


'''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   : Optional[int]             =				logging.getLogger(__name__)

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

lowerCAmelCase   : Dict             =				{
    """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   : int             =				{
    """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        _UpperCamelCase	(    pl.LightningModule):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=None							,      a_="base"							,      a_=None							,      a_=None							,      a_=None							,      **a_							,      )    ->  Optional[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_							)
					lowercase						:		str   					=		0
					lowercase						:		List[str]   					=		Path(self.hparams.output_dir							)
					lowercase						:		List[str]   					=		self.hparams.cache_dir if self.hparams.cache_dir else None
					if config is None:
						lowercase						:		List[str]   					=		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:
						lowercase						:		PretrainedConfig   					=		config

					lowercase						:		List[str]   					=		("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:
						lowercase						:		Tuple   					=		AutoTokenizer.from_pretrained(
						    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,      cache_dir=a_							,      )
					else:
						lowercase						:		PreTrainedTokenizer   					=		tokenizer
					lowercase						:		Tuple   					=		MODEL_MODES[mode]
					if model is None:
						lowercase						:		str   					=		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:
						lowercase						:		int   					=		model

				def 	a__		(     self							,      *a_							,      **a_							)    ->  Optional[int]:
					lowercase						:		str   					=		self.model_type.from_pretrained(*a_							,      **a_							)

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		str   					=		arg_to_scheduler[self.hparams.lr_scheduler]
					lowercase						:		List[str]   					=		get_schedule_func(
					    self.opt							,      num_warmup_steps=self.hparams.warmup_steps							,      num_training_steps=self.total_steps()							)
					lowercase						:		Optional[int]   					=		{"scheduler": scheduler, "interval": "step", "frequency": 1}
					return scheduler

				def 	a__		(     self							)    ->  Any:
					lowercase						:		List[Any]   					=		self.model
					lowercase						:		Union[str, Any]   					=		["bias", "LayerNorm.weight"]
					lowercase						:		str   					=		[
					    {
					        "params": [
					            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay							)
					        ],  # check this named paramters
					        "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:
						lowercase						:		Union[str, Any]   					=		Adafactor(
						    a_							,      lr=self.hparams.learning_rate							,      scale_parameter=a_							,      relative_step=a_							)

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

					lowercase						:		int   					=		self.get_lr_scheduler()

					return [optimizer], [scheduler]

				def 	a__		(     self							,      a_							,      a_							)    ->  Optional[int]:
					return self.validation_step(a_							,      a_							)

				def 	a__		(     self							,      a_							)    ->  List[str]:
					return self.validation_end(a_							)

				def 	a__		(     self							)    ->  int:
					lowercase						:		str   					=		max(1							,      self.hparams.gpus							)  # TODO: consider num_tpu_cores
					lowercase						:		int   					=		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							,      a_							)    ->  Dict:
					if stage == "test":
						lowercase						:		int   					=		len(self.test_dataloader().dataset							)
					else:
						lowercase						:		List[str]   					=		self.get_dataloader("train"							,      self.hparams.train_batch_size							,      shuffle=a_							)
						lowercase						:		Union[str, Any]   					=		len(self.train_dataloader().dataset							)

				def 	a__		(     self							,      a_							,      a_							,      a_ = False							)    ->  Dict:
					raise NotImplementedError("You must implement this for your task"							)

				def 	a__		(     self							)    ->  Optional[Any]:
					return self.train_loader

				def 	a__		(     self							)    ->  Dict:
					return self.get_dataloader("dev"							,      self.hparams.eval_batch_size							,      shuffle=a_							)

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

				def 	a__		(     self							,      a_							)    ->  Union[str, 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 	a__		(     self							,      a_							)    ->  None:
					lowercase						:		int   					=		self.output_dir.joinpath("best_tfmr"							)
					lowercase						:		Union[str, Any]   					=		self.step_count
					self.model.save_pretrained(a_							)
					self.tokenizer.save_pretrained(a_							)


				@staticmethod
				def 	a__		(     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=str(Path(a_							).parent / "test_run" / "cache"							)							,      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=3_2							,      type=a_							)
					parser.add_argument("--eval_batch_size"							,      default=3_2							,      type=a_							)
					parser.add_argument("--adafactor"							,      action="store_true"							)




class        _UpperCamelCase	(    pl.Callback):


				'''simple docstring'''


				def 	a__		(     self							,      a_							,      a_							)    ->  Union[str, Any]:
					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        _UpperCamelCase	(    pl.Callback):


				'''simple docstring'''


				def 	a__		(     self							,      a_							,      a_							)    ->  int:
					# print(pl_module.model.rag)
					for name, param in pl_module.model.rag.named_parameters():
						if param.grad is None:
							print(a_							)




class        _UpperCamelCase	(    pl.Callback):


