
"""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


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      UpperCamelCase	(       pl.LightningModule      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__=None		,	lowercase__="base"		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	**lowercase__		,	)    ->    List[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(lowercase__			)
									SCREAMING_SNAKE_CASE				=	0
									SCREAMING_SNAKE_CASE				=	Path(self.hparams.output_dir			)
									SCREAMING_SNAKE_CASE				=	self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
														SCREAMING_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=lowercase__		,	**lowercase__		,	)
									else:
														SCREAMING_SNAKE_CASE				=	config

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

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


				def 							A					(				self		,	*lowercase__		,	**lowercase__			)    ->    List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.model_type.from_pretrained(*lowercase__		,	**lowercase__			)


				def 							A					(				self			)    ->    List[str]:
									"""simple docstring"""







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


				def 							A					(				self			)    ->    List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.model
									SCREAMING_SNAKE_CASE				=	['bias', 'LayerNorm.weight']
									SCREAMING_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:
														SCREAMING_SNAKE_CASE				=	Adafactor(
														    lowercase__		,	lr=self.hparams.learning_rate		,	scale_parameter=lowercase__		,	relative_step=lowercase__			)

									else:
														SCREAMING_SNAKE_CASE				=	AdamW(
														    lowercase__		,	lr=self.hparams.learning_rate		,	eps=self.hparams.adam_epsilon			)
									SCREAMING_SNAKE_CASE				=	optimizer

									SCREAMING_SNAKE_CASE				=	self.get_lr_scheduler()

									return [optimizer], [scheduler]


				def 							A					(				self		,	lowercase__		,	lowercase__			)    ->    str:
									"""simple docstring"""







									return self.validation_step(lowercase__		,	lowercase__			)


				def 							A					(				self		,	lowercase__			)    ->    List[Any]:
									"""simple docstring"""







									return self.validation_end(lowercase__			)


				def 							A					(				self			)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	max(1		,	self.hparams.gpus			)  # TODO: consider num_tpu_cores
									SCREAMING_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 							A					(				self		,	lowercase__			)    ->    Union[str, Any]:
									"""simple docstring"""







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


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__ = False			)    ->    List[str]:
									"""simple docstring"""







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


				def 							A					(				self			)    ->    Dict:
									"""simple docstring"""







									return self.train_loader


				def 							A					(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







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


				def 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







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


				def 							A					(				self		,	lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







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


				@pl.utilities.rank_zero_only
				def 							A					(				self		,	lowercase__			)    ->    None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.output_dir.joinpath('best_tfmr'			)
									SCREAMING_SNAKE_CASE				=	self.step_count
									self.model.save_pretrained(lowercase__			)
									self.tokenizer.save_pretrained(lowercase__			)




				@staticmethod
				def 							A					(				lowercase__		,	lowercase__			)    ->    Tuple:
									"""simple docstring"""







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






class      UpperCamelCase	(       pl.Callback      ):

				"""simple docstring"""



				def 							A					(				self		,	lowercase__		,	lowercase__			)    ->    Dict:
									"""simple docstring"""







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


				def 							A					(				self		,	lowercase__		,	lowercase__			)    ->    List[str]:
									"""simple docstring"""







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




				def 							A					(				self		,	lowercase__		,	lowercase__			)    ->    Tuple:
									"""simple docstring"""







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






def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					#  To allow all pl args uncomment the following line
					#  parser = pl.Trainer.add_argparse_args(parser)
					parser.add_argument(
					    '--output_dir',     default=SCREAMING_SNAKE_CASE_,     type=SCREAMING_SNAKE_CASE_,     required=SCREAMING_SNAKE_CASE_,     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=SCREAMING_SNAKE_CASE_,     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=SCREAMING_SNAKE_CASE_			)
					parser.add_argument('--max_grad_norm',     dest='gradient_clip_val',     default=1.0,     type=SCREAMING_SNAKE_CASE_,     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=SCREAMING_SNAKE_CASE_,     default=1,     help='Number of updates steps to accumulate before performing a backward/update pass.',     )
					parser.add_argument('--seed',     type=SCREAMING_SNAKE_CASE_,     default=4_2,     help='random seed for initialization'			)
					parser.add_argument(
					    '--data_dir',     default=SCREAMING_SNAKE_CASE_,     type=SCREAMING_SNAKE_CASE_,     required=SCREAMING_SNAKE_CASE_,     help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.',     )



def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=[],     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=None,     **SCREAMING_SNAKE_CASE_,     ):
					pl.seed_everything(args.seed			)

					# init model
					SCREAMING_SNAKE_CASE				=	Path(model.hparams.output_dir			)
					odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)

					# add custom checkpoints
					if checkpoint_callback is None:
										SCREAMING_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(SCREAMING_SNAKE_CASE_			)
					if logging_callback is None:
										SCREAMING_SNAKE_CASE				=	LoggingCallback()

					SCREAMING_SNAKE_CASE				=	{}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										SCREAMING_SNAKE_CASE				=	1_6
										SCREAMING_SNAKE_CASE				=	args.fpaa_opt_level

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

					SCREAMING_SNAKE_CASE				=	args.accumulate_grad_batches
					SCREAMING_SNAKE_CASE				=	extra_train_kwargs.get('accelerator',     SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	extra_train_kwargs.get('profiler',     SCREAMING_SNAKE_CASE_			)

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

					if args.do_train:
										trainer.fit(SCREAMING_SNAKE_CASE_			)

					return trainer






import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image





class  __lowerCamelCase       (    unittest.TestCase	,				UpperCamelCase__    ):




    """simple docstring"""

    def    a      (       self     :     Any   )      ->       Tuple:
         lowerCAmelCase__		= load_tool("image-captioning"   )
         self.tool.setup()
         lowerCAmelCase__		= load_tool("image-captioning"   ,	remote=SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     int   )      ->       List[str]:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.tool(SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     List[str]   )      ->       int:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.remote_tool(SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     Any   )      ->       Any:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.tool(image=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )


    def    a      (       self     :     Dict   )      ->       Any:
         lowerCAmelCase__		= Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"   )
         lowerCAmelCase__		= self.remote_tool(image=SCREAMING_SNAKE_CASE__   )
         self.assertEqual(SCREAMING_SNAKE_CASE__   ,	"two cats sleeping on a couch"   )
