



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


snake_case__					:     List[str]    		=   logging.getLogger(__name__)

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

snake_case__					:     Optional[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
snake_case__					:     Optional[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
}
snake_case__					:     List[Any]    		=   sorted(arg_to_scheduler.keys())
snake_case__					:     str    		=   """{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class   _A   ( pl.LightningModule	):


						'''simple docstring'''





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

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



						def     _snake_case	(	self	:							List[str]							,      *lowerCamelCase	:							Union[str, Any]							,      **lowerCamelCase	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.model_type.from_pretrained(*lowerCamelCase							,      **lowerCamelCase	)



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			self.model
							__lowercase		       =			["bias", "LayerNorm.weight"]
							__lowercase		       =			[
							    {
							        "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		       =			Adafactor(
								    lowerCamelCase							,      lr=self.hparams.learning_rate							,      scale_parameter=lowerCamelCase							,      relative_step=lowerCamelCase	)

							else:
								__lowercase		       =			AdamW(
								    lowerCamelCase							,      lr=self.hparams.learning_rate							,      eps=self.hparams.adam_epsilon	)
							__lowercase		       =			optimizer

							__lowercase		       =			self.get_lr_scheduler()

							return [optimizer], [scheduler]



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



							return self.validation_step(lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							Tuple							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



							return self.validation_end(lowerCamelCase	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			max(1							,      self.hparams.gpus	)  # TODO: consider num_tpu_cores
							__lowercase		       =			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	:							Union[str, Any]							,      lowerCamelCase	:							List[Any]	):






							'''simple docstring'''



							if stage == "test":
								__lowercase		       =			len(self.test_dataloader().dataset	)
							else:
								__lowercase		       =			self.get_dataloader("train"							,      self.hparams.train_batch_size							,      shuffle=lowerCamelCase	)
								__lowercase		       =			len(self.train_dataloader().dataset	)



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							str							,      lowerCamelCase	:							int							,      lowerCamelCase	:							bool = False	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							return self.train_loader



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



							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     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							Dict[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			self.output_dir.joinpath("best_tfmr"	)
							__lowercase		       =			self.step_count
							self.model.save_pretrained(lowerCamelCase	)
							self.tokenizer.save_pretrained(lowerCamelCase	)



						@staticmethod
						def     _snake_case	(	lowerCamelCase	:							Any							,      lowerCamelCase	:							List[str]	):






							'''simple docstring'''



							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=str(Path(lowerCamelCase	).parent / "test_run" / "cache"	)							,      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   _A   ( pl.Callback	):


						'''simple docstring'''





						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							Optional[Any]							,      lowerCamelCase	:							List[Any]	):






							'''simple docstring'''



							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   _A   ( pl.Callback	):


						'''simple docstring'''





						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							List[str]	):






							'''simple docstring'''



							for name, param in pl_module.model.rag.named_parameters():
								if param.grad is None:
									print(lowerCamelCase	)

class   _A   ( pl.Callback	):


						'''simple docstring'''





						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							List[str]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							pl.Trainer							,      lowerCamelCase	:							pl.LightningModule	):






							'''simple docstring'''



							rank_zero_info("***** Validation results *****"	)
							__lowercase		       =			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     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							pl.Trainer							,      lowerCamelCase	:							pl.LightningModule	):






							'''simple docstring'''



							rank_zero_info("***** Test results *****"	)
							__lowercase		       =			trainer.callback_metrics
							# Log and save results to file
							__lowercase		       =			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    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	#  To allow all pl args uncomment the following line
	#  parser = pl.Trainer.add_argparse_args(parser)
	parser.add_argument(
	    "--output_dir"  ,	default=str(Path(_SCREAMING_SNAKE_CASE   ).parent / "test_run" / "model_checkpoints"   )  ,	type=_SCREAMING_SNAKE_CASE  ,	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=_SCREAMING_SNAKE_CASE  ,	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=_SCREAMING_SNAKE_CASE   )
	parser.add_argument("--max_grad_norm"  ,	dest="gradient_clip_val"  ,	default=1.0  ,	type=_SCREAMING_SNAKE_CASE  ,	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=_SCREAMING_SNAKE_CASE  ,	default=1  ,	help="Number of updates steps to accumulate before performing a backward/update pass."  ,	)
	parser.add_argument("--seed"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=4_2  ,	help="random seed for initialization"   )
	parser.add_argument(
	    "--data_dir"  ,	default=str(Path(_SCREAMING_SNAKE_CASE   ).parent / "test_run" / "dummy-train-data"   )  ,	type=_SCREAMING_SNAKE_CASE  ,	help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."  ,	)



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

	# init model
	__lowercase		       =			Path(model.hparams.output_dir   )
	odir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE   )

	# add custom checkpoints
	if checkpoint_callback is None:
		__lowercase		       =			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(_SCREAMING_SNAKE_CASE   )
	if logging_callback is None:
		__lowercase		       =			LoggingCallback()

	__lowercase		       =			{}

	if args.fpaa:
		__lowercase		       =			1_6

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

	__lowercase		       =			args.accumulate_grad_batches
	__lowercase		       =			None
	__lowercase		       =			"auto"

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

	if args.do_train:
		trainer.fit(_SCREAMING_SNAKE_CASE   )

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








from __future__ import annotations

from typing import Any

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							Union[str, Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							__lowercase		       =			num_of_nodes
							__lowercase		       =			[]
							__lowercase		       =			{}



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							self.m_edges.append([u_node, v_node, weight]	)



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if self.m_component[u_node] == u_node:
								return u_node
							return self.find_component(self.m_component[u_node]	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if self.m_component[u_node] != u_node:
								for k in self.m_component:
									__lowercase		       =			self.find_component(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							list[int]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if component_size[u_node] <= component_size[v_node]:
								__lowercase		       =			v_node
								component_size[v_node] += component_size[u_node]
								self.set_component(lowerCamelCase	)

							elif component_size[u_node] >= component_size[v_node]:
								__lowercase		       =			self.find_component(lowerCamelCase	)
								component_size[u_node] += component_size[v_node]
								self.set_component(lowerCamelCase	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase		       =			[]
							__lowercase		       =			0

							__lowercase		       =			[-1] * self.m_num_of_nodes

							# A list of components (initialized to all of the nodes)
							for node in range(self.m_num_of_nodes	):
								self.m_component.update({node: node}	)
								component_size.append(1	)

							__lowercase		       =			self.m_num_of_nodes

							while num_of_components > 1:
								for edge in self.m_edges:
									__lowercase       ,    __lowercase       ,    __lowercase		       =			edge

									__lowercase		       =			self.m_component[u]
									__lowercase		       =			self.m_component[v]

									if u_component != v_component:

										for component in (u_component, v_component):
											if (
											    minimum_weight_edge[component] == -1
											    or minimum_weight_edge[component][2] > w
											):
												__lowercase		       =			[u, v, w]

								for edge in minimum_weight_edge:
									if isinstance(lowerCamelCase							,      lowerCamelCase	):
										__lowercase       ,    __lowercase       ,    __lowercase		       =			edge

										__lowercase		       =			self.m_component[u]
										__lowercase		       =			self.m_component[v]

										if u_component != v_component:
											mst_weight += w
											self.union(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
											print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""	)
											num_of_components -= 1

								__lowercase		       =			[-1] * self.m_num_of_nodes
							print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""	)



def    snake_case_				(    ):
	pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()



