

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


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		"▁"

lowerCamelCase						=		{"vocab_file": "sentencepiece.bpe.model"}

lowerCamelCase						=		{
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

lowerCamelCase						=		{
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}





class  _a							(    _a):
	_a  :  Any				   =							VOCAB_FILES_NAMES
	_a  :  Optional[int]				   =							PRETRAINED_VOCAB_FILES_MAP
	_a  :  Dict				   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  Any				   =							["""input_ids""", """attention_mask"""]




	def __init__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		Optional[int]="<s>"		, _SCREAMING_SNAKE_CASE						:		Optional[int]="</s>"		, _SCREAMING_SNAKE_CASE						:		Optional[int]="</s>"		, _SCREAMING_SNAKE_CASE						:		List[Any]="<s>"		, _SCREAMING_SNAKE_CASE						:		int="<unk>"		, _SCREAMING_SNAKE_CASE						:		List[str]="<pad>"		, _SCREAMING_SNAKE_CASE						:		Optional[int]="<mask>"		, _SCREAMING_SNAKE_CASE						:		List[str] = None		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		, )->	None:
			lowerCAmelCase__       :      List[str]   					= AddedToken(_SCREAMING_SNAKE_CASE		, lstrip=_SCREAMING_SNAKE_CASE		, rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		) else mask_token

			lowerCAmelCase__       :      int   					= {} if sp_model_kwargs is None else sp_model_kwargs

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

			lowerCAmelCase__       :      Tuple   					= spm.SentencePieceProcessor(**self.sp_model_kwargs		)
			self.sp_model.Load(str(_SCREAMING_SNAKE_CASE		)		)
			lowerCAmelCase__       :      Optional[Any]   					= vocab_file

			# Original fairseq vocab and spm vocab must be "aligned":
			# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
			# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
			# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
			# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

			# Mimic fairseq token-to-id alignment for the first 4 token
			lowerCAmelCase__       :      str   					= {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

			# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
			lowerCAmelCase__       :      List[Any]   					= 1

			lowerCAmelCase__       :      List[Any]   					= len(self.sp_model		) + self.fairseq_offset
			lowerCAmelCase__       :      Dict   					= {v: k for k, v in self.fairseq_tokens_to_ids.items()}




	def __getstate__( self						:		Tuple		)->	str:
			lowerCAmelCase__       :      List[Any]   					= self.__dict__.copy()
			lowerCAmelCase__       :      Dict   					= None
			lowerCAmelCase__       :      Dict   					= self.sp_model.serialized_model_proto()
			return state




	def __setstate__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Dict		)->	Dict:
			lowerCAmelCase__       :      Tuple   					= d

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

			lowerCAmelCase__       :      Any   					= spm.SentencePieceProcessor(**self.sp_model_kwargs		)
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		List[str] = None		)->	List[int]:

			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			lowerCAmelCase__       :      int   					= [self.cls_token_id]
			lowerCAmelCase__       :      List[Any]   					= [self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Any = None		, _SCREAMING_SNAKE_CASE						:		Optional[int] = False		)->	List[int]:

			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=_SCREAMING_SNAKE_CASE		, token_ids_a=_SCREAMING_SNAKE_CASE		, already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

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




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Any = None		)->	List[int]:
			lowerCAmelCase__       :      List[Any]   					= [self.sep_token_id]
			lowerCAmelCase__       :      int   					= [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 							UpperCAmelCase__( self						:		str		)->	Optional[int]:
			return len(self.sp_model		) + self.fairseq_offset + 1  # Add the <mask> token




	def 							UpperCAmelCase__( self						:		Any		)->	Optional[Any]:
			lowerCAmelCase__       :      Tuple   					= {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
			vocab.update(self.added_tokens_encoder		)
			return vocab




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Dict		)->	List[str]:
			return self.sp_model.encode(_SCREAMING_SNAKE_CASE		, out_type=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	int:

			if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
			lowerCAmelCase__       :      Optional[Any]   					= self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE		)

			# Need to return unknown token if the SP model returned 0
			return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Tuple		)->	Any:

			if index in self.fairseq_ids_to_tokens:
					return self.fairseq_ids_to_tokens[index]
			return self.sp_model.IdToPiece(index - self.fairseq_offset		)




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	List[Any]:
			lowerCAmelCase__       :      Optional[int]   					= ''.join(_SCREAMING_SNAKE_CASE		).replace(_SCREAMING_SNAKE_CASE		, ''' '''		).strip()
			return out_string




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Any = None		)->	Tuple[str]:

			if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
					return
			lowerCAmelCase__       :      List[str]   					= os.path.join(
			    _SCREAMING_SNAKE_CASE		, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

			if os.path.abspath(self.vocab_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		) and os.path.isfile(self.vocab_file		):
					copyfile(self.vocab_file		, _SCREAMING_SNAKE_CASE		)
			elif not os.path.isfile(self.vocab_file		):
					with open(_SCREAMING_SNAKE_CASE		, '''wb'''		) as fi:
							lowerCAmelCase__       :      Optional[int]   					= self.sp_model.serialized_model_proto()
							fi.write(_SCREAMING_SNAKE_CASE		)

			return (out_vocab_file,)








import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= filter(lambda _a	: p.requires_grad     ,       model.parameters()	)
		lowerCAmelCase__       :      str   					= sum([np.prod(p.size()	) for p in model_parameters]	)
		return params


lowerCamelCase						=		logging.getLogger(__name__)






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		if metric == "rouge2":
				lowerCAmelCase__       :      Optional[int]   					= '''{val_avg_rouge2:.4f}-{step_count}'''
		elif metric == "bleu":
				lowerCAmelCase__       :      Optional[int]   					= '''{val_avg_bleu:.4f}-{step_count}'''
		elif metric == "em":
				lowerCAmelCase__       :      List[Any]   					= '''{val_avg_em:.4f}-{step_count}'''
		else:
				raise NotImplementedError(
				    f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
				    ''' function.'''	)

		lowerCAmelCase__       :      Dict   					= ModelCheckpoint(
		    dirpath=_a     ,       filename=_a     ,       monitor=f'val_{metric}'     ,       mode='''max'''     ,       save_top_k=3     ,       every_n_epochs=1     ,       )
		return checkpoint_callback






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		return EarlyStopping(
		    monitor=f'val_{metric}'     ,       mode='''min''' if '''loss''' in metric else '''max'''     ,       patience=_a     ,       verbose=_a     ,       )





class  _a							(    pl.Callback):




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Any		)->	Optional[int]:
			lowerCAmelCase__       :      Dict   					= {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
			pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		pl.Trainer		, _SCREAMING_SNAKE_CASE						:		pl.LightningModule		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		List[str]=True		)->	None:
			logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'		)
			lowerCAmelCase__       :      List[Any]   					= trainer.callback_metrics
			trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}		)
			# Log results
			lowerCAmelCase__       :      List[Any]   					= Path(pl_module.hparams.output_dir		)
			if type_path == "test":
					lowerCAmelCase__       :      Optional[int]   					= od / '''test_results.txt'''
					lowerCAmelCase__       :      Tuple   					= od / '''test_generations.txt'''
			else:
					# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
					# If people want this it will be easy enough to add back.
					lowerCAmelCase__       :      int   					= od / F'{type_path}_results/{trainer.global_step:05d}.txt'
					lowerCAmelCase__       :      int   					= od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
					results_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
					generations_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
			with open(_SCREAMING_SNAKE_CASE		, '''a+'''		) as writer:
					for key in sorted(_SCREAMING_SNAKE_CASE		):
							if key in ["log", "progress_bar", "preds"]:
									continue
							lowerCAmelCase__       :      Optional[int]   					= metrics[key]
							if isinstance(_SCREAMING_SNAKE_CASE		, torch.Tensor		):
									lowerCAmelCase__       :      List[str]   					= val.item()
							lowerCAmelCase__       :      List[str]   					= F'{key}: {val:.6f}\n'
							writer.write(_SCREAMING_SNAKE_CASE		)

			if not save_generations:
					return

			if "preds" in metrics:
					lowerCAmelCase__       :      Dict   					= '''\n'''.join(metrics['''preds''']		)
					generations_file.open('''w+'''		).write(_SCREAMING_SNAKE_CASE		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Optional[int]:
			try:
					lowerCAmelCase__       :      Tuple   					= pl_module.model.model.num_parameters()
			except AttributeError:
					lowerCAmelCase__       :      Optional[Any]   					= pl_module.model.num_parameters()

			lowerCAmelCase__       :      Dict   					= count_trainable_parameters(_SCREAMING_SNAKE_CASE		)
			# mp stands for million parameters
			trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1E6, '''grad_mp''': n_trainable_pars / 1E6}		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		pl.Trainer		, _SCREAMING_SNAKE_CASE						:		pl.LightningModule		)->	Optional[Any]:
			save_json(pl_module.metrics		, pl_module.metrics_save_path		)
			return self._write_logs(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, '''test'''		)




	@rank_zero_only
	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		pl.Trainer		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	List[Any]:
			save_json(pl_module.metrics		, pl_module.metrics_save_path		)
			# Uncommenting this will save val generations
			# return self._write_logs(trainer, pl_module, "valid")




