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 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							Tuple       =	filter(lambda lowercase					: p.requires_grad							,				model.parameters()					)
   lowerCAmelCase_:							Union[str, Any]       =	sum([np.prod(p.size()					) for p in model_parameters]					)
   return params


a  :      Dict            =    logging.getLogger(__name__)
def 		snake_case__	(		lowercase							,				lowercase					):
   if metric == "rouge2":
      lowerCAmelCase_:							int       =	"{val_avg_rouge2:.4f}-{step_count}"
   elif metric == "bleu":
      lowerCAmelCase_:							Dict       =	"{val_avg_bleu:.4f}-{step_count}"
   elif metric == "em":
      lowerCAmelCase_:							int       =	"{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."					)

   lowerCAmelCase_:							str       =	ModelCheckpoint(
       dirpath=lowercase							,				filename=lowercase							,				monitor=F'''val_{metric}'''							,				mode="max"							,				save_top_k=3							,				every_n_epochs=1							,				)
   return checkpoint_callback
def 		snake_case__	(		lowercase							,				lowercase					):
   return EarlyStopping(
       monitor=F'''val_{metric}'''							,				mode="min" if "loss" in metric else "max"							,				patience=lowercase							,				verbose=lowercase							,				)







class 					_lowercase    (     pl.Callback			):






    '''simple docstring'''




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       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(lowerCamelCase__ )




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

       if not save_generations:
          return

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




    @rank_zero_only
    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       try:
          lowerCAmelCase_:							Optional[int]       =	pl_module.model.model.num_parameters()
       except AttributeError:
          lowerCAmelCase_:							Union[str, Any]       =	pl_module.model.num_parameters()

       lowerCAmelCase_:							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      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       save_json(pl_module.metrics       ,						pl_module.metrics_save_path )
       return self._write_logs(lowerCamelCase__       ,						lowerCamelCase__       ,						"test" )




    @rank_zero_only
    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       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 gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a  :      List[str]            =    False

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    pass





@nightly
@require_torch_gpu
class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def      _a    (    self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							List[str]       =	torch.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCamelCase__ )
          lowerCAmelCase_:							int       =	VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__ )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	generator.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	VersatileDiffusionTextToImagePipeline.from_pretrained(
           "shi-labs/versatile-diffusion"       ,						torch_dtype=torch.floataa )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							str       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							int       =	torch.manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=50       ,						output_type="numpy" ).images

       lowerCAmelCase_:							List[Any]       =	image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCAmelCase_:							Optional[int]       =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

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