




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


lowercase_	:				Dict  				= logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowercase_	:				Union[str, 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
lowercase_	:				Union[str, Any]  				= {
    """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
}
lowercase_	:				int  				= sorted(arg_to_scheduler.keys())
lowercase_	:				str  				= """{""" + """, """.join(arg_to_scheduler_choices) + """}"""






class     __lowerCAmelCase (       pl.LightningModule ):







			def __init__(       self    :			Tuple    ,    snake_case__    :			Union[str, Any]    ,    snake_case__    :			List[Any]=None    ,    snake_case__    :			List[Any]="base"    ,    snake_case__    :			List[Any]=None    ,    snake_case__    :			List[str]=None    ,    snake_case__    :			List[Any]=None    ,    **snake_case__    :			List[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(snake_case__      )
									_UpperCAmelCase													=     0
									_UpperCAmelCase													=     Path(self.hparams.output_dir      )
									_UpperCAmelCase													=     self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
															_UpperCAmelCase													=     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=snake_case__    ,    **snake_case__    ,    )
									else:
															_UpperCAmelCase													=     config

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

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







			def UpperCamelCase			(       self    :			Optional[int]    ,    *snake_case__    :			Optional[Any]    ,    **snake_case__    :			Optional[int]      ):




									"""simple docstring"""


									_UpperCAmelCase													=     self.model_type.from_pretrained(*snake_case__    ,    **snake_case__      )







			def UpperCamelCase			(       self    :			int      ):




									"""simple docstring"""


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







			def UpperCamelCase			(       self    :			str      ):




									"""simple docstring"""


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

									else:
															_UpperCAmelCase													=     AdamW(
															    snake_case__    ,    lr=self.hparams.learning_rate    ,    eps=self.hparams.adam_epsilon      )
									_UpperCAmelCase													=     optimizer

									_UpperCAmelCase													=     self.get_lr_scheduler()

									return [optimizer], [scheduler]







			def UpperCamelCase			(       self    :			int    ,    snake_case__    :			Any    ,    snake_case__    :			Union[str, Any]      ):




									"""simple docstring"""


									return self.validation_step(snake_case__    ,    snake_case__      )







			def UpperCamelCase			(       self    :			Optional[Any]    ,    snake_case__    :			Dict      ):




									"""simple docstring"""


									return self.validation_end(snake_case__      )







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




									"""simple docstring"""


									_UpperCAmelCase													=     max(1    ,    self.hparams.gpus      )  # TODO: consider num_tpu_cores
									_UpperCAmelCase													=     self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
									return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







			def UpperCamelCase			(       self    :			Dict    ,    snake_case__    :			str      ):




									"""simple docstring"""


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







			def UpperCamelCase			(       self    :			Any    ,    snake_case__    :			Union[str, Any]    ,    snake_case__    :			Tuple    ,    snake_case__    :			Tuple = False      ):




									"""simple docstring"""


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







			def UpperCamelCase			(       self    :			Optional[int]      ):




									"""simple docstring"""


									return self.train_loader







			def UpperCamelCase			(       self    :			Dict      ):




									"""simple docstring"""


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







			def UpperCamelCase			(       self    :			List[str]      ):




									"""simple docstring"""


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







			def UpperCamelCase			(       self    :			Dict    ,    snake_case__    :			Union[str, Any]      ):




									"""simple docstring"""


									return os.path.join(
									    self.hparams.data_dir    ,    "cached_{}_{}_{}".format(
									        snake_case__    ,    list(filter(snake_case__    ,    self.hparams.model_name_or_path.split("/"      )      )      ).pop()    ,    str(self.hparams.max_seq_length      )    ,    )    ,    )







			@pl.utilities.rank_zero_only
			def UpperCamelCase			(       self    :			Tuple    ,    snake_case__    :			Tuple      ):




									"""simple docstring"""


									_UpperCAmelCase													=     self.output_dir.joinpath("best_tfmr"      )
									_UpperCAmelCase													=     self.step_count
									self.model.save_pretrained(snake_case__      )
									self.tokenizer.save_pretrained(snake_case__      )







			@staticmethod
			def UpperCamelCase			(       snake_case__    :			List[Any]    ,    snake_case__    :			List[Any]      ):




									"""simple docstring"""


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






class     __lowerCAmelCase (       pl.Callback ):







			def UpperCamelCase			(       self    :			List[str]    ,    snake_case__    :			Any    ,    snake_case__    :			Tuple      ):




									"""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     __lowerCAmelCase (       pl.Callback ):







			def UpperCamelCase			(       self    :			int    ,    snake_case__    :			List[Any]    ,    snake_case__    :			Union[str, Any]      ):




									"""simple docstring"""


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






class     __lowerCAmelCase (       pl.Callback ):







			def UpperCamelCase			(       self    :			Optional[int]    ,    snake_case__    :			Tuple    ,    snake_case__    :			Optional[int]      ):




									"""simple docstring"""


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







			def UpperCamelCase			(       self    :			Dict    ,    snake_case__    :			List[str]    ,    snake_case__    :			Any      ):




									"""simple docstring"""


									rank_zero_info("***** Validation results *****"      )
									_UpperCAmelCase													=     trainer.callback_metrics
									# Log results
									for key in sorted(snake_case__      ):
															if key not in ["log", "progress_bar"]:
																					rank_zero_info("{} = {}\n".format(snake_case__    ,    str(metrics[key]      )      )      )







			def UpperCamelCase			(       self    :			Any    ,    snake_case__    :			Tuple    ,    snake_case__    :			Optional[int]      ):




									"""simple docstring"""


									rank_zero_info("***** Test results *****"      )
									_UpperCAmelCase													=     trainer.callback_metrics
									# Log and save results to file
									_UpperCAmelCase													=     os.path.join(pl_module.hparams.output_dir    ,    "test_results.txt"      )
									with open(snake_case__    ,    "w"      ) as writer:
															for key in sorted(snake_case__      ):
																					if key not in ["log", "progress_bar"]:
																											rank_zero_info("{} = {}\n".format(snake_case__    ,    str(metrics[key]      )      )      )
																											writer.write("{} = {}\n".format(snake_case__    ,    str(metrics[key]      )      )      )




def 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_						):

						'''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 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_	,    snake_case_=None	,    snake_case_=True	,    snake_case_=[]	,    snake_case_=None	,    snake_case_=None	,    **snake_case_	,    ):

						'''simple docstring'''


						pl.seed_everything(args.seed						)

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

						# add custom checkpoints
						if checkpoint_callback is None:
												_UpperCAmelCase													=     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:
												_UpperCAmelCase													=     LoggingCallback()

						_UpperCAmelCase													=     {}

						if args.fpaa:
												_UpperCAmelCase													=     16

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

						_UpperCAmelCase													=     args.accumulate_grad_batches
						_UpperCAmelCase													=     None
						_UpperCAmelCase													=     """auto"""

						_UpperCAmelCase													=     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









'''simple docstring'''




from itertools import count



def       __lowerCamelCase  (					_lowercase = 5_0		)      ->			int:
							UpperCAmelCase     :		Any							     =		[1] * min_block_length

							for n in count(_lowercase		):
														fill_count_functions.append(1		)

														for block_length in range(_lowercase	,				n + 1		):
																					for block_start in range(n - block_length		):
																												fill_count_functions[n] += fill_count_functions[
																												    n - block_start - block_length - 1
																												]

																					fill_count_functions[n] += 1

														if fill_count_functions[n] > 1_0_0_0_0_0_0:
																					break

							return n


if __name__ == "__main__":
							print(F'''{solution() = }''')

