


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


_A     =  logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_A     =  {
    "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
_A     =  {
    "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
}
_A     =  sorted(arg_to_scheduler.keys())
_A     =  "{" + ", ".join(arg_to_scheduler_choices) + "}"





class 			_lowerCAmelCase      (			pl.LightningModule ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase="base"       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       **_UpperCamelCase       ,       )       ->   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(__A  )
							lowerCAmelCase_      			=			0
							lowerCAmelCase_      			=			Path(self.hparams.output_dir  )
							lowerCAmelCase_      			=			self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
										lowerCAmelCase_      			=			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_      			=			config

							lowerCAmelCase_      			=			("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_      			=			AutoTokenizer.from_pretrained(
										    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,       cache_dir=__A       ,       )
							else:
										lowerCAmelCase_      			=			tokenizer
							lowerCAmelCase_      			=			MODEL_MODES[mode]
							if model is None:
										lowerCAmelCase_      			=			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_      			=			model




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			self.model_type.from_pretrained(*__A       ,       **__A  )




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




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			self.model
							lowerCAmelCase_      			=			["bias", "LayerNorm.weight"]
							lowerCAmelCase_      			=			[
							    {
							        "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:
										lowerCAmelCase_      			=			Adafactor(
										    __A       ,       lr=self.hparams.learning_rate       ,       scale_parameter=__A       ,       relative_step=__A  )

							else:
										lowerCAmelCase_      			=			AdamW(
										    __A       ,       lr=self.hparams.learning_rate       ,       eps=self.hparams.adam_epsilon  )
							lowerCAmelCase_      			=			optimizer

							lowerCAmelCase_      			=			self.get_lr_scheduler()

							return [optimizer], [scheduler]




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							return self.validation_step(__A       ,       __A  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Optional[int]:
							return self.validation_end(__A  )




				def   __a		(      self  )       ->   int:
							lowerCAmelCase_      			=			max(1       ,       self.hparams.gpus  )  # TODO: consider num_tpu_cores
							lowerCAmelCase_      			=			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       ,       _UpperCamelCase  )       ->   Optional[int]:
							if stage == "test":
										lowerCAmelCase_      			=			len(self.test_dataloader().dataset  )
							else:
										lowerCAmelCase_      			=			self.get_dataloader("train"       ,       self.hparams.train_batch_size       ,       shuffle=__A  )
										lowerCAmelCase_      			=			len(self.train_dataloader().dataset  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = False  )       ->   Tuple:
							raise NotImplementedError("You must implement this for your task"  )




				def   __a		(      self  )       ->   str:
							return self.train_loader




				def   __a		(      self  )       ->   int:
							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       ,       _UpperCamelCase  )       ->   List[str]:
							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       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self.output_dir.joinpath("best_tfmr"  )
							lowerCAmelCase_      			=			self.step_count
							self.model.save_pretrained(__A  )
							self.tokenizer.save_pretrained(__A  )




				@staticmethod
				def   __a		(      _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
							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=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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   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 			_lowerCAmelCase      (			pl.Callback ):




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[Any]:
							# print(pl_module.model.rag)
							for name, param in pl_module.model.rag.named_parameters():
										if param.grad is None:
													print(__A  )





class 			_lowerCAmelCase      (			pl.Callback ):




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			trainer.lr_schedulers[0]["scheduler"]
							lowerCAmelCase_      			=			{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
							pl_module.logger.log_metrics(__A  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[Any]:
							rank_zero_info("***** Validation results *****"  )
							lowerCAmelCase_      			=			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       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[Any]:
							rank_zero_info("***** Test results *****"  )
							lowerCAmelCase_      			=			trainer.callback_metrics
							# Log and save results to file
							lowerCAmelCase_      			=			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__       (       __lowerCAmelCase						:						int					,							__lowerCAmelCase						:						List[Any]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						Dict=None					,							__lowerCAmelCase						:						Optional[Any]=True					,							__lowerCAmelCase						:						Dict=[]					,							__lowerCAmelCase						:						Dict=None					,							__lowerCAmelCase						:						str=None					,							**__lowerCAmelCase						:						Any					,							):




			"""simple docstring"""


			pl.seed_everything(args.seed       )

			# init model
			lowerCAmelCase_      			=			Path(model.hparams.output_dir       )
			odir.mkdir(exist_ok=_lowercase       )

			# add custom checkpoints
			if checkpoint_callback is None:
						lowerCAmelCase_      			=			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(_lowercase       )
			if logging_callback is None:
						lowerCAmelCase_      			=			LoggingCallback()

			lowerCAmelCase_      			=			{}

			if args.fpaa:
						lowerCAmelCase_      			=			16

			if args.gpus > 1:
						lowerCAmelCase_      			=			"auto"
						lowerCAmelCase_      			=			"ddp"

			lowerCAmelCase_      			=			args.accumulate_grad_batches
			lowerCAmelCase_      			=			None
			lowerCAmelCase_      			=			"auto"

			lowerCAmelCase_      			=			pl.Trainer.from_argparse_args(
			    _lowercase					,							weights_summary=_lowercase					,							callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]					,							logger=_lowercase					,							val_check_interval=1					,							num_sanity_val_steps=2					,							**_lowercase					,							)

			if args.do_train:
						trainer.fit(_lowercase       )

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





from string import ascii_lowercase, ascii_uppercase






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			if not sentence:
						return ""
			lowerCAmelCase_      			=			dict(zip(__lowerCAmelCase					,							__lowerCAmelCase       )       )
			return lower_to_upper.get(sentence[0]					,							sentence[0]       ) + sentence[1:]


if __name__ == "__main__":
			from doctest import testmod

			testmod()



