




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						:							str			     =						logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__lowerCAmelCase						:							Union[str, Any]			     =						{
    '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						:							Dict			     =						{
    '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						:							Union[str, Any]			     =						sorted(arg_to_scheduler.keys())
__lowerCAmelCase						:							Optional[int]			     =						'{' + ', '.join(arg_to_scheduler_choices) + '}'




class        snake_case__				(pl.LightningModule  ):
				"""simple docstring"""



				def __init__( self							:	List[str]   , __lowerCamelCase							:	argparse.Namespace   , __lowerCamelCase							:	Tuple=None   , __lowerCamelCase							:	Tuple="base"   , __lowerCamelCase							:	Union[str, Any]=None   , __lowerCamelCase							:	List[str]=None   , __lowerCamelCase							:	Optional[int]=None   , **__lowerCamelCase							:	Any   , )   ->	Optional[int]:
							super().__init__()
							# TODO: move to self.save_hyperparameters()
							# self.save_hyperparameters()
							# can also expand arguments into trainer signature for easier reading

							self.save_hyperparameters(__lowerCamelCase				)
							a       =     0
							a       =     Path(self.hparams.output_dir				)
							a       =     self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
										a       =     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=__lowerCamelCase   , **__lowerCamelCase   , )
							else:
										a       =     config

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

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





				def 				__UpperCAmelCase				( self							:	Dict   , *__lowerCamelCase							:	Any   , **__lowerCamelCase							:	str				)   ->	Tuple:
							a       =     self.model_type.from_pretrained(*__lowerCamelCase   , **__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	int				)   ->	int:
							a       =     arg_to_scheduler[self.hparams.lr_scheduler]
							a       =     get_schedule_func(
							    self.opt   , num_warmup_steps=self.hparams.warmup_steps   , num_training_steps=self.total_steps()				)
							a       =     {"scheduler": scheduler, "interval": "step", "frequency": 1}
							return scheduler





				def 				__UpperCAmelCase				( self							:	str				)   ->	List[str]:
							a       =     self.model
							a       =     ["bias", "LayerNorm.weight"]
							a       =     [
							    {
							        "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:
										a       =     Adafactor(
										    __lowerCamelCase   , lr=self.hparams.learning_rate   , scale_parameter=__lowerCamelCase   , relative_step=__lowerCamelCase				)

							else:
										a       =     AdamW(
										    __lowerCamelCase   , lr=self.hparams.learning_rate   , eps=self.hparams.adam_epsilon				)
							a       =     optimizer

							a       =     self.get_lr_scheduler()

							return [optimizer], [scheduler]





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Optional[int]				)   ->	Union[str, Any]:
							return self.validation_step(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Optional[Any]   , __lowerCamelCase							:	int				)   ->	Dict:
							return self.validation_end(__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	int				)   ->	int:
							a       =     max(1   , self.hparams.gpus				)  # TODO: consider num_tpu_cores
							a       =     self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
							return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





				def 				__UpperCAmelCase				( self							:	str   , __lowerCamelCase							:	Any				)   ->	Tuple:
							if stage == "test":
										a       =     len(self.test_dataloader().dataset				)
							else:
										a       =     self.get_dataloader("train"   , self.hparams.train_batch_size   , shuffle=__lowerCamelCase				)
										a       =     len(self.train_dataloader().dataset				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]   , __lowerCamelCase							:	str   , __lowerCamelCase							:	int   , __lowerCamelCase							:	bool = False				)   ->	Tuple:
							raise NotImplementedError("You must implement this for your task"				)





				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Union[str, Any]:
							return self.train_loader





				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	str:
							return self.get_dataloader("dev"   , self.hparams.eval_batch_size   , shuffle=__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Tuple:
							return self.get_dataloader("test"   , self.hparams.eval_batch_size   , shuffle=__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	str   , __lowerCamelCase							:	int				)   ->	Optional[Any]:
							return os.path.join(
							    self.hparams.data_dir   , "cached_{}_{}_{}".format(
							        __lowerCamelCase   , list(filter(__lowerCamelCase   , self.hparams.model_name_or_path.split("/"				)				)				).pop()   , str(self.hparams.max_seq_length				)   , )   , )





				@pl.utilities.rank_zero_only
				def 				__UpperCAmelCase				( self							:	Optional[Any]   , __lowerCamelCase							:	Dict[str, Any]				)   ->	None:
							a       =     self.output_dir.joinpath("best_tfmr"				)
							a       =     self.step_count
							self.model.save_pretrained(__lowerCamelCase				)
							self.tokenizer.save_pretrained(__lowerCamelCase				)





