

'''simple docstring'''

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowerCAmelCase_     =      logging.get_logger(__name__)  # pylint: disable=invalid-name







class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = ['''safety_checker''', '''feature_extractor''']

				def __init__(       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_ = True,						)    ->							str:
					super().__init__()
					if hasattr(scheduler.config,						'steps_offset' ) and scheduler.config.steps_offset != 1:
						snake_case							=					(
						    F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
						    F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
						    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
						    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
						    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
						    ' file'
						)
						deprecate('steps_offset!=1',						'1.0.0',						lowercase_,						standard_warn=lowercase_ )
						snake_case							=					dict(scheduler.config )
						snake_case							=					1
						snake_case							=					FrozenDict(lowercase_ )

					self.register_modules(
					    vae=lowercase_,						text_encoder=lowercase_,						tokenizer=lowercase_,						unet=lowercase_,						scheduler=lowercase_,						safety_checker=lowercase_,						feature_extractor=lowercase_,						)
					self.register_to_config(requires_safety_checker=lowercase_ )

				@property
				def 					_lowerCamelCase    (       self )    ->							Dict[str, Any]:
					return {k: getattr(self,						lowercase_ ) for k in self.config.keys() if not k.startswith('_' )}

				def 					_lowerCamelCase    (       self,						lowercase_ = "auto" )    ->							Optional[int]:
					if slice_size == "auto":
						# half the attention head size is usually a good trade-off between
						# speed and memory
						snake_case							=					self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Dict:
					self.enable_attention_slicing(lowercase_ )

				@torch.no_grad()
				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_ = 0.8,						lowercase_ = 50,						lowercase_ = 7.5,						lowercase_ = None,						lowercase_ = 1,						lowercase_ = 0.0,						lowercase_ = None,						lowercase_ = "pil",						lowercase_ = True,						lowercase_ = None,						lowercase_ = 1,						)    ->							List[Any]:
					# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
					return StableDiffusionInpaintPipelineLegacy(**self.components )(
					    prompt=lowercase_,						image=lowercase_,						mask_image=lowercase_,						strength=lowercase_,						num_inference_steps=lowercase_,						guidance_scale=lowercase_,						negative_prompt=lowercase_,						num_images_per_prompt=lowercase_,						eta=lowercase_,						generator=lowercase_,						output_type=lowercase_,						return_dict=lowercase_,						callback=lowercase_,						)

				@torch.no_grad()
				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_ = 0.8,						lowercase_ = 50,						lowercase_ = 7.5,						lowercase_ = None,						lowercase_ = 1,						lowercase_ = 0.0,						lowercase_ = None,						lowercase_ = "pil",						lowercase_ = True,						lowercase_ = None,						lowercase_ = 1,						**lowercase_,						)    ->							str:
					# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
					return StableDiffusionImgaImgPipeline(**self.components )(
					    prompt=lowercase_,						image=lowercase_,						strength=lowercase_,						num_inference_steps=lowercase_,						guidance_scale=lowercase_,						negative_prompt=lowercase_,						num_images_per_prompt=lowercase_,						eta=lowercase_,						generator=lowercase_,						output_type=lowercase_,						return_dict=lowercase_,						callback=lowercase_,						callback_steps=lowercase_,						)






				@torch.no_grad()
				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = 512,						lowercase_ = 512,						lowercase_ = 50,						lowercase_ = 7.5,						lowercase_ = None,						lowercase_ = 1,						lowercase_ = 0.0,						lowercase_ = None,						lowercase_ = None,						lowercase_ = "pil",						lowercase_ = True,						lowercase_ = None,						lowercase_ = 1,						)    ->							Dict:
					# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
					return StableDiffusionPipeline(**self.components )(
					    prompt=lowercase_,						height=lowercase_,						width=lowercase_,						num_inference_steps=lowercase_,						guidance_scale=lowercase_,						negative_prompt=lowercase_,						num_images_per_prompt=lowercase_,						eta=lowercase_,						generator=lowercase_,						latents=lowercase_,						output_type=lowercase_,						return_dict=lowercase_,						callback=lowercase_,						callback_steps=lowercase_,						)








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


UpperCAmelCase__			:  List[Any]					     =     logging.getLogger(__name__)







class 							a__							( pl.Callback							):







  """simple docstring"""




  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Any		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = {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 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			pl.LightningModule					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Tuple=True		)       ->None:
        """simple docstring"""







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

        if not save_generations:
              return

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


  @rank_zero_only
  def 			_lowercase   (      self     :			Optional[int]					,					UpperCAmelCase__     :			Optional[int]					,					UpperCAmelCase__     :			Union[str, Any]		)       ->int:
        """simple docstring"""







        try:
              SCREAMING_SNAKE_CASE			:   int	  = pl_module.model.model.num_parameters()
        except AttributeError:
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = pl_module.model.num_parameters()

        SCREAMING_SNAKE_CASE			:   Dict	  = 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 			_lowercase   (      self     :			str					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			pl.LightningModule		)       ->Optional[int]:
        """simple docstring"""







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




  @rank_zero_only
  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			List[Any]		)       ->Dict:
        """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        __lowercase   (  _A    ,     _A    ,     _A=1    ,     _A=False      )  ->    Tuple:
      if metric == "rouge2":
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            SCREAMING_SNAKE_CASE			:   Tuple	  = """{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "loss":
            SCREAMING_SNAKE_CASE			:   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."""      )

      SCREAMING_SNAKE_CASE			:   Tuple	  = ModelCheckpoint(
          dirpath=_A    ,     filename=_A    ,     monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     save_top_k=_A    ,     )
      return checkpoint_callback







def        __lowercase   (  _A    ,     _A      )  ->    Optional[int]:
      return EarlyStopping(
          monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     patience=_A    ,     verbose=_A    ,     )




