





"""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


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 							_lowerCamelCase   (							pl.LightningModule			):


						def __init__(self ,				__a ,				__a=None ,				__a="base" ,				__a=None ,				__a=None ,				__a=None ,				**__a ,				)			-> Any:
								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 ,				*__a ,				**__a		)			-> str:
								UpperCamelCase =						self.model_type.from_pretrained(*__a ,				**__a		)


						def      snake_case_ (self		)			-> Optional[int]:
								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]:
								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 ,				__a ,				__a		)			-> int:
								return self.validation_step(__a ,				__a		)


						def      snake_case_ (self ,				__a		)			-> List[str]:
								return self.validation_end(__a		)


						def      snake_case_ (self		)			-> int:
								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 ,				__a		)			-> Union[str, Any]:
								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 ,				__a ,				__a ,				__a = False		)			-> List[Any]:
								raise NotImplementedError("You must implement this for your task"		)


						def      snake_case_ (self		)			-> List[Any]:
								return self.train_loader


						def      snake_case_ (self		)			-> Optional[int]:
								return self.get_dataloader("dev" ,				self.hparams.eval_batch_size ,				shuffle=__a		)


						def      snake_case_ (self		)			-> str:
								return self.get_dataloader("test" ,				self.hparams.eval_batch_size ,				shuffle=__a		)


						def      snake_case_ (self ,				__a		)			-> Optional[Any]:
								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 ,				__a		)			-> None:
								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 ,				__a		)			-> Union[str, Any]:
								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 ,				__a ,				__a		)			-> List[Any]:
								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 ,				__a ,				__a		)			-> Any:
								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 ,				__a ,				__a		)			-> Optional[Any]:
								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__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		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    a__       (       _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	,						):




		"""simple docstring"""


		pl.seed_everything(args.seed     )

		# init model
		UpperCamelCase =						Path(model.hparams.output_dir     )
		odir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE     )

		# 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(_SCREAMING_SNAKE_CASE     )
		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"	,						_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						extra_train_kwargs.get("profiler"	,						_SCREAMING_SNAKE_CASE     )

		UpperCamelCase =						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













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__        =      {
    '''configuration_openai''': ['''OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenAIGPTConfig'''],
    '''tokenization_openai''': ['''OpenAIGPTTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      ['''OpenAIGPTTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''OpenAIGPTDoubleHeadsModel''',
						    '''OpenAIGPTForSequenceClassification''',
						    '''OpenAIGPTLMHeadModel''',
						    '''OpenAIGPTModel''',
						    '''OpenAIGPTPreTrainedModel''',
						    '''load_tf_weights_in_openai_gpt''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFOpenAIGPTDoubleHeadsModel''',
						    '''TFOpenAIGPTForSequenceClassification''',
						    '''TFOpenAIGPTLMHeadModel''',
						    '''TFOpenAIGPTMainLayer''',
						    '''TFOpenAIGPTModel''',
						    '''TFOpenAIGPTPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
						from .tokenization_openai import OpenAIGPTTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_openai_fast import OpenAIGPTTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_openai import (
												    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    OpenAIGPTDoubleHeadsModel,
												    OpenAIGPTForSequenceClassification,
												    OpenAIGPTLMHeadModel,
												    OpenAIGPTModel,
												    OpenAIGPTPreTrainedModel,
												    load_tf_weights_in_openai_gpt,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_openai import (
												    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFOpenAIGPTDoubleHeadsModel,
												    TFOpenAIGPTForSequenceClassification,
												    TFOpenAIGPTLMHeadModel,
												    TFOpenAIGPTMainLayer,
												    TFOpenAIGPTModel,
												    TFOpenAIGPTPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase__        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






