


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


SCREAMING_SNAKE_CASE__     					=			logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

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



class 						_UpperCAmelCase      (     pl.LightningModule							):






		def __init__(						self				:     int   ,  UpperCAmelCase				:     argparse.Namespace   ,  UpperCAmelCase				:     List[Any]=None   ,  UpperCAmelCase				:     Dict="base"   ,  UpperCAmelCase				:     Dict=None   ,  UpperCAmelCase				:     str=None   ,  UpperCAmelCase				:     int=None   ,  **UpperCAmelCase				:     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(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						0
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						Path(self.hparams.output_dir)
				SCREAMING_SNAKE_CASE_				:int				     =						self.hparams.cache_dir if self.hparams.cache_dir else None
				if config is None:
						SCREAMING_SNAKE_CASE_				:Optional[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=UpperCAmelCase   ,  **UpperCAmelCase   ,  )
				else:
						SCREAMING_SNAKE_CASE_				:PretrainedConfig				     =						config

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

				if tokenizer is None:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						AutoTokenizer.from_pretrained(
						    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,  cache_dir=UpperCAmelCase   ,  )
				else:
						SCREAMING_SNAKE_CASE_				:PreTrainedTokenizer				     =						tokenizer
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						MODEL_MODES[mode]
				if model is None:
						SCREAMING_SNAKE_CASE_				:Optional[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=UpperCAmelCase   ,  )
				else:
						SCREAMING_SNAKE_CASE_				:int				     =						model






		def _snake_case		(						self				:     Dict   ,  *UpperCAmelCase				:     List[Any]   ,  **UpperCAmelCase				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.model_type.from_pretrained(*UpperCAmelCase   ,  **UpperCAmelCase)






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






		def _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.model
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						["bias", "LayerNorm.weight"]
				SCREAMING_SNAKE_CASE_				:Tuple				     =						[
				    {
				        "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:
						SCREAMING_SNAKE_CASE_				:str				     =						Adafactor(
						    UpperCAmelCase   ,  lr=self.hparams.learning_rate   ,  scale_parameter=UpperCAmelCase   ,  relative_step=UpperCAmelCase)

				else:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						AdamW(
						    UpperCAmelCase   ,  lr=self.hparams.learning_rate   ,  eps=self.hparams.adam_epsilon)
				SCREAMING_SNAKE_CASE_				:str				     =						optimizer

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

				return [optimizer], [scheduler]






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Optional[Any]):
				return self.validation_step(UpperCAmelCase   ,  UpperCAmelCase)






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     List[Any]):
				return self.validation_end(UpperCAmelCase)






		def _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						max(1   ,  self.hparams.gpus)  # TODO: consider num_tpu_cores
				SCREAMING_SNAKE_CASE_				:Optional[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 _snake_case		(						self				:     Optional[int]   ,  UpperCAmelCase				:     Optional[Any]):
				if stage == "test":
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						len(self.test_dataloader().dataset)
				else:
						SCREAMING_SNAKE_CASE_				:str				     =						self.get_dataloader("train"   ,  self.hparams.train_batch_size   ,  shuffle=UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						len(self.train_dataloader().dataset)






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     bool = False):
				raise NotImplementedError("You must implement this for your task")






		def _snake_case		(						self				:     Dict):
				return self.train_loader






		def _snake_case		(						self				:     Optional[Any]):
				return self.get_dataloader("dev"   ,  self.hparams.eval_batch_size   ,  shuffle=UpperCAmelCase)






		def _snake_case		(						self				:     Dict):
				return self.get_dataloader("test"   ,  self.hparams.eval_batch_size   ,  shuffle=UpperCAmelCase)






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






		@pl.utilities.rank_zero_only
		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     Dict[str, Any]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.output_dir.joinpath("best_tfmr")
				SCREAMING_SNAKE_CASE_				:int				     =						self.step_count
				self.model.save_pretrained(UpperCAmelCase)
				self.tokenizer.save_pretrained(UpperCAmelCase)







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




class 						_UpperCAmelCase      (     pl.Callback							):






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     Optional[int]   ,  UpperCAmelCase				:     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 						_UpperCAmelCase      (     pl.Callback							):






		def _snake_case		(						self				:     Optional[int]   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     Any):
				# print(pl_module.model.rag)
				for name, param in pl_module.model.rag.named_parameters():
						if param.grad is None:
								print(UpperCAmelCase)




class 						_UpperCAmelCase      (     pl.Callback							):






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						trainer.lr_schedulers[0]["scheduler"]
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
				pl_module.logger.log_metrics(UpperCAmelCase)






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     pl.Trainer   ,  UpperCAmelCase				:     pl.LightningModule):
				rank_zero_info("***** Validation results *****")
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						trainer.callback_metrics
				# Log results
				for key in sorted(UpperCAmelCase):
						if key not in ["log", "progress_bar"]:
								rank_zero_info("{} = {}\n".format(UpperCAmelCase   ,  str(metrics[key])))







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




def     lowercase	(    a     , a ):



		'''simple docstring'''




		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=42     , 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     lowercase	(    a     , a     , a=None     , a=True     , a=[]     , a=None     , a=None     , **a     , ):



		'''simple docstring'''




		pl.seed_everything(args.seed )

		# init model
		SCREAMING_SNAKE_CASE_				:List[str]				     =						Path(model.hparams.output_dir )
		odir.mkdir(exist_ok=a )

		# add custom checkpoints
		if checkpoint_callback is None:
				SCREAMING_SNAKE_CASE_				:int				     =						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:
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						LoggingCallback()

		SCREAMING_SNAKE_CASE_				:Any				     =						{}

		if args.fpaa:
				SCREAMING_SNAKE_CASE_				:Tuple				     =						16

		if args.gpus > 1:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						"auto"
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						"ddp"

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

		SCREAMING_SNAKE_CASE_				:List[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










from __future__ import annotations

import math


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[Any]				     =						u
		for i in range(1     , a ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						temp * (u - i)
		return temp


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						int(input("enter the numbers of values: " ) )
		SCREAMING_SNAKE_CASE_				:list[list[float]]				     =						[]
		for _ in range(a ):
				y.append([] )
		for i in range(a ):
				for j in range(a ):
						y[i].append(a )
						SCREAMING_SNAKE_CASE_				:Any				     =						0

		print("enter the values of parameters in a list: " )
		SCREAMING_SNAKE_CASE_				:Dict				     =						list(map(a     , input().split() ) )

		print("enter the values of corresponding parameters: " )
		for i in range(a ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						float(input() )

		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						int(input("enter the value to interpolate: " ) )
		SCREAMING_SNAKE_CASE_				:str				     =						(value - x[0]) / (x[1] - x[0])

		# for calculating forward difference table

		for i in range(1     , a ):
				for j in range(n - i ):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						y[j + 1][i - 1] - y[j][i - 1]

		SCREAMING_SNAKE_CASE_				:Tuple				     =						y[0][0]
		for i in range(1     , a ):
				summ += (ucal(a     , a ) * y[0][i]) / math.factorial(a )

		print(F"the value at {value} is {summ}" )


if __name__ == "__main__":
		main()






