


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   lowerCamelCase_					( _a ):




  """simple docstring"""






  lowerCAmelCase__     :    int					  =      filter(lambda _a : p.requires_grad			,     model.parameters() )
  lowerCAmelCase__     :    Tuple					  =      sum([np.prod(p.size() ) for p in model_parameters] )
  return params


lowerCamelCase		=	logging.getLogger(__name__)





class       _a							(       pl.Callback):




 def 							UpperCAmelCase__(      self		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	Any	)-> List[Any]:
   lowerCAmelCase__     :    Union[str, Any]					  =      {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
   pl_module.logger.log_metrics(UpperCamelCase__	)




 @rank_zero_only
 def 							UpperCAmelCase__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	pl.Trainer	,      _SCREAMING_SNAKE_CASE		:	pl.LightningModule	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int=True	)-> None:
   logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'	)
   lowerCAmelCase__     :    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
   lowerCAmelCase__     :    Tuple					  =      Path(pl_module.hparams.output_dir	)
   if type_path == "test":
     lowerCAmelCase__     :    Dict					  =      od / '''test_results.txt'''
     lowerCAmelCase__     :    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.
     lowerCAmelCase__     :    str					  =      od / F'{type_path}_results/{trainer.global_step:05d}.txt'
     lowerCAmelCase__     :    int					  =      od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
     results_file.parent.mkdir(exist_ok=UpperCamelCase__	)
     generations_file.parent.mkdir(exist_ok=UpperCamelCase__	)
   with open(UpperCamelCase__	,      '''a+'''	) as writer:
     for key in sorted(UpperCamelCase__	):
       if key in ["log", "progress_bar", "preds"]:
         continue
       lowerCAmelCase__     :    Union[str, Any]					  =      metrics[key]
       if isinstance(UpperCamelCase__	,      torch.Tensor	):
         lowerCAmelCase__     :    str					  =      val.item()
       lowerCAmelCase__     :    Any					  =      F'{key}: {val:.6f}\n'
       writer.write(UpperCamelCase__	)

   if not save_generations:
     return

   if "preds" in metrics:
     lowerCAmelCase__     :    List[Any]					  =      '''\n'''.join(metrics['''preds''']	)
     generations_file.open('''w+'''	).write(UpperCamelCase__	)




 @rank_zero_only
 def 							UpperCAmelCase__(      self		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	)-> int:







   try:
     lowerCAmelCase__     :    Optional[Any]					  =      pl_module.model.model.num_parameters()
   except AttributeError:
     lowerCAmelCase__     :    Dict					  =      pl_module.model.num_parameters()

   lowerCAmelCase__     :    Optional[int]					  =      count_trainable_parameters(UpperCamelCase__	)
   # 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 							UpperCAmelCase__(      self		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	pl.Trainer	,      _SCREAMING_SNAKE_CASE		:	pl.LightningModule	)-> Dict:
   save_json(pl_module.metrics	,      pl_module.metrics_save_path	)
   return self._write_logs(UpperCamelCase__	,      UpperCamelCase__	,      '''test'''	)




 @rank_zero_only
 def 							UpperCAmelCase__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	pl.Trainer	,      _SCREAMING_SNAKE_CASE		:	Any	)-> 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   lowerCamelCase_					( _a			,     _a			,     _a=1			,     _a=False ):




  """simple docstring"""






  if metric == "rouge2":
    lowerCAmelCase__     :    List[str]					  =      '''{val_avg_rouge2:.4f}-{step_count}'''
  elif metric == "bleu":
    lowerCAmelCase__     :    Union[str, Any]					  =      '''{val_avg_bleu:.4f}-{step_count}'''
  elif metric == "loss":
    lowerCAmelCase__     :    List[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.''' )

  lowerCAmelCase__     :    Dict					  =      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   lowerCamelCase_					( _a			,     _a ):




  """simple docstring"""






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








import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()





class       _a							(       unittest.TestCase):




	@property
	def 							UpperCAmelCase__(      self		:	Dict	)-> int:
			torch.manual_seed(0	)
			lowerCAmelCase__     :    int					  =      UNetaDModel(
			    block_out_channels=(32, 64)	,      layers_per_block=2	,      sample_size=32	,      in_channels=3	,      out_channels=3	,      down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,      up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,      )
			return model




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> Dict:
			lowerCAmelCase__     :    Union[str, Any]					  =      '''cpu'''
			lowerCAmelCase__     :    List[Any]					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    Optional[Any]					  =      DDPMScheduler()

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[Any]					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    int					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    str					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	,      return_dict=_SCREAMING_SNAKE_CASE	)[0]

			lowerCAmelCase__     :    Tuple					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    List[str]					  =      image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    int					  =      np.array(
			    [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2




	def 							UpperCAmelCase__(      self		:	List[str]	)-> Any:
			lowerCAmelCase__     :    int					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    List[Any]					  =      DDPMScheduler(prediction_type='''sample'''	)

			lowerCAmelCase__     :    int					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[int]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    List[Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    Optional[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Union[str, Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	)[0]

			lowerCAmelCase__     :    str					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    Optional[Any]					  =      image_eps[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    Any					  =      1E-2 if torch_device != '''mps''' else 3E-2
			assert np.abs(image_slice.flatten() - image_eps_slice.flatten()	).max() < tolerance





@slow
@require_torch_gpu
class       _a							(       unittest.TestCase):




	def 							UpperCAmelCase__(      self		:	Any	)-> List[Any]:
			lowerCAmelCase__     :    str					  =      '''google/ddpm-cifar10-32'''

			lowerCAmelCase__     :    List[Any]					  =      UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Any					  =      DDPMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    List[Any]					  =      image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    List[str]					  =      np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]	)
			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




