

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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      Union[str, Any]:





    '''simple docstring'''






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


lowercase_       		=  logging.getLogger(__name__)
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Dict      )							->      Optional[int]:





    '''simple docstring'''






    if metric == "rouge2":
        A__          =   '{val_avg_rouge2:.4f}-{step_count}'
    elif metric == "bleu":
        A__          =   '{val_avg_bleu:.4f}-{step_count}'
    elif metric == "em":
        A__          =   '{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.'      )

    A__          =   ModelCheckpoint(
        dirpath=snake_case__    ,			filename=snake_case__    ,			monitor=f'val_{metric}'    ,			mode='max'    ,			save_top_k=3    ,			every_n_epochs=1    ,			)
    return checkpoint_callback
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      List[str]:





    '''simple docstring'''






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


class  A						(							pl.Callback ):







    """simple docstring"""





    def 						snake_case__     (      self :       Tuple,lowercase_ :       str,lowercase_ :       str		)->    Any:





        '''simple docstring'''







        A__          =   {F'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
        pl_module.logger.log_metrics(_a		)


    @rank_zero_only
    def 						snake_case__     (      self :       Tuple,lowercase_ :       Optional[int],lowercase_ :       Tuple,lowercase_ :       int,lowercase_ :       Tuple=True		)->    str:





        '''simple docstring'''







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

        if not save_generations:
            return

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


    @rank_zero_only
    def 						snake_case__     (      self :       Any,lowercase_ :       List[str],lowercase_ :       Tuple		)->    Union[str, Any]:





        '''simple docstring'''







        try:
            A__          =   pl_module.model.model.num_parameters()
        except AttributeError:
            A__          =   pl_module.model.num_parameters()

        A__          =   count_trainable_parameters(_a		)
        # 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 						snake_case__     (      self :       Any,lowercase_ :       Optional[Any],lowercase_ :       Any		)->    Tuple:





        '''simple docstring'''







        save_json(pl_module.metrics,pl_module.metrics_save_path		)
        return self._write_logs(_a,_a,'test'		)


    @rank_zero_only
    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       Dict,lowercase_ :       List[Any]		)->    Union[str, Any]:





        '''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")







"""simple docstring"""




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_xlnet import XLNetTokenizer
else:
					lowerCAmelCase							:			int     							=							None


lowerCAmelCase							:			Tuple     							=							logging.get_logger(__name__)

lowerCAmelCase							:			Tuple     							=							{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase							:			Union[str, Any]     							=							{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase							:			Optional[int]     							=							{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

lowerCAmelCase							:			Union[str, Any]     							=							"""▁"""

# Segments (not really needed)
lowerCAmelCase							:			str     							=							0
lowerCAmelCase							:			Optional[int]     							=							1
lowerCAmelCase							:			Tuple     							=							2
lowerCAmelCase							:			Optional[Any]     							=							3
lowerCAmelCase							:			List[Any]     							=							4






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						VOCAB_FILES_NAMES
				__UpperCamelCase			  =						PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase			  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase			  =						"left"
				__UpperCamelCase			  =						XLNetTokenizer





				def __init__(       self		, _a=None		, _a=None		, _a=False		, _a=True		, _a=False		, _a="<s>"		, _a="</s>"		, _a="<unk>"		, _a="<sep>"		, _a="<pad>"		, _a="<cls>"		, _a="<mask>"		, _a=["<eop>", "<eod>"]		, **_a		, ):





							"""simple docstring"""
							# Mask token behave like a normal word, i.e. include the space before it
							lowerCamelCase			      =					AddedToken(_a		, lstrip=_a		, rstrip=_a ) if isinstance(_a		, _a ) else mask_token

							super().__init__(
							    vocab_file=_a		, tokenizer_file=_a		, do_lower_case=_a		, remove_space=_a		, keep_accents=_a		, bos_token=_a		, eos_token=_a		, unk_token=_a		, sep_token=_a		, pad_token=_a		, cls_token=_a		, mask_token=_a		, additional_special_tokens=_a		, **_a		, )

							lowerCamelCase			      =					3
							lowerCamelCase			      =					do_lower_case
							lowerCamelCase			      =					remove_space
							lowerCamelCase			      =					keep_accents
							lowerCamelCase			      =					vocab_file
							lowerCamelCase			      =					False if not self.vocab_file else True





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					[self.sep_token_id]
							lowerCamelCase			      =					[self.cls_token_id]
							if token_ids_a is None:
										return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					[self.sep_token_id]
							lowerCamelCase			      =					[2]

							if token_ids_a is None:
										return len(token_ids_a + sep ) * [0] + cls_segment_id
							return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							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(_a ):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
										return
							lowerCamelCase			      =					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 ):
										copyfile(self.vocab_file		, _a )

							return (out_vocab_file,)



