



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 (    _lowerCamelCase     ):
			A				:       Optional[Any]														=    filter(lambda _lowerCamelCase     : p.requires_grad			,     model.parameters()     )
			A				:       int														=    sum([np.prod(p.size()     ) for p in model_parameters]     )
			return params


__SCREAMING_SNAKE_CASE     =	logging.getLogger(__name__)

class 						lowerCamelCase_     (			pl.Callback    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      Any						,						__lowerCamelCase   :      Tuple			)   ->					str:
									A				:       Dict														=    {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       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      pl.Trainer						,						__lowerCamelCase   :      pl.LightningModule						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Optional[Any]=True			)   ->					None:
									logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""			)
									A				:       int														=    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				:       Optional[int]														=    Path(pl_module.hparams.output_dir			)
									if type_path == "test":
												A				:       str														=    od / "test_results.txt"
												A				:       List[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				:       List[Any]														=    od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
												A				:       Optional[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				:       List[Any]														=    metrics[key]
															if isinstance(__lowerCamelCase						,						torch.Tensor			):
																		A				:       List[str]														=    val.item()
															A				:       Union[str, Any]														=    F"""{key}: {val:.6f}\n"""
															writer.write(__lowerCamelCase			)

									if not save_generations:
												return

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







						@rank_zero_only
						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      Any						,						__lowerCamelCase   :      Dict			)   ->					Optional[Any]:
									try:
												A				:       Dict														=    pl_module.model.model.num_parameters()
									except AttributeError:
												A				:       int														=    pl_module.model.num_parameters()

									A				:       str														=    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       SCREAMING_SNAKE_CASE__		(						self   :      List[str]						,						__lowerCamelCase   :      pl.Trainer						,						__lowerCamelCase   :      pl.LightningModule			)   ->					Union[str, Any]:
									save_json(pl_module.metrics						,						pl_module.metrics_save_path			)
									return self._write_logs(__lowerCamelCase						,						__lowerCamelCase						,						"test"			)







						@rank_zero_only
						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      pl.Trainer						,						__lowerCamelCase   :      Dict			)   ->					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")







def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase=1			,     _lowerCamelCase=False     ):
			if metric == "rouge2":
						A				:       str														=    "{val_avg_rouge2:.4f}-{step_count}"
			elif metric == "bleu":
						A				:       Union[str, Any]														=    "{val_avg_bleu:.4f}-{step_count}"
			elif metric == "loss":
						A				:       str														=    "{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				:       Dict														=    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 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase     ):
			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_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE     =	{
    """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 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def __init__(						self   :      Dict						,						__lowerCamelCase   :      str=None						,						__lowerCamelCase   :      int=None						,						__lowerCamelCase   :      Optional[Any]=False						,						__lowerCamelCase   :      List[Any]=True						,						__lowerCamelCase   :      int=False						,						__lowerCamelCase   :      Optional[int]="<s>"						,						__lowerCamelCase   :      Union[str, Any]="</s>"						,						__lowerCamelCase   :      Dict="<unk>"						,						__lowerCamelCase   :      List[str]="<sep>"						,						__lowerCamelCase   :      Any="<pad>"						,						__lowerCamelCase   :      Optional[Any]="<cls>"						,						__lowerCamelCase   :      Union[str, Any]="<mask>"						,						__lowerCamelCase   :      List[str]=["<eop>", "<eod>"]						,						**__lowerCamelCase   :      Optional[Any]						,						)   ->					Optional[int]:
									A				:       Any														=    AddedToken(__lowerCamelCase						,						lstrip=__lowerCamelCase						,						rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase						,						__lowerCamelCase			) else mask_token

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

									A				:       str														=    3
									A				:       str														=    do_lower_case
									A				:       int														=    remove_space
									A				:       Optional[int]														=    keep_accents
									A				:       Tuple														=    vocab_file
									A				:       int														=    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."			)
									A				:       str														=    jieba
									A				:       Optional[Any]														=    str.maketrans(" \n"						,						"\u2582\u2583"			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Dict														=    [self.sep_token_id]
									A				:       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       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Tuple														=    [self.sep_token_id]
									A				:       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       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      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(__lowerCamelCase			):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
												return
									A				:       List[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,)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						__lowerCamelCase   :      Dict						,						*__lowerCamelCase   :      List[str]						,						**__lowerCamelCase   :      Tuple			)   ->					str:
									A				:       Union[str, Any]														=    [
									    " ".join([x.translate(self.translator			) for x in self.jieba.cut(__lowerCamelCase						,						cut_all=__lowerCamelCase			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(__lowerCamelCase						,						*__lowerCamelCase						,						**__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						*__lowerCamelCase   :      Optional[Any]						,						**__lowerCamelCase   :      Optional[int]			)   ->					Tuple:
									A				:       Optional[Any]														=    super()._decode(*__lowerCamelCase						,						**__lowerCamelCase			)
									A				:       Tuple														=    text.replace(" "						,						""			).replace("\u2582"						,						" "			).replace("\u2583"						,						"\n"			)
									return text