





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			:int	 =     logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__lowerCAmelCase			:int	 =     {
    '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			:Union[str, Any]	 =     {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__lowerCAmelCase			:List[str]	 =     sorted(arg_to_scheduler.keys())
__lowerCAmelCase			:Union[str, Any]	 =     '{' + ', '.join(arg_to_scheduler_choices) + '}'






class 							_a(     pl.LightningModule     ):







		def __init__(     self			,    __snake_case			,    __snake_case=None			,    __snake_case="base"			,    __snake_case=None			,    __snake_case=None			,    __snake_case=None			,    **__snake_case			,    )   ->   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(__snake_case					)
							_snake_case							:       str												= 0
							_snake_case							:       int												= Path(self.hparams.output_dir					)
							_snake_case							:       int												= self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
												_snake_case							:       Union[str, Any]												= 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=__snake_case			,    **__snake_case			,    )
							else:
												_snake_case							:       PretrainedConfig												= config

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

							if tokenizer is None:
												_snake_case							:       List[Any]												= AutoTokenizer.from_pretrained(
												    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,    cache_dir=__snake_case			,    )
							else:
												_snake_case							:       PreTrainedTokenizer												= tokenizer
							_snake_case							:       Dict												= MODEL_MODES[mode]
							if model is None:
												_snake_case							:       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=__snake_case			,    )
							else:
												_snake_case							:       List[Any]												= model







		def 							lowercase							(     self			,    *__snake_case			,    **__snake_case					)   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       Any												= self.model_type.from_pretrained(*__snake_case			,    **__snake_case					)







		def 							lowercase							(     self					)   ->   Union[str, Any]:


							'''simple docstring'''

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







		def 							lowercase							(     self					)   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       Any												= self.model
							_snake_case							:       List[Any]												= ["bias", "LayerNorm.weight"]
							_snake_case							:       Tuple												= [
							    {
							        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
							        "weight_decay": self.hparams.weight_decay,
							    },
							    {
							        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)],
							        "weight_decay": 0.0,
							    },
							]
							if self.hparams.adafactor:
												_snake_case							:       Union[str, Any]												= Adafactor(
												    __snake_case			,    lr=self.hparams.learning_rate			,    scale_parameter=__snake_case			,    relative_step=__snake_case					)

							else:
												_snake_case							:       List[Any]												= AdamW(
												    __snake_case			,    lr=self.hparams.learning_rate			,    eps=self.hparams.adam_epsilon					)
							_snake_case							:       str												= optimizer

							_snake_case							:       Union[str, Any]												= self.get_lr_scheduler()

							return [optimizer], [scheduler]







		def 							lowercase							(     self			,    __snake_case			,    __snake_case					)   ->   int:


							'''simple docstring'''

							return self.validation_step(__snake_case			,    __snake_case					)







		def 							lowercase							(     self			,    __snake_case					)   ->   Any:


							'''simple docstring'''

							return self.validation_end(__snake_case					)







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Tuple												= max(1			,    self.hparams.gpus					)  # TODO: consider num_tpu_cores
							_snake_case							:       List[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 							lowercase							(     self			,    __snake_case					)   ->   List[Any]:


							'''simple docstring'''

							if mode == "test":
												_snake_case							:       str												= len(self.test_dataloader().dataset					)
							else:
												_snake_case							:       List[str]												= self.get_dataloader("train"			,    self.hparams.train_batch_size			,    shuffle=__snake_case					)
												_snake_case							:       Any												= len(self.train_dataloader().dataset					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case = False					)   ->   str:


							'''simple docstring'''

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







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


							'''simple docstring'''

							return self.train_loader







		def 							lowercase							(     self					)   ->   Optional[int]:


							'''simple docstring'''

							return self.get_dataloader("dev"			,    self.hparams.eval_batch_size			,    shuffle=__snake_case					)







		def 							lowercase							(     self					)   ->   Optional[int]:


							'''simple docstring'''

							return self.get_dataloader("test"			,    self.hparams.eval_batch_size			,    shuffle=__snake_case					)







		def 							lowercase							(     self			,    __snake_case					)   ->   Dict:


							'''simple docstring'''

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







		@pl.utilities.rank_zero_only
		def 							lowercase							(     self			,    __snake_case					)   ->   None:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= self.output_dir.joinpath("best_tfmr"					)
							_snake_case							:       str												= self.step_count
							self.model.save_pretrained(__snake_case					)
							self.tokenizer.save_pretrained(__snake_case					)







		@staticmethod
		def 							lowercase							(     __snake_case			,    __snake_case					)   ->   Any:


