


"""simple docstring"""




import json
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image
from torch import nn
from torchvision import transforms
from torchvision.transforms import functional

import transformers
from transformers import (
    AutoConfig,
    AutoImageProcessor,
    AutoModelForSemanticSegmentation,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__snake_case     =		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=2.0.0''', '''To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt''')






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase=0				)			->				Optional[Any]:


     """simple docstring"""


     __UpperCamelCase				=	(size, size) if isinstance(_lowercase			,       _lowercase				) else size
     __UpperCamelCase,  __UpperCamelCase				=	img.size
     __UpperCamelCase				=	size[1] - original_height if original_height < size[1] else 0
     __UpperCamelCase				=	size[0] - original_width if original_width < size[0] else 0
     __UpperCamelCase				=	functional.pad(_lowercase			,       (0, 0, pad_width, pad_height)			,       fill=_lowercase				)
     return img



class 					__lowerCamelCase	:







       def __init__(	self:				Tuple,A_:				List[str]			):






            '''simple docstring'''




            __UpperCamelCase				=	transforms







       def __call__(	self:				Dict,A_:				Any,A_:				str			):






            '''simple docstring'''




            for t in self.transforms:
                 __UpperCamelCase,  __UpperCamelCase				=	t(A_,A_			)
            return image, target



class 					__lowerCamelCase	:







       def __init__(	self:				List[str]			):






            '''simple docstring'''




            pass







       def __call__(	self:				int,A_:				Any,A_:				List[Any]			):






            '''simple docstring'''




            return image, target



class 					__lowerCamelCase	:







       def __init__(	self:				Dict,A_:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	size







       def __call__(	self:				int,A_:				Any,A_:				str			):






            '''simple docstring'''




            __UpperCamelCase				=	functional.resize(A_,self.size			)
            __UpperCamelCase				=	functional.resize(A_,self.size,interpolation=transforms.InterpolationMode.NEAREST			)
            return image, target



class 					__lowerCamelCase	:







       def __init__(	self:				List[Any],A_:				Optional[int],A_:				Union[str, Any]=None			):






            '''simple docstring'''




            __UpperCamelCase				=	min_size
            if max_size is None:
                 __UpperCamelCase				=	min_size
            __UpperCamelCase				=	max_size







       def __call__(	self:				List[Any],A_:				Tuple,A_:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	random.randint(self.min_size,self.max_size			)
            __UpperCamelCase				=	functional.resize(A_,A_			)
            __UpperCamelCase				=	functional.resize(A_,A_,interpolation=transforms.InterpolationMode.NEAREST			)
            return image, target



class 					__lowerCamelCase	:







       def __init__(	self:				List[str],A_:				List[str]			):






            '''simple docstring'''




            __UpperCamelCase				=	size if isinstance(A_,A_			) else (size, size)







       def __call__(	self:				List[Any],A_:				str,A_:				Any			):






            '''simple docstring'''




            __UpperCamelCase				=	pad_if_smaller(A_,self.size			)
            __UpperCamelCase				=	pad_if_smaller(A_,self.size,fill=255			)
            __UpperCamelCase				=	transforms.RandomCrop.get_params(A_,self.size			)
            __UpperCamelCase				=	functional.crop(A_,*A_			)
            __UpperCamelCase				=	functional.crop(A_,*A_			)
            return image, target



class 					__lowerCamelCase	:







       def __init__(	self:				int,A_:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	flip_prob







       def __call__(	self:				Tuple,A_:				str,A_:				Optional[int]			):






            '''simple docstring'''




            if random.random() < self.flip_prob:
                 __UpperCamelCase				=	functional.hflip(A_			)
                 __UpperCamelCase				=	functional.hflip(A_			)
            return image, target



class 					__lowerCamelCase	:







       def __call__(	self:				Dict,A_:				List[str],A_:				Union[str, Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	functional.pil_to_tensor(A_			)
            __UpperCamelCase				=	torch.as_tensor(np.array(A_			),dtype=torch.intaa			)
            return image, target



class 					__lowerCamelCase	:







       def __init__(	self:				List[str],A_:				Any			):






            '''simple docstring'''




            __UpperCamelCase				=	dtype







       def __call__(	self:				Any,A_:				Union[str, Any],A_:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	functional.convert_image_dtype(A_,self.dtype			)
            return image, target



class 					__lowerCamelCase	:







       def __init__(	self:				Optional[int],A_:				int,A_:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	mean
            __UpperCamelCase				=	std







       def __call__(	self:				List[str],A_:				Optional[Any],A_:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	functional.normalize(A_,mean=self.mean,std=self.std			)
            return image, target



class 					__lowerCamelCase	:







       def __call__(	self:				Union[str, Any],A_:				str,A_:				Union[str, Any]			):