				'''simple docstring'''


				def 	a__		(     self							,      a_							,      a_							)    ->  Dict:
					lowercase						:		Union[str, Any]   					=		trainer.lr_schedulers[0]["scheduler"]
					lowercase						:		Optional[Any]   					=		{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
					pl_module.logger.log_metrics(a_							)

				def 	a__		(     self							,      a_							,      a_							)    ->  int:
					rank_zero_info("***** Validation results *****"							)
					lowercase						:		str   					=		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 	a__		(     self							,      a_							,      a_							)    ->  Optional[Any]:
					rank_zero_info("***** Test results *****"							)
					lowercase						:		int   					=		trainer.callback_metrics
					# Log and save results to file
					lowercase						:		List[str]   					=		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  (       A							,A							)  ->				None:
	#  To allow all pl args uncomment the following line
	#  parser = pl.Trainer.add_argparse_args(parser)
	parser.add_argument(
	    "--output_dir"							,default=str(Path(A							).parent / "test_run" / "model_checkpoints"							)							,type=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=4_2							,help="random seed for initialization"							)
	parser.add_argument(
	    "--data_dir"							,default=str(Path(A							).parent / "test_run" / "dummy-train-data"							)							,type=A							,help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."							,)






def 	_A  (       A							,A							,A=None							,A=True							,A=[]							,A=None							,A=None							,**A							,)  ->				Union[str, Any]:
	pl.seed_everything(args.seed							)

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

	# add custom checkpoints
	if checkpoint_callback is None:
		lowercase						:		Optional[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(A							)
	if logging_callback is None:
		lowercase						:		Tuple   					=		LoggingCallback()

	lowercase						:		Optional[Any]   					=		{}

	if args.fpaa:
		lowercase						:		Optional[Any]   					=		1_6

	if args.gpus > 1:
		lowercase						:		Union[str, Any]   					=		"auto"
		lowercase						:		List[Any]   					=		"ddp"

	lowercase						:		str   					=		args.accumulate_grad_batches
	lowercase						:		List[str]   					=		None
	lowercase						:		Union[str, Any]   					=		"auto"

	lowercase						:		str   					=		pl.Trainer.from_argparse_args(
	    A							,weights_summary=A							,callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]							,logger=A							,val_check_interval=1							,num_sanity_val_steps=2							,**A							,)

	if args.do_train:
		trainer.fit(A							)

	else:
		print("RAG modeling tests with new set functions successfuly executed!"							)
	return trainer






'''simple docstring'''
import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


lowerCAmelCase   : Dict             =				logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      *a_							,      a_=None							,      a_=None							,      a_=None							,      **a_							)    ->  Any:
					super().__init__(*a_							,      **a_							)
					lowercase						:		Optional[Any]   					=		eval_examples
					lowercase						:		Any   					=		post_process_function
					lowercase						:		Dict   					=		quant_trainer_args
					lowercase						:		List[Any]   					=		1_2_8  # default number of calibration samples

				def 	a__		(     self							,      a_=None							)    ->  List[str]:
					if calib_dataset is None and self.calib_dataset is None:
						raise ValueError("Trainer: calibration requires an calib_dataset."							)
					lowercase						:		Dict   					=		calib_dataset if calib_dataset is not None else self.calib_dataset

					lowercase						:		Optional[int]   					=		self._remove_unused_columns(a_							,      description="Calibration"							)

					return DataLoader(
					    a_							,      batch_size=self.args.eval_batch_size							,      collate_fn=self.data_collator							,      drop_last=self.args.dataloader_drop_last							,      num_workers=self.args.dataloader_num_workers							,      pin_memory=self.args.dataloader_pin_memory							,      shuffle=a_							,      )

				def 	a__		(     self							,      a_=None							)    ->  str:
					lowercase						:		Optional[Any]   					=		self.train_dataset if calib_dataset is None else calib_dataset
					lowercase						:		Optional[Any]   					=		self.get_calib_dataloader(a_							)

					lowercase						:		Union[str, Any]   					=		self.model
					quant_trainer.configure_model(a_							,      self.quant_trainer_args							,      calib=a_							)
					model.eval()
					quant_trainer.enable_calibration(a_							)

					logger.info("***** Running calibration *****"							)
					logger.info(F'''  Num examples = {self.calib_num}'''							)
					logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''							)

					for step, inputs in enumerate(a_							):
						# Prediction step
						lowercase					,		lowercase					,		lowercase						:		Union[str, Any]   					=		self.prediction_step(a_							,      a_							,      prediction_loss_only=a_							)
						if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
							break

					quant_trainer.finish_calibration(a_							,      self.quant_trainer_args							)
					lowercase						:		List[Any]   					=		model

