






'''simple docstring'''







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			:    str					  =logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_A			:    Dict					  ={
    '''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			:    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
}
_A			:    Optional[int]					  =sorted(arg_to_scheduler.keys())
_A			:    Optional[Any]					  ='''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''

class _lowercase			(       pl.LightningModule	):




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

														lowerCamelCase__      :			int 						= ("""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:
																					lowerCamelCase__      :			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:
																					lowerCamelCase__      :			PreTrainedTokenizer 						= tokenizer
														lowerCamelCase__      :			List[Any] 						= MODEL_MODES[mode]
														if model is None:
																					lowerCamelCase__      :			Tuple 						= 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:
																					lowerCamelCase__      :			int 						= model




							def 	lowerCamelCase_       (							self:       Any    ,							*UpperCamelCase__:       Tuple    ,							**UpperCamelCase__:       Tuple  ):
														lowerCamelCase__      :			int 						= self.model_type.from_pretrained(*UpperCamelCase__    ,							**UpperCamelCase__  )




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




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			Any 						= self.model
														lowerCamelCase__      :			int 						= ["""bias""", """LayerNorm.weight"""]
														lowerCamelCase__      :			Union[str, Any] 						= [
														    {
														        """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__      :			Union[str, Any] 						= Adafactor(
																					    UpperCamelCase__    ,							lr=self.hparams.learning_rate    ,							scale_parameter=UpperCamelCase__    ,							relative_step=UpperCamelCase__  )

														else:
																					lowerCamelCase__      :			Any 						= AdamW(
																					    UpperCamelCase__    ,							lr=self.hparams.learning_rate    ,							eps=self.hparams.adam_epsilon  )
														lowerCamelCase__      :			Tuple 						= optimizer

														lowerCamelCase__      :			Optional[int] 						= self.get_lr_scheduler()

														return [optimizer], [scheduler]




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       List[Any]  ):
														return self.validation_step(UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       List[str]  ):
														return self.validation_end(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			Any 						= max(1    ,							self.hparams.gpus  )  # TODO: consider num_tpu_cores
														lowerCamelCase__      :			List[Any] 						= self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
														return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       List[Any]  ):
														if stage == "test":
																					lowerCamelCase__      :			Union[str, Any] 						= len(self.test_dataloader().dataset  )
														else:
																					lowerCamelCase__      :			Optional[Any] 						= self.get_dataloader("""train"""    ,							self.hparams.train_batch_size    ,							shuffle=UpperCamelCase__  )
																					lowerCamelCase__      :			Dict 						= len(self.train_dataloader().dataset  )




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




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														return self.train_loader




							def 	lowerCamelCase_       (							self:       Any  ):
														return self.get_dataloader("""dev"""    ,							self.hparams.eval_batch_size    ,							shuffle=UpperCamelCase__  )




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




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       Union[str, 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 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Dict[str, Any]  ):
														lowerCamelCase__      :			Optional[int] 						= self.output_dir.joinpath("""best_tfmr"""  )
														lowerCamelCase__      :			Optional[Any] 						= self.step_count
														self.model.save_pretrained(UpperCamelCase__  )
														self.tokenizer.save_pretrained(UpperCamelCase__  )




							@staticmethod
							def 	lowerCamelCase_       (							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]  ):
														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 _lowercase			(       pl.Callback	):




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       int  ):
														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 _lowercase			(       pl.Callback	):




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[Any]  ):
														# print(pl_module.model.rag)
														for name, param in pl_module.model.rag.named_parameters():
																					if param.grad is None:
																												print(UpperCamelCase__  )



class _lowercase			(       pl.Callback	):




