

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> 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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.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)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

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



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,    SCREAMING_SNAKE_CASE_:							Dict		,    SCREAMING_SNAKE_CASE_:							str=8 )     -> Union[str, Any]:




				'''simple docstring'''





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



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Any		,    SCREAMING_SNAKE_CASE_:							int=5_1_2		,    SCREAMING_SNAKE_CASE_:							str=5_1_2 )     -> int:




				'''simple docstring'''





				A__            =     pil_image.resize((w, h)		,    resample=Image.BICUBIC		,    reducing_gap=1 )
				A__            =     np.array(pil_image.convert("RGB" ) )
				A__            =     arr.astype(np.floataa ) / 127.5 - 1
				A__            =     np.transpose(SCREAMING_SNAKE_CASE_		,    [2, 0, 1] )
				A__            =     torch.from_numpy(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 )
				return image





class    a__  (   snake_case     ):
			"""simple docstring"""







			def __init__(      self , lowercase , lowercase , lowercase , )					->  Any:
							'''simple docstring'''






							super().__init__()

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


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase    )					->  List[str]:
							'''simple docstring'''






							A__            =     min(int(num_inference_steps * strength    ) , lowercase    )

							A__            =     max(num_inference_steps - init_timestep , 0    )
							A__            =     self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=None    )					->  Any:
							'''simple docstring'''






							if not isinstance(lowercase , (torch.Tensor, PIL.Image.Image, list)    ):
											raise ValueError(
											    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase    )}'    )

							A__            =     image.to(device=lowercase , dtype=lowercase    )

							A__            =     batch_size * num_images_per_prompt

							if image.shape[1] == 4:
											A__            =     image

							else:
											if isinstance(lowercase , lowercase    ) and len(lowercase    ) != batch_size:
															raise ValueError(
															    F'You have passed a list of generators of length {len(lowercase    )}, but requested an effective batch'
															    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'    )

											elif isinstance(lowercase , lowercase    ):
															A__            =     [
															    self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(lowercase    )
															]
															A__            =     torch.cat(lowercase , dim=0    )
											else:
															A__            =     self.movq.encode(lowercase    ).latent_dist.sample(lowercase    )

											A__            =     self.movq.config.scaling_factor * init_latents

							A__            =     torch.cat([init_latents] , dim=0    )

							A__            =     init_latents.shape
							A__            =     randn_tensor(lowercase , generator=lowercase , device=lowercase , dtype=lowercase    )

							# get latents
							A__            =     self.scheduler.add_noise(lowercase , lowercase , lowercase    )

							A__            =     init_latents

							return latents


			def        UpperCamelCase						(      self , lowercase=0    )					->  Tuple:
							'''simple docstring'''






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

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

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


			def        UpperCamelCase						(      self , lowercase=0    )					->  Optional[Any]:
							'''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__            =     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__            =     None
							for cpu_offloaded_model in [self.unet, self.movq]:
											A__      ,			A__            =     cpu_offload_with_hook(lowercase , lowercase , prev_module_hook=lowercase    )

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


			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def        UpperCamelCase						(      self    )					->  str:
							'''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 , lowercase , lowercase , lowercase , lowercase , lowercase = 512 , lowercase = 512 , lowercase = 100 , lowercase = 4.0 , lowercase = 0.3 , lowercase = 1 , lowercase = None , lowercase = "pil" , lowercase = True , )					->  Union[str, Any]:
							'''simple docstring'''






							A__            =     self._execution_device

							A__            =     guidance_scale > 1.0

							if isinstance(lowercase , lowercase    ):
											A__            =     torch.cat(lowercase , dim=0    )
							if isinstance(lowercase , lowercase    ):
											A__            =     torch.cat(lowercase , dim=0    )
							if isinstance(lowercase , lowercase    ):
											A__            =     torch.cat(lowercase , dim=0    )

							A__            =     image_embeds.shape[0]

							if do_classifier_free_guidance:
											A__            =     image_embeds.repeat_interleave(lowercase , dim=0    )
											A__            =     negative_image_embeds.repeat_interleave(lowercase , dim=0    )
											A__            =     hint.repeat_interleave(lowercase , dim=0    )

							A__            =     torch.cat([negative_image_embeds, image_embeds] , dim=0    ).to(dtype=self.unet.dtype , device=lowercase    )
							A__            =     torch.cat([hint, hint] , dim=0    ).to(dtype=self.unet.dtype , device=lowercase    )

							if not isinstance(lowercase , lowercase    ):
											A__            =     [image]
							if not all(isinstance(lowercase , (PIL.Image.Image, torch.Tensor)    ) for i in image    ):
											raise ValueError(
											    F'Input is in incorrect format: {[type(lowercase    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'    )

							A__            =     torch.cat([prepare_image(lowercase , lowercase , lowercase    ) for i in image] , dim=0    )
							A__            =     image.to(dtype=image_embeds.dtype , device=lowercase    )

							A__            =     self.movq.encode(lowercase    )["latents"]
							A__            =     latents.repeat_interleave(lowercase , dim=0    )
							self.scheduler.set_timesteps(lowercase , device=lowercase    )
							A__      ,			A__            =     self.get_timesteps(lowercase , lowercase , lowercase    )
							A__            =     timesteps[:1].repeat(batch_size * num_images_per_prompt    )
							A__      ,			A__            =     downscale_height_and_width(lowercase , lowercase , self.movq_scale_factor    )
							A__            =     self.prepare_latents(
							    lowercase , lowercase , lowercase , lowercase , image_embeds.dtype , lowercase , lowercase    )
							for i, t in enumerate(self.progress_bar(lowercase    )    ):
											# expand the latents if we are doing classifier free guidance
											A__            =     torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

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

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

											# compute the previous noisy sample x_t -> x_t-1

											A__            =     self.scheduler.step(
											    lowercase , lowercase , lowercase , generator=lowercase , )[0]

							# post-processing
							A__            =     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__            =     image * 0.5 + 0.5
											A__            =     image.clamp(0 , 1    )
											A__            =     image.cpu().permute(0 , 2 , 3 , 1    ).float().numpy()

							if output_type == "pil":
											A__            =     self.numpy_to_pil(lowercase    )

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=lowercase    )



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__						=	logging.get_logger(__name__)

lowerCAmelCase__						=	{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'swiftformer'


			def __init__(      self , lowercase=3 , lowercase=[3, 3, 6, 4] , lowercase=[48, 56, 112, 220] , lowercase=4 , lowercase=[True, True, True, True] , lowercase="gelu" , lowercase=3 , lowercase=2 , lowercase=1 , lowercase=0.0 , lowercase=True , lowercase=1e-5 , lowercase=1e-5 , **lowercase , )					->  Optional[int]:
							'''simple docstring'''






							super().__init__(**lowercase    )
							A__            =     num_channels
							A__            =     depths
							A__            =     embed_dims
							A__            =     mlp_ratio
							A__            =     downsamples
							A__            =     hidden_act
							A__            =     down_patch_size
							A__            =     down_stride
							A__            =     down_pad
							A__            =     drop_path_rate
							A__            =     use_layer_scale
							A__            =     layer_scale_init_value
							A__            =     batch_norm_eps

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    version.parse('1.11'     )


			@property
			def        UpperCamelCase						(      self    )					->  Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]    )


			@property
			def        UpperCamelCase						(      self    )					->  float:
							'''simple docstring'''






							return 1e-4

