
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__		:Any			  =   logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase__		: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
lowercase__		:Tuple			  =   {
    "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__		:Union[str, Any]			  =   sorted(arg_to_scheduler.keys())
lowercase__		:List[str]			  =   "{" + ", ".join(arg_to_scheduler_choices) + "}"







class     lowercase		( pl.LightningModule		):




							def __init__(    self       ,A__       ,A__=None       ,A__="base"       ,A__=None       ,A__=None       ,A__=None       ,**A__       ,):
												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__)
												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=A__       ,**A__       ,)
												else:
																	lowercase						     =						config

												lowercase						     =						('''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:
																	lowercase						     =						AutoTokenizer.from_pretrained(
																	    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,cache_dir=A__       ,)
												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=A__       ,)
												else:
																	lowercase						     =						model




							def 			A__				(    self       ,*A__       ,**A__):
												lowercase						     =						self.model_type.from_pretrained(*A__       ,**A__)




							def 			A__				(    self):
												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 			A__				(    self):
												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(
																	    A__       ,lr=self.hparams.learning_rate       ,scale_parameter=A__       ,relative_step=A__)

												else:
																	lowercase						     =						AdamW(
																	    A__       ,lr=self.hparams.learning_rate       ,eps=self.hparams.adam_epsilon)
												lowercase						     =						optimizer

												lowercase						     =						self.get_lr_scheduler()

												return [optimizer], [scheduler]




							def 			A__				(    self       ,A__       ,A__):
												return self.validation_step(A__       ,A__)




							def 			A__				(    self       ,A__):
												return self.validation_end(A__)




							def 			A__				(    self):
												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 			A__				(    self       ,A__):
												if stage == "test":
																	lowercase						     =						len(self.test_dataloader().dataset)
												else:
																	lowercase						     =						self.get_dataloader('''train'''       ,self.hparams.train_batch_size       ,shuffle=A__)
																	lowercase						     =						len(self.train_dataloader().dataset)




							def 			A__				(    self       ,A__       ,A__       ,A__ = False):
												raise NotImplementedError('''You must implement this for your task''')




							def 			A__				(    self):
												return self.train_loader




							def 			A__				(    self):
												return self.get_dataloader('''dev'''       ,self.hparams.eval_batch_size       ,shuffle=A__)




							def 			A__				(    self):
												return self.get_dataloader('''test'''       ,self.hparams.eval_batch_size       ,shuffle=A__)




							def 			A__				(    self       ,A__):
												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 			A__				(    self       ,A__):
												lowercase						     =						self.output_dir.joinpath('''best_tfmr''')
												lowercase						     =						self.step_count
												self.model.save_pretrained(A__)
												self.tokenizer.save_pretrained(A__)





							@staticmethod
							def 			A__				(    A__       ,A__):
												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=str(Path(A__).parent / '''test_run''' / '''cache''')       ,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=3_2       ,type=A__)
												parser.add_argument('''--eval_batch_size'''       ,default=3_2       ,type=A__)
												parser.add_argument('''--adafactor'''       ,action='''store_true''')







class     lowercase		( pl.Callback		):




							def 			A__				(    self       ,A__       ,A__):
												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 			A__				(    self       ,A__       ,A__):
												# print(pl_module.model.rag)
												for name, param in pl_module.model.rag.named_parameters():
																	if param.grad is None:
																						print(A__)







class     lowercase		( pl.Callback		):




							def 			A__				(    self       ,A__       ,A__):
												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(A__)




							def 			A__				(    self       ,A__       ,A__):
												rank_zero_info('''***** Validation results *****''')
												lowercase						     =						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 			A__				(    self       ,A__       ,A__):
												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(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 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					#  To allow all pl args uncomment the following line
					#  parser = pl.Trainer.add_argparse_args(parser)
					parser.add_argument(
					    '''--output_dir'''			,      default=str(Path(lowerCAmelCase__		).parent / '''test_run''' / '''model_checkpoints'''		)			,      type=lowerCAmelCase__			,      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=lowerCAmelCase__			,      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=lowerCAmelCase__		)
					parser.add_argument('''--max_grad_norm'''			,      dest='''gradient_clip_val'''			,      default=1.0			,      type=lowerCAmelCase__			,      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=lowerCAmelCase__			,      default=1			,      help='''Number of updates steps to accumulate before performing a backward/update pass.'''			,      )
					parser.add_argument('''--seed'''			,      type=lowerCAmelCase__			,      default=42			,      help='''random seed for initialization'''		)
					parser.add_argument(
					    '''--data_dir'''			,      default=str(Path(lowerCAmelCase__		).parent / '''test_run''' / '''dummy-train-data'''		)			,      type=lowerCAmelCase__			,      help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''			,      )



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__=None			,      lowerCAmelCase__=True			,      lowerCAmelCase__=[]			,      lowerCAmelCase__=None			,      lowerCAmelCase__=None			,      **lowerCAmelCase__			,      ):
					'''simple docstring'''
					pl.seed_everything(args.seed		)

					# init model
					lowercase						     =						Path(model.hparams.output_dir		)
					odir.mkdir(exist_ok=lowerCAmelCase__		)

					# 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(lowerCAmelCase__		)
					if logging_callback is None:
										lowercase						     =						LoggingCallback()

					lowercase						     =						{}

					if args.fpaa:
										lowercase						     =						16

					if args.gpus > 1:
										lowercase						     =						'''auto'''
										lowercase						     =						'''ddp'''

					lowercase						     =						args.accumulate_grad_batches
					lowercase						     =						None
					lowercase						     =						'''auto'''

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

					if args.do_train:
										trainer.fit(lowerCAmelCase__		)

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






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__		:str			  =   logging.get_logger(__name__)

lowercase__		:Any			  =   {"vocab_file": "sentencepiece.bpe.model"}

lowercase__		:Tuple			  =   {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

lowercase__		:str			  =   {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

lowercase__		:int			  =   "▁"







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Union[str, Any]	 =VOCAB_FILES_NAMES
							lowercase_	:	List[Any]	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	Tuple	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	str	 =['''input_ids''', '''attention_mask''']




							def __init__(    self       ,A__       ,A__="<s>"       ,A__="</s>"       ,A__="</s>"       ,A__="<s>"       ,A__="<unk>"       ,A__="<pad>"       ,A__="<mask>"       ,A__ = None       ,**A__       ,):
												# Mask token behave like a normal word, i.e. include the space before it
												lowercase						     =						AddedToken(A__       ,lstrip=A__       ,rstrip=A__) if isinstance(A__       ,A__) else mask_token

												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=A__       ,eos_token=A__       ,unk_token=A__       ,sep_token=A__       ,cls_token=A__       ,pad_token=A__       ,mask_token=A__       ,sp_model_kwargs=self.sp_model_kwargs       ,**A__       ,)

												lowercase						     =						vocab_file
												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(str(A__))

												lowercase						     =						{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

												lowercase						     =						len(self.sp_model) - 1
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}




							def 			A__				(    self       ,A__       ,A__ = None):

												if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowercase						     =						[self.cls_token_id]
												lowercase						     =						[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = False):
												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A__       ,token_ids_a=A__       ,already_has_special_tokens=A__)

												if token_ids_a is None:
																	return [1] + ([0] * len(A__)) + [1]
												return [1] + ([0] * len(A__)) + [1, 1] + ([0] * len(A__)) + [1]




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]
												lowercase						     =						[self.cls_token_id]

												if token_ids_a is None:
																	return len(cls + token_ids_a + sep) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]




							@property
							def 			A__				(    self):
												return len(self.sp_model)




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def 			A__				(    self       ,A__):
												return self.sp_model.encode(A__       ,out_type=A__)




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						self.sp_model.PieceToId(A__)

												return spm_id if spm_id else self.unk_token_id




							def 			A__				(    self       ,A__):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(A__)




							def 			A__				(    self       ,A__):
												lowercase						     =						[]
												lowercase						     =						''''''
												lowercase						     =						False
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						if not prev_is_special:
																											out_string += " "
																						out_string += self.sp_model.decode(A__) + token
																						lowercase						     =						True
																						lowercase						     =						[]
																	else:
																						current_sub_tokens.append(A__)
																						lowercase						     =						False
												out_string += self.sp_model.decode(A__)
												return out_string.strip()




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d

												# for backward compatibility
												if not hasattr(self       ,'''sp_model_kwargs'''):
																	lowercase						     =						{}

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(self.vocab_file)





							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						os.path.join(
												    A__       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

												if os.path.abspath(self.vocab_file) != os.path.abspath(A__) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file       ,A__)
												elif not os.path.isfile(self.vocab_file):
																	with open(A__       ,'''wb''') as fi:
																						lowercase						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)




