





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       =	logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_lowerCamelCase       =	{
    '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       =	{
    '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       =	sorted(arg_to_scheduler.keys())
_lowerCamelCase       =	'{' + ', '.join(arg_to_scheduler_choices) + '}'




class      UpperCamelCase_				(   pl.LightningModule			):


							def __init__(							self			:str							, __A			:argparse.Namespace							, __A			:Optional[Any]=None							, __A			:Dict="base"							, __A			:Optional[Any]=None							, __A			:Tuple=None							, __A			:Dict=None							, **__A			:List[Any]							, )      ->      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(__A						)
									SCREAMING_SNAKE_CASE__								=					0
									SCREAMING_SNAKE_CASE__								=					Path(self.hparams.output_dir						)
									SCREAMING_SNAKE_CASE__								=					self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
											SCREAMING_SNAKE_CASE__								=					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:
											SCREAMING_SNAKE_CASE__								=					config

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


							def    _snake_case     (							self			:int							, *__A			:int							, **__A			:Optional[int]						)      ->      Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.model_type.from_pretrained(*__A							, **__A						)


							def    _snake_case     (							self			:Optional[int]						)      ->      List[Any]:



									"""simple docstring"""



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


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



									"""simple docstring"""



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

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

									SCREAMING_SNAKE_CASE__								=					self.get_lr_scheduler()

									return [optimizer], [scheduler]


							def    _snake_case     (							self			:str							, __A			:Optional[Any]							, __A			:int						)      ->      Dict:



									"""simple docstring"""



									return self.validation_step(__A							, __A						)


							def    _snake_case     (							self			:List[Any]							, __A			:Dict						)      ->      int:



									"""simple docstring"""



									return self.validation_end(__A						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					max(1							, self.hparams.gpus						)  # TODO: consider num_tpu_cores
									SCREAMING_SNAKE_CASE__								=					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			:Dict							, __A			:Dict						)      ->      Dict:



									"""simple docstring"""



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


							def    _snake_case     (							self			:int							, __A			:str							, __A			:int							, __A			:bool = False						)      ->      Union[str, Any]:



									"""simple docstring"""



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


							def    _snake_case     (							self			:Dict						)      ->      int:



									"""simple docstring"""



									return self.train_loader


							def    _snake_case     (							self			:str						)      ->      List[Any]:



									"""simple docstring"""



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


							def    _snake_case     (							self			:List[Any]						)      ->      Optional[int]:



									"""simple docstring"""



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


							def    _snake_case     (							self			:int							, __A			:Optional[int]						)      ->      Union[str, Any]:



									"""simple docstring"""



									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    _snake_case     (							self			:Optional[int]							, __A			:Dict[str, Any]						)      ->      None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.output_dir.joinpath("""best_tfmr"""						)
									SCREAMING_SNAKE_CASE__								=					self.step_count
									self.model.save_pretrained(__A						)
									self.tokenizer.save_pretrained(__A						)


							@staticmethod
							def    _snake_case     (							__A			:Any							, __A			:Tuple						)      ->      str:



									"""simple docstring"""



									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=""""""							, 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      UpperCamelCase_				(   pl.Callback			):


							def    _snake_case     (							self			:Tuple							, __A			:str							, __A			:int						)      ->      str:



									"""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      UpperCamelCase_				(   pl.Callback			):


							def    _snake_case     (							self			:Any							, __A			:int							, __A			:int						)      ->      List[Any]:



									"""simple docstring"""



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


							def    _snake_case     (							self			:Optional[Any]							, __A			:pl.Trainer							, __A			:pl.LightningModule						)      ->      Tuple:



									"""simple docstring"""



									rank_zero_info("""***** Validation results *****"""						)
									SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							self			:Optional[Any]							, __A			:pl.Trainer							, __A			:pl.LightningModule						)      ->      List[str]:



