
"""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 import save_json


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      Optional[Any]:


					'''simple docstring'''
					__a:    Any		     =       filter(lambda lowerCamelCase_			: p.requires_grad						,					model.parameters()			)
					__a:    Dict		     =       sum([np.prod(p.size()			) for p in model_parameters]			)
					return params


lowercase		:    Optional[Any]			=       logging.getLogger(__name__)




class  _snake_case	(    pl.Callback							):






							def 				__lowercase	(       self    :      int    ,			snake_case__    :      Optional[Any]    ,			snake_case__    :      str     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       {f"""lr_group_{i}""": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
												pl_module.logger.log_metrics(__lowerCamelCase     )






							@rank_zero_only
							def 				__lowercase	(       self    :      Optional[int]    ,			snake_case__    :      pl.Trainer    ,			snake_case__    :      pl.LightningModule    ,			snake_case__    :      str    ,			snake_case__    :      Any=True     ):






												"""simple docstring"""



												logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
												__a:    str		     =       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
												__a:    Any		     =       Path(pl_module.hparams.output_dir     )
												if type_path == "test":
																	__a:    Optional[Any]		     =       od / """test_results.txt"""
																	__a:    str		     =       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.
																	__a:    Dict		     =       od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
																	__a:    Union[str, Any]		     =       od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
																	results_file.parent.mkdir(exist_ok=__lowerCamelCase     )
																	generations_file.parent.mkdir(exist_ok=__lowerCamelCase     )
												with open(__lowerCamelCase    ,			"""a+"""     ) as writer:
																	for key in sorted(__lowerCamelCase     ):
																						if key in ["log", "progress_bar", "preds"]:
																											continue
																						__a:    int		     =       metrics[key]
																						if isinstance(__lowerCamelCase    ,			torch.Tensor     ):
																											__a:    Optional[int]		     =       val.item()
																						__a:    int		     =       f"""{key}: {val:.6f}\n"""
																						writer.write(__lowerCamelCase     )

												if not save_generations:
																	return

												if "preds" in metrics:
																	__a:    Union[str, Any]		     =       """\n""".join(metrics["""preds"""]     )
																	generations_file.open("""w+"""     ).write(__lowerCamelCase     )






							@rank_zero_only
							def 				__lowercase	(       self    :      Any    ,			snake_case__    :      int    ,			snake_case__    :      List[Any]     ):






												"""simple docstring"""



												try:
																	__a:    List[str]		     =       pl_module.model.model.num_parameters()
												except AttributeError:
																	__a:    List[Any]		     =       pl_module.model.num_parameters()

												__a:    List[Any]		     =       count_trainable_parameters(__lowerCamelCase     )
												# 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 				__lowercase	(       self    :      Optional[Any]    ,			snake_case__    :      pl.Trainer    ,			snake_case__    :      pl.LightningModule     ):






												"""simple docstring"""



												save_json(pl_module.metrics    ,			pl_module.metrics_save_path     )
												return self._write_logs(__lowerCamelCase    ,			__lowerCamelCase    ,			"""test"""     )






							@rank_zero_only
							def 				__lowercase	(       self    :      List[str]    ,			snake_case__    :      pl.Trainer    ,			snake_case__    :      Dict     ):






												"""simple docstring"""



												save_json(pl_module.metrics    ,			pl_module.metrics_save_path     )
												# Uncommenting this will save val generations
												# return self._write_logs(trainer, pl_module, "valid")






def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_=1						,					lowerCamelCase_=False			)  ->      Tuple:


					'''simple docstring'''



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

					__a:    int		     =       ModelCheckpoint(
					    dirpath=lowerCamelCase_						,					filename=lowerCamelCase_						,					monitor=f"""val_{metric}"""						,					mode="""min""" if """loss""" in metric else """max"""						,					save_top_k=lowerCamelCase_						,					)
					return checkpoint_callback




def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_			)  ->      Optional[Any]:


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








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_xlm_roberta import XLMRobertaTokenizer
else:
	UpperCAmelCase_					:		str           = None


UpperCAmelCase_					:		int           = logging.get_logger(__name__)

UpperCAmelCase_					:		List[str]           = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_					:		Any           = {
    '''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'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_					:		Any           = {
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Dict	      =    VOCAB_FILES_NAMES
	snake_case__				:	Optional[int]	      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:	str	      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:	int	      =    ["""input_ids""", """attention_mask"""]
	snake_case__				:	Dict	      =    XLMRobertaTokenizer





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

		super().__init__(
		    __lowerCamelCase					,    tokenizer_file=__lowerCamelCase					,    bos_token=__lowerCamelCase					,    eos_token=__lowerCamelCase					,    sep_token=__lowerCamelCase					,    cls_token=__lowerCamelCase					,    unk_token=__lowerCamelCase					,    pad_token=__lowerCamelCase					,    mask_token=__lowerCamelCase					,    **__lowerCamelCase					,    )

		UpperCamelCase	:Optional[int]		=      vocab_file
		UpperCamelCase	:List[Any]		=      False if not self.vocab_file else True





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):

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





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):
		UpperCamelCase	:str		=      [self.sep_token_id]
		UpperCamelCase	: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]





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Optional[str] = None      ):
		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(__lowerCamelCase      ):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""      )
			return
		UpperCamelCase	:Union[str, Any]		=      os.path.join(
		    __lowerCamelCase					,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

		if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCamelCase      ):
			copyfile(self.vocab_file					,    __lowerCamelCase      )

		return (out_vocab_file,)
