
import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard






@slow
@require_flax
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  int					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''     )
      _A:  Union[str, Any]					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''     )

      _A:  List[Any]					=       '''xvjiarui/stable-diffusion-2-inpainting'''
      _A			,  _A:  Dict					=       FlaxStableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase_			,			safety_checker=lowerCAmelCase_     )

      _A:  Any					=       '''Face of a yellow cat, high resolution, sitting on a park bench'''

      _A:  Optional[int]					=       jax.random.PRNGKey(0     )
      _A:  List[str]					=       5_0

      _A:  Tuple					=       jax.device_count()
      _A:  Optional[Any]					=       num_samples * [prompt]
      _A:  Union[str, Any]					=       num_samples * [init_image]
      _A:  Optional[Any]					=       num_samples * [mask_image]
      _A			,  _A			,  _A:  List[Any]					=       pipeline.prepare_inputs(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_     )

      # shard inputs and rng
      _A:  Union[str, Any]					=       replicate(lowerCAmelCase_     )
      _A:  List[Any]					=       jax.random.split(lowerCAmelCase_			,			jax.device_count()     )
      _A:  int					=       shard(lowerCAmelCase_     )
      _A:  Any					=       shard(lowerCAmelCase_     )
      _A:  Optional[int]					=       shard(lowerCAmelCase_     )

      _A:  str					=       pipeline(
          lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			jit=lowerCAmelCase_     )

      _A:  Union[str, Any]					=       output.images.reshape(lowerCAmelCase_			,			5_1_2			,			5_1_2			,			3     )

      _A:  str					=       images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      _A:  Optional[int]					=       jnp.asarray(jax.device_get(image_slice.flatten()     )     )
      _A:  str					=       jnp.array(
          [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]     )
      print(F"""output_slice: {output_slice}"""     )
      assert jnp.abs(output_slice - expected_slice     ).max() < 1e-2





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__							(					a      )      ->   int:
   _A:  int					=       filter(lambda a      : p.requires_grad		,		model.parameters()      )
   _A:  Dict					=       sum([np.prod(p.size()      ) for p in model_parameters]      )
   return params


UpperCAmelCase__		:     str     			=      logging.getLogger(__name__)






def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   if metric == "rouge2":
      _A:  Tuple					=       '''{val_avg_rouge2:.4f}-{step_count}'''
   elif metric == "bleu":
      _A:  List[str]					=       '''{val_avg_bleu:.4f}-{step_count}'''
   elif metric == "em":
      _A:  str					=       '''{val_avg_em:.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.'''      )

   _A:  List[str]					=       ModelCheckpoint(
       dirpath=a		,		filename=a		,		monitor=f"""val_{metric}"""		,		mode='''max'''		,		save_top_k=3		,		every_n_epochs=1		,		)
   return checkpoint_callback






def   lowerCamelCase__							(					a		,		a      )      ->   Optional[Any]:
   return EarlyStopping(
       monitor=f"""val_{metric}"""		,		mode='''min''' if '''loss''' in metric else '''max'''		,		patience=a		,		verbose=a		,		)






class UpperCAmelCase   (						pl.Callback   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Dict     ):

      """simple docstring"""
      _A:  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(lowerCAmelCase_     )


   @rank_zero_only
   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							pl.LightningModule			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Any=True     ):

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

      if not save_generations:
         return

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


   @rank_zero_only
   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      try:
         _A:  Union[str, Any]					=       pl_module.model.model.num_parameters()
      except AttributeError:
         _A:  Any					=       pl_module.model.num_parameters()

      _A:  Optional[int]					=       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  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							pl.LightningModule     ):

      """simple docstring"""
      save_json(pl_module.metrics			,			pl_module.metrics_save_path     )
      return self._write_logs(lowerCAmelCase_			,			lowerCAmelCase_			,			'''test'''     )


   @rank_zero_only
   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							pl.Trainer			,			lowerCAmelCase_     :							Optional[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")



