




'''simple docstring'''



from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCAmelCase_    :					Union[str, Any]          =						logging.get_logger(__name__)  # pylint: disable=invalid-name





class       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: int	    =     ['''safety_checker''', '''feature_extractor''']


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = True      ,					) ->					str:



            '''simple docstring'''




            super().__init__()
            if hasattr(scheduler.config      ,					'''steps_offset'''					) and scheduler.config.steps_offset != 1:
                 __snake_case         =					(
                     F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
                     F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
                     '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
                     ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
                     ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
                     ''' file'''
                 )
                 deprecate('''steps_offset!=1'''      ,					'''1.0.0'''      ,					__SCREAMING_SNAKE_CASE      ,					standard_warn=__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					dict(scheduler.config					)
                 __snake_case         =					1
                 __snake_case         =					FrozenDict(__SCREAMING_SNAKE_CASE					)

            self.register_modules(
                vae=__SCREAMING_SNAKE_CASE      ,					text_encoder=__SCREAMING_SNAKE_CASE      ,					tokenizer=__SCREAMING_SNAKE_CASE      ,					unet=__SCREAMING_SNAKE_CASE      ,					scheduler=__SCREAMING_SNAKE_CASE      ,					safety_checker=__SCREAMING_SNAKE_CASE      ,					feature_extractor=__SCREAMING_SNAKE_CASE      ,					)
            self.register_to_config(requires_safety_checker=__SCREAMING_SNAKE_CASE					)


       @property
       def  lowerCAmelCase (				self					) ->					Dict[str, Any]:



            '''simple docstring'''




            return {k: getattr(self      ,					__SCREAMING_SNAKE_CASE					) for k in self.config.keys() if not k.startswith('''_'''					)}


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



            '''simple docstring'''




            if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 __snake_case         =					self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(__SCREAMING_SNAKE_CASE					)


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



            '''simple docstring'''




            self.enable_attention_slicing(__SCREAMING_SNAKE_CASE					)


       @torch.no_grad()
       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = 0.8      ,					__SCREAMING_SNAKE_CASE = 50      ,					__SCREAMING_SNAKE_CASE = 7.5      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					__SCREAMING_SNAKE_CASE = 0.0      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = "pil"      ,					__SCREAMING_SNAKE_CASE = True      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					) ->					List[str]:



            '''simple docstring'''




            return StableDiffusionInpaintPipelineLegacy(**self.components					)(
                prompt=__SCREAMING_SNAKE_CASE      ,					image=__SCREAMING_SNAKE_CASE      ,					mask_image=__SCREAMING_SNAKE_CASE      ,					strength=__SCREAMING_SNAKE_CASE      ,					num_inference_steps=__SCREAMING_SNAKE_CASE      ,					guidance_scale=__SCREAMING_SNAKE_CASE      ,					negative_prompt=__SCREAMING_SNAKE_CASE      ,					num_images_per_prompt=__SCREAMING_SNAKE_CASE      ,					eta=__SCREAMING_SNAKE_CASE      ,					generator=__SCREAMING_SNAKE_CASE      ,					output_type=__SCREAMING_SNAKE_CASE      ,					return_dict=__SCREAMING_SNAKE_CASE      ,					callback=__SCREAMING_SNAKE_CASE      ,					)


       @torch.no_grad()
       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = 0.8      ,					__SCREAMING_SNAKE_CASE = 50      ,					__SCREAMING_SNAKE_CASE = 7.5      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					__SCREAMING_SNAKE_CASE = 0.0      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = "pil"      ,					__SCREAMING_SNAKE_CASE = True      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					Dict:



            '''simple docstring'''




            return StableDiffusionImgaImgPipeline(**self.components					)(
                prompt=__SCREAMING_SNAKE_CASE      ,					image=__SCREAMING_SNAKE_CASE      ,					strength=__SCREAMING_SNAKE_CASE      ,					num_inference_steps=__SCREAMING_SNAKE_CASE      ,					guidance_scale=__SCREAMING_SNAKE_CASE      ,					negative_prompt=__SCREAMING_SNAKE_CASE      ,					num_images_per_prompt=__SCREAMING_SNAKE_CASE      ,					eta=__SCREAMING_SNAKE_CASE      ,					generator=__SCREAMING_SNAKE_CASE      ,					output_type=__SCREAMING_SNAKE_CASE      ,					return_dict=__SCREAMING_SNAKE_CASE      ,					callback=__SCREAMING_SNAKE_CASE      ,					callback_steps=__SCREAMING_SNAKE_CASE      ,					)




       @torch.no_grad()
       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = 512      ,					__SCREAMING_SNAKE_CASE = 512      ,					__SCREAMING_SNAKE_CASE = 50      ,					__SCREAMING_SNAKE_CASE = 7.5      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					__SCREAMING_SNAKE_CASE = 0.0      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = "pil"      ,					__SCREAMING_SNAKE_CASE = True      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					) ->					str:



            '''simple docstring'''




            return StableDiffusionPipeline(**self.components					)(
                prompt=__SCREAMING_SNAKE_CASE      ,					height=__SCREAMING_SNAKE_CASE      ,					width=__SCREAMING_SNAKE_CASE      ,					num_inference_steps=__SCREAMING_SNAKE_CASE      ,					guidance_scale=__SCREAMING_SNAKE_CASE      ,					negative_prompt=__SCREAMING_SNAKE_CASE      ,					num_images_per_prompt=__SCREAMING_SNAKE_CASE      ,					eta=__SCREAMING_SNAKE_CASE      ,					generator=__SCREAMING_SNAKE_CASE      ,					latents=__SCREAMING_SNAKE_CASE      ,					output_type=__SCREAMING_SNAKE_CASE      ,					return_dict=__SCREAMING_SNAKE_CASE      ,					callback=__SCREAMING_SNAKE_CASE      ,					callback_steps=__SCREAMING_SNAKE_CASE      ,					)












'''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