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




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       pl.Trainer    ,							UpperCamelCase__:       pl.LightningModule  ):
														rank_zero_info("""***** Validation results *****"""  )
														lowerCamelCase__      :			Dict 						= 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 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       pl.Trainer    ,							UpperCamelCase__:       pl.LightningModule  ):
														rank_zero_info("""***** Test results *****"""  )
														lowerCamelCase__      :			Dict 						= trainer.callback_metrics
														# Log and save results to file
														lowerCamelCase__      :			Optional[Any] 						= 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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> None:
							#  To allow all pl args uncomment the following line
							#  parser = pl.Trainer.add_argparse_args(parser)
							parser.add_argument(
							    """--output_dir"""       ,    default=str(Path(UpperCamelCase   ).parent / """test_run""" / """model_checkpoints"""   )       ,    type=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=str(Path(UpperCamelCase   ).parent / """test_run""" / """dummy-train-data"""   )       ,    type=UpperCamelCase       ,    help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""       ,    )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    UpperCamelCase=True       ,    UpperCamelCase=[]       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    **UpperCamelCase       ,    )   -> Any:
							pl.seed_everything(args.seed   )

							# init model
							lowerCamelCase__      :			Optional[int] 						= Path(model.hparams.output_dir   )
							odir.mkdir(exist_ok=UpperCamelCase   )

							# add custom checkpoints
							if checkpoint_callback is None:
														lowerCamelCase__      :			Optional[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(UpperCamelCase   )
							if logging_callback is None:
														lowerCamelCase__      :			Optional[Any] 						= LoggingCallback()

							lowerCamelCase__      :			Optional[Any] 						= {}

							if args.fpaa:
														lowerCamelCase__      :			Tuple 						= 16

							if args.gpus > 1:
														lowerCamelCase__      :			List[str] 						= """auto"""
														lowerCamelCase__      :			Any 						= """ddp"""

							lowerCamelCase__      :			List[str] 						= args.accumulate_grad_batches
							lowerCamelCase__      :			Optional[int] 						= None
							lowerCamelCase__      :			int 						= """auto"""

							lowerCamelCase__      :			Dict 						= pl.Trainer.from_argparse_args(
							    UpperCamelCase       ,    weights_summary=UpperCamelCase       ,    callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]       ,    logger=UpperCamelCase       ,    val_check_interval=1       ,    num_sanity_val_steps=2       ,    **UpperCamelCase       ,    )

							if args.do_train:
														trainer.fit(UpperCamelCase   )

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









'''simple docstring'''







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


_A			:    Optional[Any]					  =logging.get_logger(__name__)

_A			:    Dict					  ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A			:    Tuple					  ={
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

_A			:    List[Any]					  ={
    '''gpt-neox-20b''': 2_048,
}

class _lowercase			(       _lowercase	):
							a          = VOCAB_FILES_NAMES
							a          = PRETRAINED_VOCAB_FILES_MAP
							a          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a          = ["""input_ids""", """attention_mask"""]




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]=None    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       Tuple=None    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Union[str, Any]="<|endoftext|>"    ,							UpperCamelCase__:       Tuple=False    ,							**UpperCamelCase__:       str    ,							):
														super().__init__(
														    UpperCamelCase__    ,							UpperCamelCase__    ,							tokenizer_file=UpperCamelCase__    ,							unk_token=UpperCamelCase__    ,							bos_token=UpperCamelCase__    ,							eos_token=UpperCamelCase__    ,							add_prefix_space=UpperCamelCase__    ,							**UpperCamelCase__    ,							)

														lowerCamelCase__      :			Dict 						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
														if pre_tok_state.get("""add_prefix_space"""    ,							UpperCamelCase__  ) != add_prefix_space:
																					lowerCamelCase__      :			Any 						= getattr(UpperCamelCase__    ,							pre_tok_state.pop("""type"""  )  )
																					lowerCamelCase__      :			Dict 						= add_prefix_space
																					lowerCamelCase__      :			Optional[int] 						= pre_tok_class(**UpperCamelCase__  )

														lowerCamelCase__      :			Dict 						= add_prefix_space




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[str] = None  ):
														lowerCamelCase__      :			Optional[Any] 						= self._tokenizer.model.save(UpperCamelCase__    ,							name=UpperCamelCase__  )
														return tuple(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       "Conversation"  ):
														lowerCamelCase__      :			str 						= []
														for is_user, text in conversation.iter_texts():
																					input_ids.extend(self.encode(UpperCamelCase__    ,							add_special_tokens=UpperCamelCase__  ) + [self.eos_token_id]  )

														if len(UpperCamelCase__  ) > self.model_max_length:
																					lowerCamelCase__      :			int 						= input_ids[-self.model_max_length :]
														return input_ids

