





'''simple docstring'''




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,
)


lowercase__   :				Optional[Any]      =   logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase__   :				str      =   '''\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 	_lowerCAmelCase      (  __snake_case  :			List[str]			,							__snake_case  :			Any			,							__snake_case  :			List[str]=8      )					->					Any:
     __A						:			List[str]								=	height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     __A						:			Tuple								=	width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor







class 			SCREAMING_SNAKE_CASE   (lowercase__							):


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     ):

          '''simple docstring'''






          super().__init__()

          self.register_modules(
              unet=lowercase_						,     scheduler=lowercase_						,     movq=lowercase_						,     )
          __A						:			int								=	2 ** (len(self.movq.config.block_out_channels) - 1)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          if latents is None:
               __A						:			Dict								=	randn_tensor(lowercase_						,     generator=lowercase_						,     device=lowercase_						,     dtype=lowercase_)
          else:
               if latents.shape != shape:
                    raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}')
               __A						:			str								=	latents.to(lowercase_)

          __A						:			Dict								=	latents * scheduler.init_noise_sigma
          return latents


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase=0):

          '''simple docstring'''






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

          __A						:			Any								=	torch.device(F'cuda:{gpu_id}')

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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase=0):

          '''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.')

          __A						:			Any								=	torch.device(F'cuda:{gpu_id}')

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

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

          # We'll offload the last model manually.
          __A						:			Tuple								=	hook


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if not hasattr(self.unet						,     '_hf_hook'):
               return self.device
          for module in self.unet.modules():
               if (
                   hasattr(lowercase_						,     '_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(lowercase_)
     def __call__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 512						,     _UpperCAmelCase = 100						,     _UpperCAmelCase = 4.0						,     _UpperCAmelCase = 1						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = "pil"						,     _UpperCAmelCase = True						,     ):

          '''simple docstring'''






          __A						:			str								=	self._execution_device

          __A						:			List[Any]								=	guidance_scale > 1.0

          if isinstance(lowercase_						,     lowercase_):
               __A						:			int								=	torch.cat(lowercase_						,     dim=0)
          __A						:			Any								=	image_embeds.shape[0] * num_images_per_prompt
          if isinstance(lowercase_						,     lowercase_):
               __A						:			List[Any]								=	torch.cat(lowercase_						,     dim=0)

          if do_classifier_free_guidance:
               __A						:			Tuple								=	image_embeds.repeat_interleave(lowercase_						,     dim=0)
               __A						:			List[str]								=	negative_image_embeds.repeat_interleave(lowercase_						,     dim=0)

          __A						:			Optional[Any]								=	torch.cat([negative_image_embeds, image_embeds]						,     dim=0).to(dtype=self.unet.dtype						,     device=lowercase_)

          self.scheduler.set_timesteps(lowercase_						,     device=lowercase_)
          __A						:			List[Any]								=	self.scheduler.timesteps

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

          __A						:			Dict								=	downscale_height_and_width(lowercase_						,     lowercase_						,     self.movq_scale_factor)

          # create initial latent
          __A						:			int								=	self.prepare_latents(
              (batch_size, num_channels_latents, height, width)						,     image_embeds.dtype						,     lowercase_						,     lowercase_						,     lowercase_						,     self.scheduler						,     )

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

               __A						:			Union[str, Any]								=	{"image_embeds": image_embeds}
               __A						:			Optional[Any]								=	self.unet(
                   sample=lowercase_						,     timestep=lowercase_						,     encoder_hidden_states=lowercase_						,     added_cond_kwargs=lowercase_						,     return_dict=lowercase_						,     )[0]

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

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

          if output_type == "pil":
               __A						:			Dict								=	self.numpy_to_pil(lowercase_)

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=lowercase_)






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''vocab_size''': len(tokenizer),
    '''scale_attn_by_inverse_layer_idx''': True,
    '''reorder_and_upcast_attn''': True,
}

# Load model config (GPT-2 large in this case)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)