndarray]]
       __lowercase					: Optional[str]


if is_transformers_available() and is_torch_available():
     from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
     from .safety_checker import SafeStableDiffusionSafetyChecker












'''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


UpperCAmelCase_    :					Tuple					      =						logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

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





class       lowerCAmelCase				(	pl.LightningModule):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE="base"      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=None      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					Optional[int]:



            '''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(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					0
            __snake_case         =					Path(self.hparams.output_dir					)
            __snake_case         =					self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                 __snake_case         =					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=__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE      ,					)
            else:
                 __snake_case         =					config

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

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


       def  lowerCAmelCase (				self      ,					*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




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

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

            __snake_case         =					self.get_lr_scheduler()

            return [optimizer], [scheduler]


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Any:



            '''simple docstring'''




            return self.validation_step(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					List[str]:



            '''simple docstring'''




            return self.validation_end(__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case         =					max(1      ,					self.hparams.gpus					)  # TODO: consider num_tpu_cores
            __snake_case         =					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      ,					__SCREAMING_SNAKE_CASE					) ->					str:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = False					) ->					str:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            return self.train_loader


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




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


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



            '''simple docstring'''




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


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            return os.path.join(
                self.hparams.data_dir      ,					'''cached_{}_{}_{}'''.format(
                    __SCREAMING_SNAKE_CASE      ,					list(filter(__SCREAMING_SNAKE_CASE      ,					self.hparams.model_name_or_path.split('''/'''					)					)					).pop()      ,					str(self.hparams.max_seq_length					)      ,					)      ,					)


       @pl.utilities.rank_zero_only
       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					None:



            '''simple docstring'''




            __snake_case         =					self.output_dir.joinpath('''best_tfmr'''					)
            __snake_case         =					self.step_count
            self.model.save_pretrained(__SCREAMING_SNAKE_CASE					)
            self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE					)




       @staticmethod
       def  lowerCAmelCase (				__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




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





class       lowerCAmelCase				(	pl.Callback):


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




            rank_zero_info('''***** Validation results *****'''					)
            __snake_case         =					trainer.callback_metrics
            # Log results
            for key in sorted(__SCREAMING_SNAKE_CASE					):
                 if key not in ["log", "progress_bar"]:
                      rank_zero_info('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE      ,					str(metrics[key]					)					)					)




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




            rank_zero_info('''***** Test results *****'''					)
            __snake_case         =					trainer.callback_metrics
            # Log and save results to file
            __snake_case         =					os.path.join(pl_module.hparams.output_dir      ,					'''test_results.txt'''					)
            with open(__SCREAMING_SNAKE_CASE      ,					'''w'''					) as writer:
                 for key in sorted(__SCREAMING_SNAKE_CASE					):
                      if key not in ["log", "progress_bar"]:
                           rank_zero_info('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE      ,					str(metrics[key]					)					)					)
                           writer.write('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE      ,					str(metrics[key]					)					)					)







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[int]				,		_lowerCamelCase							:       Optional[Any]		)->				None:




     '''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      _UpperCamelCase	(_lowerCamelCase							:       BaseTransformer				,		_lowerCamelCase							:       argparse.Namespace				,		_lowerCamelCase							:       Any=None				,		_lowerCamelCase							:       Union[str, Any]=True				,		_lowerCamelCase							:       Union[str, Any]=[]				,		_lowerCamelCase							:       int=None				,		_lowerCamelCase							:       Any=None				,		**_lowerCamelCase							:       Tuple				,		)->				Optional[Any]:




     '''simple docstring'''





     pl.seed_everything(args.seed		)

     # init model
     __snake_case         =					Path(model.hparams.output_dir		)
     odir.mkdir(exist_ok=_lowerCamelCase		)

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

     __snake_case         =					{}

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

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

     __snake_case         =					args.accumulate_grad_batches
     __snake_case         =					extra_train_kwargs.get('''accelerator'''				,		_lowerCamelCase		)
     __snake_case         =					extra_train_kwargs.get('''profiler'''				,		_lowerCamelCase		)

     __snake_case         =					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