							'''simple docstring'''

							parser.add_argument(
							    "--model_name_or_path"			,    default=__snake_case			,    type=__snake_case			,    required=__snake_case			,    help="Path to pretrained model or model identifier from huggingface.co/models"			,    )
							parser.add_argument(
							    "--config_name"			,    default=""			,    type=__snake_case			,    help="Pretrained config name or path if not the same as model_name"					)
							parser.add_argument(
							    "--tokenizer_name"			,    default=__snake_case			,    type=__snake_case			,    help="Pretrained tokenizer name or path if not the same as model_name"			,    )
							parser.add_argument(
							    "--cache_dir"			,    default=""			,    type=__snake_case			,    help="Where do you want to store the pre-trained models downloaded from huggingface.co"			,    )
							parser.add_argument(
							    "--encoder_layerdrop"			,    type=__snake_case			,    help="Encoder layer dropout probability (Optional). Goes into model.config"			,    )
							parser.add_argument(
							    "--decoder_layerdrop"			,    type=__snake_case			,    help="Decoder layer dropout probability (Optional). Goes into model.config"			,    )
							parser.add_argument(
							    "--dropout"			,    type=__snake_case			,    help="Dropout probability (Optional). Goes into model.config"			,    )
							parser.add_argument(
							    "--attention_dropout"			,    type=__snake_case			,    help="Attention dropout probability (Optional). Goes into model.config"			,    )
							parser.add_argument("--learning_rate"			,    default=5E-5			,    type=__snake_case			,    help="The initial learning rate for Adam."					)
							parser.add_argument(
							    "--lr_scheduler"			,    default="linear"			,    choices=__snake_case			,    metavar=__snake_case			,    type=__snake_case			,    help="Learning rate scheduler"			,    )
							parser.add_argument("--weight_decay"			,    default=0.0			,    type=__snake_case			,    help="Weight decay if we apply some."					)
							parser.add_argument("--adam_epsilon"			,    default=1E-8			,    type=__snake_case			,    help="Epsilon for Adam optimizer."					)
							parser.add_argument("--warmup_steps"			,    default=0			,    type=__snake_case			,    help="Linear warmup over warmup_steps."					)
							parser.add_argument("--num_workers"			,    default=4			,    type=__snake_case			,    help="kwarg passed to DataLoader"					)
							parser.add_argument("--num_train_epochs"			,    dest="max_epochs"			,    default=3			,    type=__snake_case					)
							parser.add_argument("--train_batch_size"			,    default=3_2			,    type=__snake_case					)
							parser.add_argument("--eval_batch_size"			,    default=3_2			,    type=__snake_case					)
							parser.add_argument("--adafactor"			,    action="store_true"					)






class 							_a(     pl.Callback     ):







		def 							lowercase							(     self			,    __snake_case			,    __snake_case					)   ->   int:


							'''simple docstring'''

							_snake_case							:       List[Any]												= trainer.lr_schedulers[0]["scheduler"]
							_snake_case							:       Any												= {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
							pl_module.logger.log_metrics(__snake_case					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case					)   ->   Union[str, Any]:


							'''simple docstring'''

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







		def 							lowercase							(     self			,    __snake_case			,    __snake_case					)   ->   Any:


							'''simple docstring'''

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







def  A					(				UpperCAmelCase	,	UpperCAmelCase					):
					#  To allow all pl args uncomment the following line
					#  parser = pl.Trainer.add_argparse_args(parser)
					parser.add_argument(
					    "--output_dir"	,	default=UpperCAmelCase	,	type=UpperCAmelCase	,	required=UpperCAmelCase	,	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=UpperCAmelCase	,	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=UpperCAmelCase					)
					parser.add_argument("--max_grad_norm"	,	dest="gradient_clip_val"	,	default=1.0	,	type=UpperCAmelCase	,	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=UpperCAmelCase	,	default=1	,	help="Number of updates steps to accumulate before performing a backward/update pass."	,	)
					parser.add_argument("--seed"	,	type=UpperCAmelCase	,	default=42	,	help="random seed for initialization"					)
					parser.add_argument(
					    "--data_dir"	,	default=UpperCAmelCase	,	type=UpperCAmelCase	,	required=UpperCAmelCase	,	help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."	,	)







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

					# init model
					_snake_case							:       List[Any]												= Path(model.hparams.output_dir					)
					odir.mkdir(exist_ok=UpperCAmelCase					)

					# add custom checkpoints
					if checkpoint_callback is None:
										_snake_case							:       List[str]												= 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(UpperCAmelCase					)
					if logging_callback is None:
										_snake_case							:       int												= LoggingCallback()

					_snake_case							:       Optional[Any]												= {}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										_snake_case							:       Optional[int]												= 16
										_snake_case							:       Tuple												= args.fpaa_opt_level

					if args.gpus > 1:
										_snake_case							:       List[Any]												= "ddp"

					_snake_case							:       Dict												= args.accumulate_grad_batches
					_snake_case							:       Optional[Any]												= extra_train_kwargs.get("accelerator"	,	UpperCAmelCase					)
					_snake_case							:       List[Any]												= extra_train_kwargs.get("profiler"	,	UpperCAmelCase					)

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

					if args.do_train:
										trainer.fit(UpperCAmelCase					)

					return trainer






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase			:Optional[int]	 =     {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase			:Dict	 =     ['MobileNetV1FeatureExtractor']
							__lowerCAmelCase			:List[Any]	 =     ['MobileNetV1ImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase			:Optional[int]	 =     [
							    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MobileNetV1ForImageClassification',
							    'MobileNetV1Model',
							    'MobileNetV1PreTrainedModel',
							    'load_tf_weights_in_mobilenet_v1',
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

							__lowerCAmelCase			:Optional[int]	 =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)