
"""simple docstring"""
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	(		UpperCamelCase__   ):


	"""simple docstring"""






	_UpperCAmelCase			=    filter(lambda UpperCamelCase__   : p.requires_grad					,  model.parameters()   )
	_UpperCAmelCase			=    sum([np.prod(p.size()   ) for p in model_parameters]   )
	return params


__magic_name__									=					logging.getLogger(__name__)




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






	if metric == "rouge2":
		_UpperCAmelCase			=    "{val_avg_rouge2:.4f}-{step_count}"
	elif metric == "bleu":
		_UpperCAmelCase			=    "{val_avg_bleu:.4f}-{step_count}"
	elif metric == "em":
		_UpperCAmelCase			=    "{val_avg_em:.4f}-{step_count}"
	elif metric == "loss":
		_UpperCAmelCase			=    "{val_avg_loss:.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."   )

	_UpperCAmelCase			=    ModelCheckpoint(
	    dirpath=snake_case__					,  filename=snake_case__					,  monitor=f"val_{metric}"					,  mode="max"					,  save_top_k=1					,  every_n_epochs=1					,  )

	return checkpoint_callback




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__   ):


	"""simple docstring"""






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



class  _lowerCAmelCase    (    pl.Callback    ):



							def    _a (   self			,    a_			,    a_ )				->				Optional[Any]:
								_UpperCAmelCase			=    {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    _a (   self			,    a_			,    a_			,    a_			,    a_=True )				->				None:
								logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****" )
								_UpperCAmelCase			=    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
								_UpperCAmelCase			=    Path(pl_module.hparams.output_dir )
								if type_path == "test":
									_UpperCAmelCase			=    od / "test_results.txt"
									_UpperCAmelCase			=    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.
									_UpperCAmelCase			=    od / f"{type_path}_results/{trainer.global_step:05d}.txt"
									_UpperCAmelCase			=    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
										_UpperCAmelCase			=    metrics[key]
										if isinstance(SCREAMING_SNAKE_CASE_			,    torch.Tensor ):
											_UpperCAmelCase			=    val.item()
										_UpperCAmelCase			=    f"{key}: {val:.6f}\n"
										writer.write(SCREAMING_SNAKE_CASE_ )

								if not save_generations:
									return

								if "preds" in metrics:
									_UpperCAmelCase			=    "\n".join(metrics["preds"] )
									generations_file.open("w+" ).write(SCREAMING_SNAKE_CASE_ )



							@rank_zero_only
							def    _a (   self			,    a_			,    a_ )				->				Union[str, Any]:
								try:
									_UpperCAmelCase			=    pl_module.model.model.num_parameters()
								except AttributeError:
									_UpperCAmelCase			=    pl_module.model.num_parameters()

								_UpperCAmelCase			=    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    _a (   self			,    a_			,    a_ )				->				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    _a (   self			,    a_			,    a_ )				->				Dict:
								save_json(pl_module.metrics			,    pl_module.metrics_save_path )
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")






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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						__a         =   None

__a         =   logging.get_logger(__name__)

__a         =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__a         =   {
    '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'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__a         =   {
    'moussaKam/mbarthez': 1_0_2_4,
    'moussaKam/barthez': 1_0_2_4,
    'moussaKam/barthez-orangesum-title': 1_0_2_4,
}

__a         =   '▁'
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :int       	=      VOCAB_FILES_NAMES
	a     :Any       	=      PRETRAINED_VOCAB_FILES_MAP
	a     :Optional[int]       	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     :Dict       	=      ['input_ids', 'attention_mask']
	a     :Optional[Any]       	=      BarthezTokenizer
	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=None			,				SCREAMING_SNAKE_CASE_    :     Dict=None			,				SCREAMING_SNAKE_CASE_    :     Dict="<s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]="</s>"			,				SCREAMING_SNAKE_CASE_    :     int="</s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]="<s>"			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="<unk>"			,				SCREAMING_SNAKE_CASE_    :     List[str]="<pad>"			,				SCREAMING_SNAKE_CASE_    :     Any="<mask>"			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				)			->	Any:
					# Mask token behave like a normal word, i.e. include the space before it
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else mask_token

					super().__init__(
					    SCREAMING_SNAKE_CASE_			,				tokenizer_file=SCREAMING_SNAKE_CASE_			,				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_			,				**SCREAMING_SNAKE_CASE_			,				)

					lowercase_	    =   vocab_file
					lowercase_	    =   False if not self.vocab_file else True
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:

					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 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:
					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]




	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			)			->	Tuple[str]:
					if not self.can_save_slow_tokenizer:
									raise ValueError(
									    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
									    '''tokenizer.'''			)

					if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					lowercase_	    =   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_			):
									copyfile(self.vocab_file			,				SCREAMING_SNAKE_CASE_			)

					return (out_vocab_file,)




