'''simple docstring'''




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

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


_A										= logging.get_logger(__name__)

_A										= {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_A										= {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}


class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   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							,					)     ->  List[str]:
        lowercase_       :							Tuple  					=       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							,					)

        lowercase_       :							Optional[Any]  					=       3
        lowercase_       :							Any  					=       do_lower_case
        lowercase_       :							List[str]  					=       remove_space
        lowercase_       :							str  					=       keep_accents
        lowercase_       :							List[str]  					=       vocab_file
        lowercase_       :							Optional[Any]  					=       False if not self.vocab_file else True

        try:
             import jieba
        except ModuleNotFoundError as error:
             raise error.__class__(
                 'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
                 'See https://pypi.org/project/jieba/ for installation.'					)
        lowercase_       :							List[str]  					=       jieba
        lowercase_       :							Tuple  					=       str.maketrans(' \n'							,					'\u2582\u2583'					)
   def 		_lowerCamelCase			(self							,					_a							,					_a = None					)     ->  List[int]:
        lowercase_       :							Union[str, Any]  					=       [self.sep_token_id]
        lowercase_       :							Tuple  					=       [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					)     ->  List[int]:
        lowercase_       :							str  					=       [self.sep_token_id]
        lowercase_       :							Dict  					=       [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					)     ->  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(_a					):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
             return
        lowercase_       :							int  					=       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,)
   def 		_lowerCamelCase			(self							,					_a							,					*_a							,					**_a					)     ->  Any:
        lowercase_       :							Optional[int]  					=       [
            ' '.join([x.translate(self.translator					) for x in self.jieba.cut(_a							,					cut_all=_a					)]					)
            for text in batch_text_or_text_pairs
        ]
        return super()._batch_encode_plus(_a							,					*_a							,					**_a					)





   def 		_lowerCamelCase			(self							,					*_a							,					**_a					)     ->  Tuple:
        lowercase_       :							str  					=       super()._decode(*_a							,					**_a					)
        lowercase_       :							Union[str, Any]  					=       text.replace(' '							,					''					).replace('\u2582'							,					' '					).replace('\u2583'							,					'\n'					)
        return text





'''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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Optional[Any]  					=       filter(lambda SCREAMING_SNAKE_CASE_				: p.requires_grad	,							model.parameters()				)
     lowercase_       :							Tuple  					=       sum([np.prod(p.size()				) for p in model_parameters]				)
     return params


_A										= logging.getLogger(__name__)


class    UpperCAmelCase__	(							pl.Callback      ):






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

        if not save_generations:
             return

        if "preds" in metrics:
             lowercase_       :							Optional[Any]  					=       '\n'.join(metrics['preds']					)
             generations_file.open('w+'					).write(_a					)
   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  Tuple:
        try:
             lowercase_       :							List[Any]  					=       pl_module.model.model.num_parameters()
        except AttributeError:
             lowercase_       :							Dict  					=       pl_module.model.num_parameters()

        lowercase_       :							Optional[Any]  					=       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 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  int:
        save_json(pl_module.metrics							,					pl_module.metrics_save_path					)
        return self._write_logs(_a							,					_a							,					'test'					)





   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  List[str]:
        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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=1	,							SCREAMING_SNAKE_CASE_=False				):
     if metric == "rouge2":
          lowercase_       :							Optional[int]  					=       '{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          lowercase_       :							int  					=       '{val_avg_bleu:.4f}-{step_count}'
     elif metric == "loss":
          lowercase_       :							List[Any]  					=       '{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.'				)

     lowercase_       :							Tuple  					=       ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE_	,							filename=SCREAMING_SNAKE_CASE_	,							monitor=f'''val_{metric}'''	,							mode='min' if 'loss' in metric else 'max'	,							save_top_k=SCREAMING_SNAKE_CASE_	,							)
     return checkpoint_callback






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