				def 	a__		(     self							,      a_=None							,      a_=None							,      a_=None							,      a_ = "eval"							)    ->  int:
					lowercase						:		List[str]   					=		self.eval_dataset if eval_dataset is None else eval_dataset
					lowercase						:		Union[str, Any]   					=		self.get_eval_dataloader(a_							)
					lowercase						:		Tuple   					=		self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					lowercase						:		List[str]   					=		self.compute_metrics
					lowercase						:		Dict   					=		None
					lowercase						:		int   					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
						lowercase						:		Tuple   					=		eval_loop(
						    a_							,      description="Evaluation"							,      prediction_loss_only=True if compute_metrics is None else None							,      ignore_keys=a_							,      )
					finally:
						lowercase						:		List[Any]   					=		compute_metrics

					if self.post_process_function is not None and self.compute_metrics is not None:
						lowercase						:		Union[str, Any]   					=		self.post_process_function(a_							,      a_							,      output.predictions							)
						lowercase						:		Union[str, Any]   					=		self.compute_metrics(a_							)

						# Prefix all keys with metric_key_prefix + '_'
						for key in list(metrics.keys()							):
							if not key.startswith(F'''{metric_key_prefix}_'''							):
								lowercase						:		Dict   					=		metrics.pop(a_							)

						self.log(a_							)
					else:
						lowercase						:		Tuple   					=		{}

					if self.args.tpu_metrics_debug or self.args.debug:
						# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
						xm.master_print(met.metrics_report()							)

					lowercase						:		int   					=		self.callback_handler.on_evaluate(self.args							,      self.state							,      self.control							,      a_							)
					return metrics

				def 	a__		(     self							,      a_							,      a_							,      a_=None							,      a_ = "test"							)    ->  Optional[int]:
					lowercase						:		Dict   					=		self.get_test_dataloader(a_							)

					# Temporarily disable metric computation, we will do it in the loop here.
					lowercase						:		List[str]   					=		self.compute_metrics
					lowercase						:		List[str]   					=		None
					lowercase						:		int   					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
						lowercase						:		Optional[Any]   					=		eval_loop(
						    a_							,      description="Prediction"							,      prediction_loss_only=True if compute_metrics is None else None							,      ignore_keys=a_							,      )
					finally:
						lowercase						:		int   					=		compute_metrics

					if self.post_process_function is None or self.compute_metrics is None:
						return output

					lowercase						:		List[str]   					=		self.post_process_function(a_							,      a_							,      output.predictions							,      "predict"							)
					lowercase						:		Dict   					=		self.compute_metrics(a_							)

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()							):
						if not key.startswith(F'''{metric_key_prefix}_'''							):
							lowercase						:		Union[str, Any]   					=		metrics.pop(a_							)

					return PredictionOutput(predictions=predictions.predictions							,      label_ids=predictions.label_ids							,      metrics=a_							)


				def 	a__		(     self							,      a_="./"							)    ->  Optional[Any]:
					lowercase						:		str   					=		self.eval_dataset
					lowercase						:		Dict   					=		self.get_eval_dataloader(a_							)

					lowercase						:		Any   					=		next(iter(a_							)							)

					# saving device - to make it consistent
					lowercase						:		List[Any]   					=		torch.device("cuda" if torch.cuda.is_available() else "cpu"							)

					# convert to tuple
					lowercase						:		Tuple   					=		tuple(v.to(a_							) for k, v in batch.items()							)

					logger.info("Converting model to be onnx compatible"							)
					from pytorch_quantization.nn import TensorQuantizer

					lowercase						:		Optional[Any]   					=		True

					lowercase						:		int   					=		self.model.to(a_							)

					model.eval()
					model.float()

					lowercase						:		Union[str, Any]   					=		model.module if hasattr(a_							,      "module"							) else model
					quant_trainer.configure_model(a_							,      self.quant_trainer_args							)

					lowercase						:		List[str]   					=		os.path.join(a_							,      "model.onnx"							)
					logger.info(F'''exporting model to {output_model_file}'''							)

					lowercase						:		Any   					=		{0: "batch_size", 1: "seq_len"}

					torch.onnx.export(
					    a_							,      a_							,      a_							,      export_params=a_							,      opset_version=1_3							,      do_constant_folding=a_							,      input_names=["input_ids", "attention_mask", "token_type_ids"]							,      output_names=["output_start_logits", "output_end_logits"]							,      dynamic_axes={
					        "input_ids": axes,
					        "attention_mask": axes,
					        "token_type_ids": axes,
					        "output_start_logits": axes,
					        "output_end_logits": axes,
					    }							,      verbose=a_							,      )
					logger.info("onnx export finished"							)


