


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        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_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:     Optional[Any]   ,						UpperCamelCase_:     UNetaDConditionModel   ,						UpperCamelCase_:     DDPMScheduler   ,						UpperCamelCase_:     VQModel   ,						)      ->       Union[str, Any]:





									"""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[Any]   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     List[str]							)      ->       List[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:     int   ,						UpperCamelCase_:     List[str]=0							)      ->       Tuple:





									"""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:     Optional[Any]   ,						UpperCamelCase_:     List[str]=0							)      ->       Dict:





									"""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:     Any							)      ->       Dict:





									"""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:     List[str]   ,						UpperCamelCase_:     Union[torch.FloatTensor, List[torch.FloatTensor]]   ,						UpperCamelCase_:     Union[torch.FloatTensor, List[torch.FloatTensor]]   ,						UpperCamelCase_:     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   ,						)      ->       str:





									"""simple docstring"""







									lowercase__						=				self._execution_device

									lowercase__						=				guidance_scale > 1.0

									if isinstance(UpperCamelCase_   ,						UpperCamelCase_							):
											lowercase__						=				torch.cat(UpperCamelCase_   ,						dim=0							)
									if isinstance(UpperCamelCase_   ,						UpperCamelCase_							):
											lowercase__						=				torch.cat(UpperCamelCase_   ,						dim=0							)
									if isinstance(UpperCamelCase_   ,						UpperCamelCase_							):
											lowercase__						=				torch.cat(UpperCamelCase_   ,						dim=0							)

									lowercase__						=				image_embeds.shape[0] * num_images_per_prompt

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

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

									lowercase__						=				self.movq.config.latent_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, '''hint''': hint}
											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 copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      int  				=			'''git_vision_model'''

							def __init__(							self:     Tuple   ,						UpperCamelCase_:     Optional[int]=768   ,						UpperCamelCase_:     Optional[int]=3_072   ,						UpperCamelCase_:     Optional[int]=12   ,						UpperCamelCase_:     Union[str, Any]=12   ,						UpperCamelCase_:     Any=3   ,						UpperCamelCase_:     str=224   ,						UpperCamelCase_:     int=16   ,						UpperCamelCase_:     Any="quick_gelu"   ,						UpperCamelCase_:     Union[str, Any]=1E-5   ,						UpperCamelCase_:     Tuple=0.0   ,						UpperCamelCase_:     Tuple=0.02   ,						**UpperCamelCase_:     str   ,						)      ->       Optional[int]:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)

									lowercase__						=				hidden_size
									lowercase__						=				intermediate_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				num_channels
									lowercase__						=				patch_size
									lowercase__						=				image_size
									lowercase__						=				initializer_range
									lowercase__						=				attention_dropout
									lowercase__						=				layer_norm_eps
									lowercase__						=				hidden_act


							@classmethod
							def 				lowerCamelCase_			(							cls:     Optional[Any]   ,						UpperCamelCase_:     Union[str, os.PathLike]   ,						**UpperCamelCase_:     List[Any]							)      ->       "PretrainedConfig":





									"""simple docstring"""







									cls._set_token_in_kwargs(UpperCamelCase_							)

									lowercase__					,		lowercase__						=				cls.get_config_dict(UpperCamelCase_   ,						**UpperCamelCase_							)

									# get the vision config dict if we are loading from GITConfig
									if config_dict.get('''model_type'''							) == "git":
											lowercase__						=				config_dict['''vision_config''']

									if "model_type" in config_dict and hasattr(cls   ,						'''model_type'''							) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
											    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'							)

									return cls.from_dict(UpperCamelCase_   ,						**UpperCamelCase_							)


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      Optional[int]  				=			'''git'''

							def __init__(							self:     Optional[Any]   ,						UpperCamelCase_:     Optional[Any]=None   ,						UpperCamelCase_:     List[str]=30_522   ,						UpperCamelCase_:     List[Any]=768   ,						UpperCamelCase_:     List[Any]=6   ,						UpperCamelCase_:     Union[str, Any]=12   ,						UpperCamelCase_:     Any=3_072   ,						UpperCamelCase_:     Any="gelu"   ,						UpperCamelCase_:     List[Any]=0.1   ,						UpperCamelCase_:     Dict=0.1   ,						UpperCamelCase_:     str=1_024   ,						UpperCamelCase_:     Any=0.02   ,						UpperCamelCase_:     str=1E-1_2   ,						UpperCamelCase_:     Optional[int]=0   ,						UpperCamelCase_:     Optional[Any]="absolute"   ,						UpperCamelCase_:     Optional[int]=True   ,						UpperCamelCase_:     List[Any]=False   ,						UpperCamelCase_:     List[Any]=101   ,						UpperCamelCase_:     int=102   ,						UpperCamelCase_:     List[str]=None   ,						**UpperCamelCase_:     int   ,						)      ->       Union[str, Any]:





									"""simple docstring"""







									super().__init__(bos_token_id=UpperCamelCase_   ,						eos_token_id=UpperCamelCase_   ,						pad_token_id=UpperCamelCase_   ,						**UpperCamelCase_							)

									if vision_config is None:
											lowercase__						=				{}
											logger.info('''vision_config is None. initializing the GitVisionConfig with default values.'''							)

									lowercase__						=				GitVisionConfig(**UpperCamelCase_							)
									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				hidden_act
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				initializer_range
									lowercase__						=				layer_norm_eps
									lowercase__						=				position_embedding_type
									lowercase__						=				use_cache
									lowercase__						=				tie_word_embeddings
									lowercase__						=				num_image_with_embedding

									lowercase__						=				bos_token_id
									lowercase__						=				eos_token_id


							def 				lowerCamelCase_			(							self:     Dict							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				copy.deepcopy(self.__dict__							)
									lowercase__						=				self.vision_config.to_dict()
									lowercase__						=				self.__class__.model_type
									return output