            '''simple docstring'''




            if not isinstance(A_,np.ndarray			):
                 __UpperCamelCase				=	np.array(A_			).astype(np.uinta			)
            # avoid using underflow conversion
            __UpperCamelCase				=	255
            __UpperCamelCase				=	target - 1
            __UpperCamelCase				=	255

            __UpperCamelCase				=	Image.fromarray(A_			)
            return image, target



@dataclass
class 					__lowerCamelCase	:
       _lowercase    							=      field(
           default="""segments/sidewalk-semantic"""							,     metadata={
               """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
           }							,     )
       _lowercase    							=      field(
           default=_a							,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}		)
       _lowercase    							=      field(
           default=0.15							,     metadata={"""help""": """Percent to split off of train for validation."""}		)
       _lowercase    							=      field(
           default=_a							,     metadata={
               """help""": (
                   """For debugging purposes or quicker training, truncate the number of training examples to this """
                   """value if set."""
               )
           }							,     )
       _lowercase    							=      field(
           default=_a							,     metadata={
               """help""": (
                   """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                   """value if set."""
               )
           }							,     )
       _lowercase    							=      field(
           default=_a							,     metadata={"""help""": """Whether or not to reduce all labels by 1 and replace background by 255."""}							,     )







       def 						snake_case_					(	self:				List[str]			):






            '''simple docstring'''




            if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
                 raise ValueError(
                     'You must specify either a dataset name from the hub or a train and/or validation directory.'			)



@dataclass
class 					__lowerCamelCase	:
       _lowercase    							=      field(
           default="""nvidia/mit-b0"""							,     metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							,     )
       _lowercase    							=      field(
           default=_a							,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}		)
       _lowercase    							=      field(
           default=_a							,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}		)
       _lowercase    							=      field(
           default="""main"""							,     metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,     )
       _lowercase    							=      field(default=_a							,     metadata={"""help""": """Name or path of preprocessor config."""}		)
       _lowercase    							=      field(
           default=_a							,     metadata={
               """help""": (
                   """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                   """with private models)."""
               )
           }							,     )






def 						_A						(						)			->				Any:


     """simple docstring"""


     __UpperCamelCase				=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase				=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase				=	parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry('run_semantic_segmentation'			,       _lowercase			,       _lowercase				)

     # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,       datefmt='%m/%d/%Y %H:%M:%S'			,       handlers=[logging.StreamHandler(sys.stdout				)]			,       )

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     __UpperCamelCase				=	training_args.get_process_log_level()
     logger.setLevel(_lowercase				)
     transformers.utils.logging.set_verbosity(_lowercase				)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # Log on each process the small summary:
     logger.warning(
         f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
         + f'''distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'''				)
     logger.info(f'''Training/evaluation parameters {training_args}'''				)

     # Detecting last checkpoint.
     __UpperCamelCase				=	None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          __UpperCamelCase				=	get_last_checkpoint(training_args.output_dir				)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
               raise ValueError(
                   f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                   'Use --overwrite_output_dir to overcome.'				)
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
               logger.info(
                   f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                   'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)

    # Load dataset
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    # TODO support datasets from local folders
     __UpperCamelCase				=	load_dataset(data_args.dataset_name			,       cache_dir=model_args.cache_dir				)

     # Rename column names to standardized names (only "image" and "label" need to be present)
     if "pixel_values" in dataset["train"].column_names:
          __UpperCamelCase				=	dataset.rename_columns({'pixel_values': 'image'}				)
     if "annotation" in dataset["train"].column_names:
          __UpperCamelCase				=	dataset.rename_columns({'annotation': 'label'}				)

     # If we don't have a validation split, split off a percentage of train as validation.
     __UpperCamelCase				=	None if 'validation' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split			,       _lowercase				) and data_args.train_val_split > 0.0:
          __UpperCamelCase				=	dataset['train'].train_test_split(data_args.train_val_split				)
          __UpperCamelCase				=	split['train']
          __UpperCamelCase				=	split['test']

     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     if data_args.dataset_name == "scene_parse_150":
          __UpperCamelCase				=	'huggingface/label-files'
          __UpperCamelCase				=	'ade20k-id2label.json'
     else:
          __UpperCamelCase				=	data_args.dataset_name
          __UpperCamelCase				=	'id2label.json'
     __UpperCamelCase				=	json.load(open(hf_hub_download(_lowercase			,       _lowercase			,       repo_type='dataset'				)			,       'r'				)				)
     __UpperCamelCase				=	{int(_lowercase				): v for k, v in idalabel.items()}
     __UpperCamelCase				=	{v: str(_lowercase				) for k, v in idalabel.items()}

     # Load the mean IoU metric from the datasets package
     __UpperCamelCase				=	evaluate.load('mean_iou'				)

     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.
     @torch.no_grad()
     def compute_metrics(_lowercase				):
          __UpperCamelCase,  __UpperCamelCase				=	eval_pred
          __UpperCamelCase				=	torch.from_numpy(_lowercase				)
          # scale the logits to the size of the label
          __UpperCamelCase				=	nn.functional.interpolate(
              _lowercase			,       size=labels.shape[-2:]			,       mode='bilinear'			,       align_corners=_lowercase			,       ).argmax(dim=1				)

          __UpperCamelCase				=	logits_tensor.detach().cpu().numpy()
          __UpperCamelCase				=	metric.compute(
              predictions=_lowercase			,       references=_lowercase			,       num_labels=len(_lowercase				)			,       ignore_index=0			,       reduce_labels=image_processor.do_reduce_labels			,       )
          # add per category metrics as individual key-value pairs
          __UpperCamelCase				=	metrics.pop('per_category_accuracy'				).tolist()
          __UpperCamelCase				=	metrics.pop('per_category_iou'				).tolist()

          metrics.update({f'''accuracy_{idalabel[i]}''': v for i, v in enumerate(_lowercase				)}				)
          metrics.update({f'''iou_{idalabel[i]}''': v for i, v in enumerate(_lowercase				)}				)

          return metrics

     __UpperCamelCase				=	AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path			,       labelaid=_lowercase			,       idalabel=_lowercase			,       cache_dir=model_args.cache_dir			,       revision=model_args.model_revision			,       use_auth_token=True if model_args.use_auth_token else None			,       )
     __UpperCamelCase				=	AutoModelForSemanticSegmentation.from_pretrained(
         model_args.model_name_or_path			,       from_tf=bool('.ckpt' in model_args.model_name_or_path				)			,       config=_lowercase			,       cache_dir=model_args.cache_dir			,       revision=model_args.model_revision			,       use_auth_token=True if model_args.use_auth_token else None			,       )
     __UpperCamelCase				=	AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path			,       cache_dir=model_args.cache_dir			,       revision=model_args.model_revision			,       use_auth_token=True if model_args.use_auth_token else None			,       )

     # Define torchvision transforms to be applied to each image + target.
     # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
     # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
     if "shortest_edge" in image_processor.size:
          # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
          __UpperCamelCase				=	(image_processor.size['shortest_edge'], image_processor.size['shortest_edge'])
     else:
          __UpperCamelCase				=	(image_processor.size['height'], image_processor.size['width'])
     __UpperCamelCase				=	Compose(
         [
             ReduceLabels() if data_args.reduce_labels else Identity(),
             RandomCrop(size=_lowercase				),
             RandomHorizontalFlip(flip_prob=0.5				),
             PILToTensor(),
             ConvertImageDtype(torch.float				),
             Normalize(mean=image_processor.image_mean			,       std=image_processor.image_std				),
         ]				)
     # Define torchvision transform to be applied to each image.
     # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
     __UpperCamelCase				=	Compose(
         [
             ReduceLabels() if data_args.reduce_labels else Identity(),
             Resize(size=_lowercase				),
             PILToTensor(),
             ConvertImageDtype(torch.float				),
             Normalize(mean=image_processor.image_mean			,       std=image_processor.image_std				),
         ]				)

     def preprocess_train(_lowercase				):
          __UpperCamelCase				=	[]
          __UpperCamelCase				=	[]
          for image, target in zip(example_batch['image']			,       example_batch['label']				):
               __UpperCamelCase,  __UpperCamelCase				=	train_transforms(image.convert('RGB'				)			,       _lowercase				)
               pixel_values.append(_lowercase				)
               labels.append(_lowercase				)

          __UpperCamelCase				=	{}
          __UpperCamelCase				=	torch.stack(_lowercase				)
          __UpperCamelCase				=	torch.stack(_lowercase				)

          return encoding

     def preprocess_val(_lowercase				):
          __UpperCamelCase				=	[]
          __UpperCamelCase				=	[]
          for image, target in zip(example_batch['image']			,       example_batch['label']				):
               __UpperCamelCase,  __UpperCamelCase				=	val_transforms(image.convert('RGB'				)			,       _lowercase				)
               pixel_values.append(_lowercase				)
               labels.append(_lowercase				)

          __UpperCamelCase				=	{}
          __UpperCamelCase				=	torch.stack(_lowercase				)
          __UpperCamelCase				=	torch.stack(_lowercase				)

          return encoding

     if training_args.do_train:
          if "train" not in dataset:
               raise ValueError('--do_train requires a train dataset'				)
          if data_args.max_train_samples is not None:
               __UpperCamelCase				=	(
                   dataset['train'].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
               )
          # Set the training transforms
          dataset["train"].set_transform(_lowercase				)

     if training_args.do_eval:
          if "validation" not in dataset:
               raise ValueError('--do_eval requires a validation dataset'				)
          if data_args.max_eval_samples is not None:
               __UpperCamelCase				=	(
                   dataset['validation'].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
               )
          # Set the validation transforms
          dataset["validation"].set_transform(_lowercase				)

     # Initalize our trainer
     __UpperCamelCase				=	Trainer(
         model=_lowercase			,       args=_lowercase			,       train_dataset=dataset['train'] if training_args.do_train else None			,       eval_dataset=dataset['validation'] if training_args.do_eval else None			,       compute_metrics=_lowercase			,       tokenizer=_lowercase			,       data_collator=_lowercase			,       )

     # Training
     if training_args.do_train:
          __UpperCamelCase				=	None
          if training_args.resume_from_checkpoint is not None:
               __UpperCamelCase				=	training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               __UpperCamelCase				=	last_checkpoint
          __UpperCamelCase				=	trainer.train(resume_from_checkpoint=_lowercase				)
          trainer.save_model()
          trainer.log_metrics('train'			,       train_result.metrics				)
          trainer.save_metrics('train'			,       train_result.metrics				)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          __UpperCamelCase				=	trainer.evaluate()
          trainer.log_metrics('eval'			,       _lowercase				)
          trainer.save_metrics('eval'			,       _lowercase				)

     # Write model card and (optionally) push to hub
     __UpperCamelCase				=	{
         'finetuned_from': model_args.model_name_or_path,
         'dataset': data_args.dataset_name,
         'tags': ['image-segmentation', 'vision'],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**_lowercase				)
     else:
          trainer.create_model_card(**_lowercase				)


if __name__ == "__main__":
    main()






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


__snake_case     =		logging.getLogger(__name__)

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

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



class 					__lowerCamelCase	(pl.LightningModule		):







       def __init__(	self:				Optional[int],A_:				argparse.Namespace,A_:				Tuple=None,A_:				Tuple="base",A_:				List[str]=None,A_:				List[Any]=None,A_:				Union[str, Any]=None,**A_:				Optional[Any],):






            '''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 						snake_case_					(	self:				Dict,*A_:				Union[str, Any],**A_:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	self.model_type.from_pretrained(*A_,**A_			)







       def 						snake_case_					(	self:				int			):






            '''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 						snake_case_					(	self:				Optional[Any]			):






            '''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 						snake_case_					(	self:				Any,A_:				List[Any],A_:				Dict			):






            '''simple docstring'''




            return self.validation_step(A_,A_			)







       def 						snake_case_					(	self:				List[Any],A_:				Dict			):






            '''simple docstring'''




            return self.validation_end(A_			)







       def 						snake_case_					(	self:				Optional[int]			):






            '''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 						snake_case_					(	self:				int,A_:				Tuple			):






            '''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 						snake_case_					(	self:				List[str],A_:				str,A_:				int,A_:				bool = False			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            return self.train_loader







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				Any,A_:				Union[str, Any]			):






            '''simple docstring'''




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







       @pl.utilities.rank_zero_only
       def 						snake_case_					(	self:				Any,A_:				Dict[str, Any]			):






            '''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 						snake_case_					(	A_:				int,A_:				List[Any]			):






            '''simple docstring'''




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



class 					__lowerCamelCase	(pl.Callback		):







       def 						snake_case_					(	self:				List[str],A_:				Any,A_:				List[str]			):






            '''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 						snake_case_					(	self:				Optional[Any],A_:				pl.Trainer,A_:				pl.LightningModule			):






            '''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 						snake_case_					(	self:				List[str],A_:				pl.Trainer,A_:				pl.LightningModule			):






            '''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 						_A						(						_lowercase			,       _lowercase				)			->				None:


     """simple docstring"""


     parser.add_argument(
         '--output_dir'			,       default=_lowercase			,       type=_lowercase			,       required=_lowercase			,       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=_lowercase			,       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=_lowercase				)
     parser.add_argument('--max_grad_norm'			,       dest='gradient_clip_val'			,       default=1.0			,       type=_lowercase			,       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=_lowercase			,       default=1			,       help='Number of updates steps to accumulate before performing a backward/update pass.'			,       )
     parser.add_argument('--seed'			,       type=_lowercase			,       default=42			,       help='random seed for initialization'				)
     parser.add_argument(
         '--data_dir'			,       default=_lowercase			,       type=_lowercase			,       required=_lowercase			,       help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'			,       )






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=True			,       _lowercase=[]			,       _lowercase=None			,       _lowercase=None			,       **_lowercase			,       )			->				int:


     """simple docstring"""


     pl.seed_everything(args.seed				)

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

     # 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(_lowercase				)
     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'			,       _lowercase				)
     __UpperCamelCase				=	extra_train_kwargs.get('profiler'			,       _lowercase				)

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

     if args.do_train:
          trainer.fit(_lowercase				)

     return trainer


