






'''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_rag import save_json




def        _SCREAMING_SNAKE_CASE  (				A  :       Any  )    -> Union[str, Any]:







   """simple docstring"""
   __snake_case				:      List[str]     							= filter(lambda A  : p.requires_grad  ,		model.parameters()  )
   __snake_case				:      int     							= sum([np.prod(p.size()  ) for p in model_parameters]  )
   return params


__A    	=   logging.getLogger(__name__)




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       int  )    -> Any:







   """simple docstring"""
   if metric == "rouge2":
      __snake_case				:      Tuple     							= '{val_avg_rouge2:.4f}-{step_count}'
   elif metric == "bleu":
      __snake_case				:      Tuple     							= '{val_avg_bleu:.4f}-{step_count}'
   elif metric == "em":
      __snake_case				:      Optional[int]     							= '{val_avg_em:.4f}-{step_count}'
   elif metric == "loss":
      __snake_case				:      Union[str, Any]     							= '{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.'  )

   __snake_case				:      str     							= ModelCheckpoint(
       dirpath=A  ,		filename=A  ,		monitor=F"""val_{metric}"""  ,		mode='max'  ,		save_top_k=1  ,		every_n_epochs=1  ,		)

   return checkpoint_callback




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[int]  ,		A  :       Tuple  )    -> Dict:







   """simple docstring"""
   return EarlyStopping(
       monitor=F"""val_{metric}"""  ,		mode='min' if 'loss' in metric else 'max'  ,		patience=A  ,		verbose=A  ,		)





class a_    (    pl.Callback							):







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      str:
      """simple docstring"""






      __snake_case				:      int     							= {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     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a=True)			->      None:
      """simple docstring"""






      logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****""")
      __snake_case				:      List[str]     							= 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				:      int     							= Path(pl_module.hparams.output_dir)
      if type_path == "test":
         __snake_case				:      Any     							= od / 'test_results.txt'
         __snake_case				:      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				:      Optional[Any]     							= od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
         __snake_case				:      Union[str, Any]     							= 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
            __snake_case				:      Dict     							= metrics[key]
            if isinstance(__a					,   torch.Tensor):
               __snake_case				:      str     							= val.item()
            __snake_case				:      Tuple     							= F"""{key}: {val:.6f}\n"""
            writer.write(__a)

      if not save_generations:
         return

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







   @rank_zero_only
   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      Dict:
      """simple docstring"""






      try:
         __snake_case				:      List[Any]     							= pl_module.model.model.num_parameters()
      except AttributeError:
         __snake_case				:      Dict     							= pl_module.model.num_parameters()

      __snake_case				:      Union[str, 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     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      Optional[int]:
      """simple docstring"""






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







   @rank_zero_only
   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      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")







'''simple docstring'''






import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class a_    (    UpperCamelCase_		,      UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							VQModel
   _snake_case        =							"""sample"""







   @property
   def     SCREAMING_SNAKE_CASE__					(self					,   __a=(3_2, 3_2))			->      str:
      """simple docstring"""






      __snake_case				:      Dict     							= 4
      __snake_case				:      Optional[int]     							= 3

      __snake_case				:      str     							= floats_tensor((batch_size, num_channels) + sizes).to(__a)

      return {"sample": image}







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= {
          'block_out_channels': [3_2, 6_4],
          'in_channels': 3,
          'out_channels': 3,
          'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
          'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
          'latent_channels': 3,
      }
      __snake_case				:      List[Any]     							= self.dummy_input
      return init_dict, inputs_dict







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      __snake_case							,__snake_case				:      List[Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy'					,   output_loading_info=__a)
      self.assertIsNotNone(__a)
      self.assertEqual(len(loading_info['missing_keys'])					,   0)

      model.to(__a)
      __snake_case				:      Any     							= model(**self.dummy_input)

      assert image is not None, "Make sure output is not None"







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy')
      model.to(__a).eval()

      torch.manual_seed(0)
      if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0)

      __snake_case				:      Tuple     							= torch.randn(1					,   model.config.in_channels					,   model.config.sample_size					,   model.config.sample_size)
      __snake_case				:      Optional[int]     							= image.to(__a)
      with torch.no_grad():
         __snake_case				:      List[Any]     							= model(__a).sample

      __snake_case				:      int     							= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      __snake_case				:      int     							= torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143])
      # fmt: on
      self.assertTrue(torch.allclose(__a					,   __a					,   atol=1E-3))