
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCamelCase__        =						logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

lowerCamelCase__        =						{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCamelCase__        =						{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCamelCase__        =						sorted(arg_to_scheduler.keys())
lowerCamelCase__        =						"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""







class 	A__				(	pl.LightningModule      ):






				def __init__(		self   : str       ,	a   : argparse.Namespace       ,	a   : Optional[int]=None       ,	a   : List[str]="base"       ,	a   : List[str]=None       ,	a   : List[str]=None       ,	a   : Dict=None       ,	**a   : Dict       ,	):






							'''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  )
							lowerCAmelCase__ : List[Any]   =						0
							lowerCAmelCase__ : List[Any]   =						Path(self.hparams.output_dir  )
							lowerCAmelCase__ : Union[str, Any]   =						self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
										lowerCAmelCase__ : Dict   =						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:
										lowerCAmelCase__ : PretrainedConfig   =						config

							lowerCAmelCase__ : Dict   =						('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:
										lowerCAmelCase__ : Optional[Any]   =						AutoTokenizer.from_pretrained(
										    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,	cache_dir=a       ,	)
							else:
										lowerCAmelCase__ : PreTrainedTokenizer   =						tokenizer
							lowerCAmelCase__ : Tuple   =						MODEL_MODES[mode]
							if model is None:
										lowerCAmelCase__ : Any   =						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:
										lowerCAmelCase__ : Tuple   =						model






				def 				_lowerCamelCase						(		self   : Dict       ,	*a   : Optional[Any]       ,	**a   : Union[str, Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						self.model_type.from_pretrained(*a       ,	**a  )






				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : List[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						self.model
							lowerCAmelCase__ : List[Any]   =						['bias', 'LayerNorm.weight']
							lowerCAmelCase__ : List[str]   =						[
							    {
							        '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:
										lowerCAmelCase__ : Dict   =						Adafactor(
										    a       ,	lr=self.hparams.learning_rate       ,	scale_parameter=a       ,	relative_step=a  )

							else:
										lowerCAmelCase__ : Union[str, Any]   =						AdamW(
										    a       ,	lr=self.hparams.learning_rate       ,	eps=self.hparams.adam_epsilon  )
							lowerCAmelCase__ : Dict   =						optimizer

							lowerCAmelCase__ : Optional[int]   =						self.get_lr_scheduler()

							return [optimizer], [scheduler]






				def 				_lowerCamelCase						(		self   : List[str]       ,	a   : Optional[int]       ,	a   : List[str]  ):






							'''simple docstring'''







							return self.validation_step(a       ,	a  )






				def 				_lowerCamelCase						(		self   : List[str]       ,	a   : Union[str, Any]  ):






							'''simple docstring'''







							return self.validation_end(a  )






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						max(1       ,	self.hparams.gpus  )  # TODO: consider num_tpu_cores
							lowerCAmelCase__ : Optional[Any]   =						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   : str       ,	a   : Tuple  ):






							'''simple docstring'''







							if stage == "test":
										lowerCAmelCase__ : Union[str, Any]   =						len(self.test_dataloader().dataset  )
							else:
										lowerCAmelCase__ : Union[str, Any]   =						self.get_dataloader('train'       ,	self.hparams.train_batch_size       ,	shuffle=a  )
										lowerCAmelCase__ : Union[str, Any]   =						len(self.train_dataloader().dataset  )






				def 				_lowerCamelCase						(		self   : Any       ,	a   : str       ,	a   : int       ,	a   : bool = False  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							return self.train_loader






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : Optional[int]       ,	a   : List[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 				_lowerCamelCase						(		self   : Optional[int]       ,	a   : Dict[str, Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						self.output_dir.joinpath('best_tfmr'  )
							lowerCAmelCase__ : str   =						self.step_count
							self.model.save_pretrained(a  )
							self.tokenizer.save_pretrained(a  )






				@staticmethod
				def 				_lowerCamelCase						(		a   : List[str]       ,	a   : Union[str, 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 	A__				(	pl.Callback      ):






				def 				_lowerCamelCase						(		self   : Tuple       ,	a   : Any       ,	a   : Any  ):






							'''simple docstring'''







							if (
							    trainer.is_global_zero and trainer.global_rank == 0
							):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
										pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class 	A__				(	pl.Callback      ):






				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : List[Any]       ,	a   : int  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : str       ,	a   : pl.Trainer       ,	a   : pl.LightningModule  ):






							'''simple docstring'''







							rank_zero_info('***** Validation results *****'  )
							lowerCAmelCase__ : Any   =						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 				_lowerCamelCase						(		self   : int       ,	a   : pl.Trainer       ,	a   : pl.LightningModule  ):






							'''simple docstring'''







							rank_zero_info('***** Test results *****'  )
							lowerCAmelCase__ : List[Any]   =						trainer.callback_metrics
							# Log and save results to file
							lowerCAmelCase__ : Optional[int]   =						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 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						None:
			#  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=42			,				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 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_=None			,				SCREAMING_SNAKE_CASE_=True			,				SCREAMING_SNAKE_CASE_=None			,				SCREAMING_SNAKE_CASE_=[]			,				SCREAMING_SNAKE_CASE_=None			,				SCREAMING_SNAKE_CASE_=None			,				**SCREAMING_SNAKE_CASE_			,				)							->						Any:
			pl.seed_everything(args.seed	)

			# init model
			lowerCAmelCase__ : Dict   =						Path(model.hparams.output_dir	)
			odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)

			# add custom checkpoints
			if checkpoint_callback is None:
						lowerCAmelCase__ : Any   =						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:
						lowerCAmelCase__ : Optional[int]   =						LoggingCallback()

			lowerCAmelCase__ : Optional[int]   =						{}

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

			if args.gpus > 1:
						lowerCAmelCase__ : List[str]   =						'auto'  # "ddp"
						lowerCAmelCase__ : str   =						'ddp'

			lowerCAmelCase__ : Any   =						args.accumulate_grad_batches
			lowerCAmelCase__ : Tuple   =						None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
			lowerCAmelCase__ : Optional[Any]   =						'auto'

			lowerCAmelCase__ : List[str]   =						pl.Trainer.from_argparse_args(
			    SCREAMING_SNAKE_CASE_			,				weights_summary=SCREAMING_SNAKE_CASE_			,				callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]			,				logger=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

			if args.do_train:
						trainer.fit(SCREAMING_SNAKE_CASE_	)

			return trainer

import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[str]:
			# Construct configuration depending on size
			lowerCAmelCase__ : Tuple   =						{'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ : Optional[int]   =						MODELS[model_size]  # set model hyperparameters
			lowerCAmelCase__ : Optional[int]   =						ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE_			,				n_layer=SCREAMING_SNAKE_CASE_			,				n_head=SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : List[Any]   =						ImageGPTForCausalLM(SCREAMING_SNAKE_CASE_	)

			# Load weights from numpy
			load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			# Save pytorch-model
			lowerCAmelCase__ : Optional[int]   =						pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			lowerCAmelCase__ : Optional[int]   =						pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
			torch.save(model.state_dict()			,				SCREAMING_SNAKE_CASE_	)
			print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
			with open(SCREAMING_SNAKE_CASE_			,				'w'			,				encoding='utf-8'	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
					lowerCamelCase__        =						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--imagegpt_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the TensorFlow checkpoint path.""",
					)
					parser.add_argument(
					    """--model_size""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Size of the model (can be either 'small', 'medium' or 'large').""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					lowerCamelCase__        =						parser.parse_args()
					convert_imagegpt_checkpoint_to_pytorch(
					    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
					)