


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

_A		       =						'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					List[Any]=8				):
     __UpperCamelCase    							=height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     __UpperCamelCase    							=width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def __init__(			self					,   A_					,   A_					,   A_					,   )  ->       int:
         super().__init__()

         self.register_modules(
             unet=A_					,   scheduler=A_					,   movq=A_					,   )
         __UpperCamelCase    							=2 ** (len(self.movq.config.block_out_channels    ) - 1)






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       Tuple:
         if latents is None:
              __UpperCamelCase    							=randn_tensor(A_					,   generator=A_					,   device=A_					,   dtype=A_    )
         else:
              if latents.shape != shape:
                   raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'    )
              __UpperCamelCase    							=latents.to(A_    )

         __UpperCamelCase    							=latents * scheduler.init_noise_sigma
         return latents






    def _a					(			self					,   A_=0    )  ->       List[str]:
         if is_accelerate_available():
              from accelerate import cpu_offload
         else:
              raise ImportError('Please install accelerate via `pip install accelerate`'    )

         __UpperCamelCase    							=torch.device(f'cuda:{gpu_id}'    )

         __UpperCamelCase    							=[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                   cpu_offload(A_					,   A_    )






    def _a					(			self					,   A_=0    )  ->       Tuple:
         if is_accelerate_available() and is_accelerate_version('>='					,   '0.17.0.dev0'    ):
              from accelerate import cpu_offload_with_hook
         else:
              raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'    )

         __UpperCamelCase    							=torch.device(f'cuda:{gpu_id}'    )

         if self.device.type != "cpu":
              self.to('cpu'					,   silence_dtype_warnings=A_    )
              torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         __UpperCamelCase    							=None
         for cpu_offloaded_model in [self.unet, self.movq]:
              __UpperCamelCase							,      __UpperCamelCase    							=cpu_offload_with_hook(A_					,   A_					,   prev_module_hook=A_    )

         # We'll offload the last model manually.
         __UpperCamelCase    							=hook






    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def _a					(			self    )  ->       Dict:
         if not hasattr(self.unet					,   '_hf_hook'    ):
              return self.device
         for module in self.unet.modules():
              if (
                  hasattr(A_					,   '_hf_hook'    )
                  and hasattr(module._hf_hook					,   'execution_device'    )
                  and module._hf_hook.execution_device is not None
              ):
                   return torch.device(module._hf_hook.execution_device    )
         return self.device






    @torch.no_grad()
    @replace_example_docstring(A_    )
    def __call__(			self					,   A_					,   A_					,   A_ = 512					,   A_ = 512					,   A_ = 100					,   A_ = 4.0					,   A_ = 1					,   A_ = None					,   A_ = None					,   A_ = "pil"					,   A_ = True					,   )  ->       Dict:
         __UpperCamelCase    							=self._execution_device

         __UpperCamelCase    							=guidance_scale > 1.0

         if isinstance(A_					,   A_    ):
              __UpperCamelCase    							=torch.cat(A_					,   dim=0    )
         __UpperCamelCase    							=image_embeds.shape[0] * num_images_per_prompt
         if isinstance(A_					,   A_    ):
              __UpperCamelCase    							=torch.cat(A_					,   dim=0    )

         if do_classifier_free_guidance:
              __UpperCamelCase    							=image_embeds.repeat_interleave(A_					,   dim=0    )
              __UpperCamelCase    							=negative_image_embeds.repeat_interleave(A_					,   dim=0    )

         __UpperCamelCase    							=torch.cat([negative_image_embeds, image_embeds]					,   dim=0    ).to(dtype=self.unet.dtype					,   device=A_    )

         self.scheduler.set_timesteps(A_					,   device=A_    )
         __UpperCamelCase    							=self.scheduler.timesteps

         __UpperCamelCase    							=self.unet.config.in_channels

         __UpperCamelCase							,      __UpperCamelCase    							=downscale_height_and_width(A_					,   A_					,   self.movq_scale_factor    )

