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


__lowerCamelCase							= logging.getLogger(__name__)






class 						UpperCAmelCase	(		pl.Callback				):



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]					,		snake_case__	:      Optional[Any]					,		snake_case__	:      List[Any]				)	->			Tuple:


          '''simple docstring'''


          snake_case :     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(snake_case__				)



   @rank_zero_only
   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]					,		snake_case__	:      pl.Trainer					,		snake_case__	:      pl.LightningModule					,		snake_case__	:      str					,		snake_case__	:      Tuple=True				)	->			None:


          '''simple docstring'''


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

          if not save_generations:
                 return

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



   @rank_zero_only
   def        _SCREAMING_SNAKE_CASE     (self	:      int					,		snake_case__	:      Optional[int]					,		snake_case__	:      Optional[Any]				)	->			Optional[int]:


          '''simple docstring'''


          try:
                 snake_case :     Tuple           =   pl_module.model.model.num_parameters()
          except AttributeError:
                 snake_case :     Optional[int]           =   pl_module.model.num_parameters()

          snake_case :     Optional[int]           =   count_trainable_parameters(snake_case__				)
          # 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[Any]					,		snake_case__	:      pl.Trainer					,		snake_case__	:      pl.LightningModule				)	->			List[str]:


          '''simple docstring'''


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



   @rank_zero_only
   def        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      pl.Trainer					,		snake_case__	:      List[Any]				)	->			str:


          '''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     UpperCamelCase      (					__lowerCamelCase  :				str       ,      __lowerCamelCase  :				Any       ,      __lowerCamelCase  :				Optional[Any]=1       ,      __lowerCamelCase  :				str=False					):
       if metric == "rouge2":
              snake_case :     str           =   "{val_avg_rouge2:.4f}-{step_count}"
       elif metric == "bleu":
              snake_case :     List[Any]           =   "{val_avg_bleu:.4f}-{step_count}"
       elif metric == "loss":
              snake_case :     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."					)

       snake_case :     str           =   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  :				str       ,      __lowerCamelCase  :				Any					):
       return EarlyStopping(
           monitor=f"""val_{metric}"""       ,      mode="min" if "loss" in metric else "max"       ,      patience=__lowerCamelCase       ,      verbose=__lowerCamelCase       ,      )







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class 						UpperCAmelCase	(		A_				):



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]					,		snake_case__	:      Dict					,		snake_case__	:      Tuple					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]				)	->			Any:


          '''simple docstring'''


          return True



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Dict:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"				)
          snake_case :     str           =   "Hugging Face大法好，谁用谁知道。"
          snake_case :     Optional[Any]           =   "Hugging Face大法好,谁用谁知道。<unk>"
          snake_case :     int           =   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

          snake_case :     Union[str, Any]           =   tokenizer.tokenize(snake_case__				)
          self.assertListEqual(snake_case__					,		snake_case__				)

          snake_case :     Tuple           =   tokens + [tokenizer.unk_token]

          snake_case :     int           =   [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)					,		snake_case__				)

          snake_case :     Dict           =   tokenizer.decode(snake_case__				)
          self.assertEqual(snake_case__					,		snake_case__				)