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




class        snake_case__				(pl.Callback  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	int   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	List[Any]				)   ->	str:
							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        snake_case__				(pl.Callback  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	int				)   ->	Any:
							a       =     trainer.lr_schedulers[0]["scheduler"]
							a       =     {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
							pl_module.logger.log_metrics(__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	int   , __lowerCamelCase							:	pl.Trainer   , __lowerCamelCase							:	pl.LightningModule				)   ->	Union[str, Any]:
							rank_zero_info("***** Validation results *****"				)
							a       =     trainer.callback_metrics
							# Log results
							for key in sorted(__lowerCamelCase				):
										if key not in ["log", "progress_bar"]:
													rank_zero_info("{} = {}\n".format(__lowerCamelCase   , str(metrics[key]				)				)				)





				def 				__UpperCAmelCase				( self							:	str   , __lowerCamelCase							:	pl.Trainer   , __lowerCamelCase							:	pl.LightningModule				)   ->	Optional[Any]:
							rank_zero_info("***** Test results *****"				)
							a       =     trainer.callback_metrics
							# Log and save results to file
							a       =     os.path.join(pl_module.hparams.output_dir   , "test_results.txt"				)
							with open(__lowerCamelCase   , "w"				) as writer:
										for key in sorted(__lowerCamelCase				):
													if key not in ["log", "progress_bar"]:
																rank_zero_info("{} = {}\n".format(__lowerCamelCase   , str(metrics[key]				)				)				)
																writer.write("{} = {}\n".format(__lowerCamelCase   , str(metrics[key]				)				)				)




def      __magic_name__							(					A						:   int,    A						:   str  ):






			'''simple docstring'''







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






def      __magic_name__							(					A						:   BaseTransformer,    A						:   argparse.Namespace,    A						:   Any=None,    A						:   List[Any]=True,    A						:   Optional[int]=None,    A						:   Union[str, Any]=[],    A						:   Optional[int]=None,    A						:   str=None,    **A						:   Union[str, Any],    ):






			'''simple docstring'''







			pl.seed_everything(args.seed  )

			# init model
			a       =     Path(model.hparams.output_dir  )
			odir.mkdir(exist_ok=A  )

			# add custom checkpoints
			if checkpoint_callback is None:
						a       =     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(A  )
			if logging_callback is None:
						a       =     LoggingCallback()

			a       =     {}

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

			if args.gpus > 1:
						a       =     "auto"  # "ddp"
						a       =     "ddp"

			a       =     args.accumulate_grad_batches
			a       =     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
			a       =     "auto"

			a       =     pl.Trainer.from_argparse_args(
			    A,    weights_summary=A,    callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()],    logger=A,    **A,    )

			if args.do_train:
						trainer.fit(A  )

			return trainer












import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


__lowerCAmelCase						:							Optional[int]			     =						'\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

__lowerCAmelCase						:							str			     =						'\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

__lowerCAmelCase						:							Optional[Any]			     =						'\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - \'id\': id of the question-answer pair (see above),\n            - \'answers\': a list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,			_KWARGS_DESCRIPTION  )
class        snake_case__				(datasets.Metric  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Union[str, Any]:
							return datasets.MetricInfo(
							    description=_DESCRIPTION   , citation=_CITATION   , inputs_description=_KWARGS_DESCRIPTION   , features=datasets.Features(
							        {
							            "predictions": {
							                "id": datasets.Value("string"				),
							                "prediction_text": datasets.Value("string"				),
							                "no_answer_probability": datasets.Value("float32"				),
							            },
							            "references": {
							                "id": datasets.Value("string"				),
							                "answers": datasets.features.Sequence(
							                    {"text": datasets.Value("string"				), "answer_start": datasets.Value("int32"				)}				),
							            },
							        }				)   , codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]   , reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]   , )





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Dict=1.0				)   ->	List[str]:
							a       =     {p["id"]: p["no_answer_probability"] for p in predictions}
							a       =     [{"paragraphs": [{"qas": references}]}]
							a       =     {p["id"]: p["prediction_text"] for p in predictions}

							a       =     make_qid_to_has_ans(__lowerCamelCase				)  # maps qid to True/False
							a       =     [k for k, v in qid_to_has_ans.items() if v]
							a       =     [k for k, v in qid_to_has_ans.items() if not v]

							a       ,						a       =     get_raw_scores(__lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase				)

							if has_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "HasAns"				)
							if no_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "NoAns"				)
							find_all_best_thresh(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							return dict(__lowerCamelCase				)