         # create initial latent
         __UpperCamelCase    							=self.prepare_latents(
             (batch_size, num_channels_latents, height, width)					,   image_embeds.dtype					,   A_					,   A_					,   A_					,   self.scheduler					,   )

         for i, t in enumerate(self.progress_bar(A_    )    ):
              # expand the latents if we are doing classifier free guidance
              __UpperCamelCase    							=torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

              __UpperCamelCase    							={'image_embeds': image_embeds}
              __UpperCamelCase    							=self.unet(
                  sample=A_					,   timestep=A_					,   encoder_hidden_states=A_					,   added_cond_kwargs=A_					,   return_dict=A_					,   )[0]

              if do_classifier_free_guidance:
                   __UpperCamelCase							,      __UpperCamelCase    							=noise_pred.split(latents.shape[1]					,   dim=1    )
                   __UpperCamelCase							,      __UpperCamelCase    							=noise_pred.chunk(2    )
                   __UpperCamelCase							,      __UpperCamelCase    							=variance_pred.chunk(2    )
                   __UpperCamelCase    							=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   __UpperCamelCase    							=torch.cat([noise_pred, variance_pred_text]					,   dim=1    )

              if not (
                  hasattr(self.scheduler.config					,   'variance_type'    )
                  and self.scheduler.config.variance_type in ["learned", "learned_range"]
              ):
                   __UpperCamelCase							,      __UpperCamelCase    							=noise_pred.split(latents.shape[1]					,   dim=1    )

              # compute the previous noisy sample x_t -> x_t-1
              __UpperCamelCase    							=self.scheduler.step(
                  A_					,   A_					,   A_					,   generator=A_					,   )[0]
         # post-processing
         __UpperCamelCase    							=self.movq.decode(A_					,   force_not_quantize=A_    )['sample']

         if output_type not in ["pt", "np", "pil"]:
              raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'    )

         if output_type in ["np", "pil"]:
              __UpperCamelCase    							=image * 0.5 + 0.5
              __UpperCamelCase    							=image.clamp(0					,   1    )
              __UpperCamelCase    							=image.cpu().permute(0					,   2					,   3					,   1    ).float().numpy()

         if output_type == "pil":
              __UpperCamelCase    							=self.numpy_to_pil(A_    )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=A_    )












'''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  _lowerCamelCase				( lowercase						:      Dict						)	->	Any:
				_a								= filter(lambda lowercase						: p.requires_grad    ,							model.parameters()						)
				_a								= sum([np.prod(p.size()						) for p in model_parameters]						)
				return params


lowerCAmelCase_					:    int      	=			logging.getLogger(__name__)






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Any						)	->	Any:
				if metric == "rouge2":
								_a								= "{val_avg_rouge2:.4f}-{step_count}"
				elif metric == "bleu":
								_a								= "{val_avg_bleu:.4f}-{step_count}"
				elif metric == "em":
								_a								= "{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								= ModelCheckpoint(
				    dirpath=lowercase    ,							filename=lowercase    ,							monitor=F'val_{metric}'    ,							mode="max"    ,							save_top_k=3    ,							every_n_epochs=1    ,							)
				return checkpoint_callback






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      Optional[int]						)	->	Union[str, Any]:
				return EarlyStopping(
				    monitor=F'val_{metric}'    ,							mode="min" if "loss" in metric else "max"    ,							patience=lowercase    ,							verbose=lowercase    ,							)



class 				__SCREAMING_SNAKE_CASE  (pl.Callback      ):






				"""simple docstring"""







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

								if not save_generations:
												return

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




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					int					,				__a     :					List[Any]					,				__a     :					Union[str, Any]					):
								try:
												_a								= pl_module.model.model.num_parameters()
								except AttributeError:
												_a								= pl_module.model.num_parameters()

								_a								= 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   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					):
								save_json(pl_module.metrics					,				pl_module.metrics_save_path					)
								return self._write_logs(__a					,				__a					,				"test"					)




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					Any					,				__a     :					pl.Trainer					,				__a     :					int					):
								save_json(pl_module.metrics					,				pl_module.metrics_save_path					)
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")
