



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_(lowerCamelCase_    ) ->       Dict:
						UpperCAmelCase         =     filter(lambda lowerCamelCase_    : p.requires_grad    ,   model.parameters()    )
						UpperCAmelCase         =     sum([np.prod(p.size()    ) for p in model_parameters]    )
						return params


__lowerCamelCase     :							Dict  							=				logging.getLogger(__name__)

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       Union[str, Any]:
						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=UpperCAmelCase__    ,   filename=UpperCAmelCase__    ,   monitor=F'val_{metric}'    ,   mode="max"    ,   save_top_k=1    ,   every_n_epochs=1    ,   )

						return checkpoint_callback

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       Tuple:
						return EarlyStopping(
						    monitor=F'val_{metric}'    ,   mode="min" if "loss" in metric else "max"    ,   patience=UpperCAmelCase__    ,   verbose=UpperCAmelCase__    ,   )





class  __magic_name__   (							pl.Callback						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     List[str]      )				->					Dict:
								'''simple docstring'''







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






		@rank_zero_only
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     pl.Trainer				,		UpperCamelCase__      :     pl.LightningModule				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Dict=True      )				->					int:
								'''simple docstring'''







								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=lowercase_      )
														generations_file.parent.mkdir(exist_ok=lowercase_      )
								with open(lowercase_				,		"a+"      ) as writer:
														for key in sorted(lowercase_      ):
																				if key in ["log", "progress_bar", "preds"]:
																										continue
																				UpperCAmelCase         =     metrics[key]
																				if isinstance(lowercase_				,		torch.Tensor      ):
																										UpperCAmelCase         =     val.item()
																				UpperCAmelCase         =     F'{key}: {val:.6f}\n'
																				writer.write(lowercase_      )

								if not save_generations:
														return

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






		@rank_zero_only
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     List[str]      )				->					int:
								'''simple docstring'''







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

								UpperCAmelCase         =     count_trainable_parameters(lowercase_      )
								# 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      :     Tuple				,		UpperCamelCase__      :     pl.Trainer				,		UpperCamelCase__      :     pl.LightningModule      )				->					int:
								'''simple docstring'''







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







		@rank_zero_only
		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     pl.Trainer				,		UpperCamelCase__      :     int      )				->					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")








import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin





@require_jieba
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Optional[Any]          =CpmAntTokenizer
		lowercase      :       Dict          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								super().setUp()

								UpperCAmelCase         =     [
								    "<d>",
								    "</d>",
								    "<s>",
								    "</s>",
								    "</_>",
								    "<unk>",
								    "<pad>",
								    "</n>",
								    "我",
								    "是",
								    "C",
								    "P",
								    "M",
								    "A",
								    "n",
								    "t",
								]
								UpperCAmelCase         =     os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["vocab_file"]      )
								with open(self.vocab_file				,		"w"				,		encoding="utf-8"      ) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )







		@tooslow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     ["今天", "天气", "真", "好", "！"]
								UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     [tokenizer.bos_token] + tokens

								UpperCAmelCase         =     [6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.decode(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )



