



"""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,
)


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

SCREAMING_SNAKE_CASE_				= """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> 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\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=8	)			->      List[str]:
		a_     :      Optional[Any] 				=     height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		a_     :      List[str] 				=     width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor




class 			snake_case_						(						a_				):






							def __init__(   self			,     a_			,     a_			,     a_			,     ):
									super().__init__()

									self.register_modules(
									    unet=a_			,     scheduler=a_			,     movq=a_			,     )
									a_     :      Optional[int] 				=     2 ** (len(self.movq.config.block_out_channels      ) - 1)






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_      ):
									if latents is None:
											a_     :      Optional[int] 				=     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}"""      )
											a_     :      Optional[int] 				=     latents.to(a_      )

									a_     :      List[Any] 				=     latents * scheduler.init_noise_sigma
									return latents






							def snake_case_  (   self			,     a_=0      ):
									if is_accelerate_available():
											from accelerate import cpu_offload
									else:
											raise ImportError("Please install accelerate via `pip install accelerate`"      )

									a_     :      str 				=     torch.device(F"""cuda:{gpu_id}"""      )

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






							def snake_case_  (   self			,     a_=0      ):
									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_     :      List[str] 				=     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)

									a_     :      Tuple 				=     None
									for cpu_offloaded_model in [self.unet, self.movq]:
											a_     :      Optional[Any] 				=     cpu_offload_with_hook(a_			,     a_			,     prev_module_hook=a_      )

									# We'll offload the last model manually.
									a_     :      List[str] 				=     hook






							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def snake_case_  (   self      ):
									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_			,     a_ = 5_1_2			,     a_ = 5_1_2			,     a_ = 1_0_0			,     a_ = 4.0			,     a_ = 1			,     a_ = None			,     a_ = None			,     a_ = "pil"			,     a_ = True			,     ):
									a_     :      Dict 				=     self._execution_device

									a_     :      List[str] 				=     guidance_scale > 1.0

									if isinstance(a_			,     a_      ):
											a_     :      Optional[int] 				=     torch.cat(a_			,     dim=0      )
									if isinstance(a_			,     a_      ):
											a_     :      Optional[Any] 				=     torch.cat(a_			,     dim=0      )
									if isinstance(a_			,     a_      ):
											a_     :      str 				=     torch.cat(a_			,     dim=0      )

									a_     :      str 				=     image_embeds.shape[0] * num_images_per_prompt

									if do_classifier_free_guidance:
											a_     :      Dict 				=     image_embeds.repeat_interleave(a_			,     dim=0      )
											a_     :      Dict 				=     negative_image_embeds.repeat_interleave(a_			,     dim=0      )
											a_     :      Dict 				=     hint.repeat_interleave(a_			,     dim=0      )

									a_     :      List[str] 				=     torch.cat([negative_image_embeds, image_embeds]			,     dim=0      ).to(dtype=self.unet.dtype			,     device=a_      )
									a_     :      Dict 				=     torch.cat([hint, hint]			,     dim=0      ).to(dtype=self.unet.dtype			,     device=a_      )
									self.scheduler.set_timesteps(a_			,     device=a_      )
									a_     :      List[str] 				=     self.scheduler.timesteps

									a_     :      Any 				=     self.movq.config.latent_channels

									a_     :      Tuple 				=     downscale_height_and_width(a_			,     a_			,     self.movq_scale_factor      )

									# create initial latent
									a_     :      str 				=     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
											a_     :      Any 				=     torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

											a_     :      Any 				=     {"image_embeds": image_embeds, "hint": hint}
											a_     :      Dict 				=     self.unet(
											    sample=a_			,     timestep=a_			,     encoder_hidden_states=a_			,     added_cond_kwargs=a_			,     return_dict=a_			,     )[0]

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

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

									if output_type == "pil":
											a_     :      int 				=     self.numpy_to_pil(a_      )

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=a_      )




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_				= {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)