


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,
)


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

SCREAMING_SNAKE_CASE       :      Optional[Any]												= "\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_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_=8 )    ->						Dict:
    _lowercase       :							Optional[Any]      		=		height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    _lowercase       :							Any      		=		width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   Dict:




           """simple docstring"""

           super().__init__()

           self.register_modules(
               unet=lowerCamelCase,      scheduler=lowerCamelCase,      movq=lowerCamelCase,      )
           _lowercase       :							List[str]      		=		2 ** (len(self.movq.config.block_out_channels) - 1)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Optional[Any]:




           """simple docstring"""

           if latents is None:
               _lowercase       :							Optional[Any]      		=		randn_tensor(lowerCamelCase,      generator=lowerCamelCase,      device=lowerCamelCase,      dtype=lowerCamelCase)
           else:
               if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''')
               _lowercase       :							int      		=		latents.to(lowerCamelCase)

           _lowercase       :							int      		=		latents * scheduler.init_noise_sigma
           return latents
       def 			UpperCamelCase     (					self,      lowerCamelCase=0)       ->   Optional[int]:




           """simple docstring"""

           if is_accelerate_available():
               from accelerate import cpu_offload
           else:
               raise ImportError('Please install accelerate via `pip install accelerate`')

           _lowercase       :							Tuple      		=		torch.device(F'''cuda:{gpu_id}''')

           _lowercase       :							Optional[Any]      		=		[
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                   cpu_offload(lowerCamelCase,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase=0)       ->   int:




           """simple docstring"""

           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.')

           _lowercase       :							Union[str, Any]      		=		torch.device(F'''cuda:{gpu_id}''')

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

           _lowercase       :							Optional[Any]      		=		None
           for cpu_offloaded_model in [self.unet, self.movq]:
               _lowercase		,				_lowercase       :							List[str]      		=		cpu_offload_with_hook(lowerCamelCase,      lowerCamelCase,      prev_module_hook=lowerCamelCase)

           # We'll offload the last model manually.
           _lowercase       :							Dict      		=		hook
       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           if not hasattr(self.unet,      '_hf_hook'):
               return self.device
           for module in self.unet.modules():
               if (
                   hasattr(lowerCamelCase,      '_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(lowerCamelCase)
       def __call__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = 5_12,      lowerCamelCase = 5_12,      lowerCamelCase = 1_00,      lowerCamelCase = 4.0,      lowerCamelCase = 1,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = "pil",      lowerCamelCase = True,      )       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							int      		=		self._execution_device

           _lowercase       :							Any      		=		guidance_scale > 1.0

           if isinstance(lowerCamelCase,      lowerCamelCase):
               _lowercase       :							int      		=		torch.cat(lowerCamelCase,      dim=0)
           _lowercase       :							Optional[int]      		=		image_embeds.shape[0] * num_images_per_prompt
           if isinstance(lowerCamelCase,      lowerCamelCase):
               _lowercase       :							Optional[int]      		=		torch.cat(lowerCamelCase,      dim=0)

           if do_classifier_free_guidance:
               _lowercase       :							str      		=		image_embeds.repeat_interleave(lowerCamelCase,      dim=0)
               _lowercase       :							int      		=		negative_image_embeds.repeat_interleave(lowerCamelCase,      dim=0)

           _lowercase       :							List[str]      		=		torch.cat([negative_image_embeds, image_embeds],      dim=0).to(dtype=self.unet.dtype,      device=lowerCamelCase)

           self.scheduler.set_timesteps(lowerCamelCase,      device=lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		self.scheduler.timesteps

           _lowercase       :							List[str]      		=		self.unet.config.in_channels

           _lowercase		,				_lowercase       :							Union[str, Any]      		=		downscale_height_and_width(lowerCamelCase,      lowerCamelCase,      self.movq_scale_factor)

           # create initial latent
           _lowercase       :							str      		=		self.prepare_latents(
               (batch_size, num_channels_latents, height, width),      image_embeds.dtype,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      self.scheduler,      )

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

               _lowercase       :							Tuple      		=		{'image_embeds': image_embeds}
               _lowercase       :							Optional[Any]      		=		self.unet(
                   sample=lowerCamelCase,      timestep=lowerCamelCase,      encoder_hidden_states=lowerCamelCase,      added_cond_kwargs=lowerCamelCase,      return_dict=lowerCamelCase,      )[0]

               if do_classifier_free_guidance:
                   _lowercase		,				_lowercase       :							List[str]      		=		noise_pred.split(latents.shape[1],      dim=1)
                   _lowercase		,				_lowercase       :							Tuple      		=		noise_pred.chunk(2)
                   _lowercase		,				_lowercase       :							Any      		=		variance_pred.chunk(2)
                   _lowercase       :							Dict      		=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   _lowercase       :							Tuple      		=		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"]
               ):
                   _lowercase		,				_lowercase       :							int      		=		noise_pred.split(latents.shape[1],      dim=1)

               # compute the previous noisy sample x_t -> x_t-1
               _lowercase       :							str      		=		self.scheduler.step(
                   lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      generator=lowerCamelCase,      )[0]
           # post-processing
           _lowercase       :							str      		=		self.movq.decode(lowerCamelCase,      force_not_quantize=lowerCamelCase)['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"]:
               _lowercase       :							Dict      		=		image * 0.5 + 0.5
               _lowercase       :							Any      		=		image.clamp(0,      1)
               _lowercase       :							List[str]      		=		image.cpu().permute(0,      2,      3,      1).float().numpy()

           if output_type == "pil":
               _lowercase       :							Optional[Any]      		=		self.numpy_to_pil(lowerCamelCase)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=lowerCamelCase)







"""simple docstring"""
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase    :int     =					['small', 'medium', 'large']

_lowerCAmelCase    :int     =					'lm_head.decoder.weight'
_lowerCAmelCase    :Dict     =					'lm_head.weight'





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    d.pop(UpperCamelCase__							)
    os.makedirs(UpperCamelCase__     ,       exist_ok=UpperCamelCase__							)
    torch.save(UpperCamelCase__     ,       os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)							)


if __name__ == "__main__":
  _lowerCAmelCase    :Dict     =					argparse.ArgumentParser()
  parser.add_argument('--dialogpt_path', default='.', type=str)
  _lowerCAmelCase    :str     =					parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase    :Tuple     =					os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl")
    _lowerCAmelCase    :int     =					f"./DialoGPT-{MODEL}"
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )


