


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,
)


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

lowerCAmelCase       =							'\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 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=8     ):


		"""simple docstring"""




		lowercase__            =      height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowercase__            =      width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor


class        _a							(			UpperCamelCase__			):

							def __init__(							self:	str    ,						UpperCamelCase_:	UNetaDConditionModel    ,						UpperCamelCase_:	DDPMScheduler    ,						UpperCamelCase_:	VQModel    ,						)			->   str:





									"""simple docstring"""







									super().__init__()

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

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Optional[int]    ,						UpperCamelCase_:	Dict    ,						UpperCamelCase_:	Optional[int]    ,						UpperCamelCase_:	Tuple							)			->   Any:





									"""simple docstring"""







									if latents is None:
											lowercase__            =      randn_tensor(UpperCamelCase_    ,						generator=UpperCamelCase_    ,						device=UpperCamelCase_    ,						dtype=UpperCamelCase_							)
									else:
											if latents.shape != shape:
													raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'							)
											lowercase__            =      latents.to(UpperCamelCase_							)

									lowercase__            =      latents * scheduler.init_noise_sigma
									return latents

							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	int=0							)			->   Union[str, Any]:





									"""simple docstring"""







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

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

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

							def       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	int=0							)			->   List[str]:





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

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

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

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

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def       lowerCamelCase_ (							self:	Dict							)			->   int:





									"""simple docstring"""







									if not hasattr(self.unet    ,						'''_hf_hook'''							):
											return self.device
									for module in self.unet.modules():
											if (
											    hasattr(UpperCamelCase_    ,						'''_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(UpperCamelCase_							)
							def __call__(							self:	Tuple    ,						UpperCamelCase_:	Union[torch.FloatTensor, List[torch.FloatTensor]]    ,						UpperCamelCase_:	Union[torch.FloatTensor, List[torch.FloatTensor]]    ,						UpperCamelCase_:	int = 512    ,						UpperCamelCase_:	int = 512    ,						UpperCamelCase_:	int = 100    ,						UpperCamelCase_:	float = 4.0    ,						UpperCamelCase_:	int = 1    ,						UpperCamelCase_:	Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						UpperCamelCase_:	Optional[torch.FloatTensor] = None    ,						UpperCamelCase_:	Optional[str] = "pil"    ,						UpperCamelCase_:	bool = True    ,						)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self._execution_device

									lowercase__            =      guidance_scale > 1.0

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

									if do_classifier_free_guidance:
											lowercase__            =      image_embeds.repeat_interleave(UpperCamelCase_    ,						dim=0							)
											lowercase__            =      negative_image_embeds.repeat_interleave(UpperCamelCase_    ,						dim=0							)

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

									self.scheduler.set_timesteps(UpperCamelCase_    ,						device=UpperCamelCase_							)
									lowercase__            =      self.scheduler.timesteps

									lowercase__            =      self.unet.config.in_channels

									lowercase__     ,		lowercase__            =      downscale_height_and_width(UpperCamelCase_    ,						UpperCamelCase_    ,						self.movq_scale_factor							)

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

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

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

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

											# compute the previous noisy sample x_t -> x_t-1
											lowercase__            =      self.scheduler.step(
											    UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						generator=UpperCamelCase_    ,						)[0]
									# post-processing
									lowercase__            =      self.movq.decode(UpperCamelCase_    ,						force_not_quantize=UpperCamelCase_							)['''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__            =      image * 0.5 + 0.5
											lowercase__            =      image.clamp(0    ,						1							)
											lowercase__            =      image.cpu().permute(0    ,						2    ,						3    ,						1							).float().numpy()

									if output_type == "pil":
											lowercase__            =      self.numpy_to_pil(UpperCamelCase_							)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=UpperCamelCase_							)






import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase       =							'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase       =							direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCAmelCase       =							transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCAmelCase       =							re.compile(R'\[(.+?)\]\((https://huggingface\.co/.+?)\)')


lowerCAmelCase       =							{
    'DecisionTransformerConfig',
    'EncoderDecoderConfig',
    'MusicgenConfig',
    'RagConfig',
    'SpeechEncoderDecoderConfig',
    'TimmBackboneConfig',
    'VisionEncoderDecoderConfig',
    'VisionTextDualEncoderConfig',
    'LlamaConfig',
}
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      None

		# source code of `config_class`
		lowercase__            =      inspect.getsource(SCREAMING_SNAKE_CASE     )
		lowercase__            =      _re_checkpoint.findall(SCREAMING_SNAKE_CASE     )

		# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
		# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
		for ckpt_name, ckpt_link in checkpoints:
				# allow the link to end with `/`
				if ckpt_link.endswith('''/'''     ):
						lowercase__            =      ckpt_link[:-1]

				# verify the checkpoint name corresponds to the checkpoint link
				lowercase__            =      f'https://huggingface.co/{ckpt_name}'
				if ckpt_link == ckpt_link_from_name:
						lowercase__            =      ckpt_name
						break

		return checkpoint
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      []

		for config_class in list(CONFIG_MAPPING.values()     ):
				# Skip deprecated models
				if "models.deprecated" in config_class.__module__:
						continue
				lowercase__            =      get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE     )

				lowercase__            =      config_class.__name__
				if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
						configs_without_checkpoint.append(SCREAMING_SNAKE_CASE     )

		if len(SCREAMING_SNAKE_CASE     ) > 0:
				lowercase__            =      '''\n'''.join(sorted(SCREAMING_SNAKE_CASE     )     )
				raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}'     )


if __name__ == "__main__":
			check_config_docstrings_have_checkpoints()