									"""simple docstring"""



									rank_zero_info("""***** Test results *****"""						)
									SCREAMING_SNAKE_CASE__								=					trainer.callback_metrics
									# Log and save results to file
									SCREAMING_SNAKE_CASE__								=					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  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Dict    ,      UpperCamelCase__:							int			):
		#  To allow all pl args uncomment the following line
		#  parser = pl.Trainer.add_argparse_args(parser)
		parser.add_argument(
		    """--output_dir"""    ,      default=UpperCamelCase__    ,      type=UpperCamelCase__    ,      required=UpperCamelCase__    ,      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=UpperCamelCase__    ,      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=UpperCamelCase__			)
		parser.add_argument("""--max_grad_norm"""    ,      dest="""gradient_clip_val"""    ,      default=1.0    ,      type=UpperCamelCase__    ,      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=UpperCamelCase__    ,      default=1    ,      help="""Number of updates steps to accumulate before performing a backward/update pass."""    ,      )
		parser.add_argument("""--seed"""    ,      type=UpperCamelCase__    ,      default=42    ,      help="""random seed for initialization"""			)
		parser.add_argument(
		    """--data_dir"""    ,      default=UpperCamelCase__    ,      type=UpperCamelCase__    ,      required=UpperCamelCase__    ,      help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""    ,      )






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							BaseTransformer    ,      UpperCamelCase__:							argparse.Namespace    ,      UpperCamelCase__:							Optional[Any]=None    ,      UpperCamelCase__:							Dict=True    ,      UpperCamelCase__:							List[str]=None    ,      UpperCamelCase__:							Dict=[]    ,      UpperCamelCase__:							Optional[int]=None    ,      UpperCamelCase__:							Union[str, Any]=None    ,      **UpperCamelCase__:							Tuple    ,      ):
		pl.seed_everything(args.seed			)

		# init model
		SCREAMING_SNAKE_CASE__								=					Path(model.hparams.output_dir			)
		odir.mkdir(exist_ok=UpperCamelCase__			)

		# add custom checkpoints
		if checkpoint_callback is None:
				SCREAMING_SNAKE_CASE__								=					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(UpperCamelCase__			)
		if logging_callback is None:
				SCREAMING_SNAKE_CASE__								=					LoggingCallback()

		SCREAMING_SNAKE_CASE__								=					{}

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

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

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

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

		if args.do_train:
				trainer.fit(UpperCamelCase__			)

		return trainer






from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

_lowerCamelCase       =	{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							dict    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					{start}
		SCREAMING_SNAKE_CASE__								=					[start]
		SCREAMING_SNAKE_CASE__								=					Queue()
		queue.put(UpperCamelCase__			)
		while not queue.empty():
				SCREAMING_SNAKE_CASE__								=					queue.get()
				for w in graph[v]:
						if w not in explored:
								explored.add(UpperCamelCase__			)
								result.append(UpperCamelCase__			)
								queue.put(UpperCamelCase__			)
		return result






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							dict    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					{start}
		SCREAMING_SNAKE_CASE__								=					[start]
		SCREAMING_SNAKE_CASE__								=					deque([start]			)
		while queue:
				SCREAMING_SNAKE_CASE__								=					queue.popleft()
				for child in graph[v]:
						if child not in visited:
								visited.add(UpperCamelCase__			)
								result.append(UpperCamelCase__			)
								queue.append(UpperCamelCase__			)
		return result






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					f'''from __main__ import G, {name}'''
		SCREAMING_SNAKE_CASE__								=					10_000
		SCREAMING_SNAKE_CASE__								=					timeit(f'''{name}(G, \'A\')'''    ,      setup=UpperCamelCase__    ,      number=UpperCamelCase__			)
		print(f'''{name:<35} finished {number} runs in {res:.5f} seconds'''			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	benchmark_function('breadth_first_search')
	benchmark_function('breadth_first_search_with_deque')
	# breadth_first_search                finished 10000 runs in 0.20999 seconds
	# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds