

import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :      Optional[int]			 =logging.get_logger(__name__)

_lowerCAmelCase  :      Optional[Any]			 ={
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

_lowerCAmelCase  :      str			 ={
    """b0""": {
        """hidden_dim""": 12_80,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 2_24,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 12_80,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 2_40,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 14_08,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 2_60,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 15_36,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 3_00,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 17_92,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 3_80,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 20_48,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 4_56,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 23_04,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 5_28,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 25_60,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 6_00,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[Any]									= EfficientNetConfig()
	UpperCAmelCase__:	List[str]									= CONFIG_MAP[model_name]["hidden_dim"]
	UpperCAmelCase__:	List[Any]									= CONFIG_MAP[model_name]["width_coef"]
	UpperCAmelCase__:	Any									= CONFIG_MAP[model_name]["depth_coef"]
	UpperCAmelCase__:	Dict									= CONFIG_MAP[model_name]["image_size"]
	UpperCAmelCase__:	List[Any]									= CONFIG_MAP[model_name]["dropout_rate"]
	UpperCAmelCase__:	Dict									= CONFIG_MAP[model_name]["dw_padding"]

	UpperCAmelCase__:	Tuple									= "huggingface/label-files"
	UpperCAmelCase__:	Optional[Any]									= "imagenet-1k-id2label.json"
	UpperCAmelCase__:	str									= 1_0_0_0
	UpperCAmelCase__:	List[str]									= json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,repo_type="dataset"      )	,"r"      )      )
	UpperCAmelCase__:	int									= {int(SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}

	UpperCAmelCase__:	Dict									= idalabel
	UpperCAmelCase__:	str									= {v: k for k, v in idalabel.items()}
	return config



def 			_A	(      ):
	UpperCAmelCase__:	Any									= "http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase__:	Union[str, Any]									= Image.open(requests.get(SCREAMING_SNAKE_CASE	,stream=SCREAMING_SNAKE_CASE      ).raw      )
	return im



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= CONFIG_MAP[model_name]["image_size"]
	UpperCAmelCase__:	List[str]									= EfficientNetImageProcessor(
	    size={"height": size, "width": size}	,image_mean=[0.4_85, 0.4_56, 0.4_06]	,image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]	,do_center_crop=SCREAMING_SNAKE_CASE	,)
	return preprocessor



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Union[str, Any]									= [v.split("_"      )[0].split("block"      )[1] for v in original_param_names if v.startswith("block"      )]
	UpperCAmelCase__:	Dict									= sorted(set(SCREAMING_SNAKE_CASE      )      )
	UpperCAmelCase__:	List[str]									= len(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	int									= {b: str(SCREAMING_SNAKE_CASE      ) for b, i in zip(SCREAMING_SNAKE_CASE	,range(SCREAMING_SNAKE_CASE      )      )}

	UpperCAmelCase__:	int									= []
	rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")      )
	rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")      )
	rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")      )
	rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")      )
	rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")      )

	for b in block_names:
		UpperCAmelCase__:	Union[str, Any]									= block_name_mapping[b]
		rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight")      )
		rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight")      )
		rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias")      )
		rename_keys.append(
		    (f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")      )
		rename_keys.append(
		    (f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")      )
		rename_keys.append(
		    (f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")      )
		rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight")      )
		rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias")      )
		rename_keys.append(
		    (f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")      )
		rename_keys.append(
		    (f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")      )

		rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight")      )
		rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias")      )
		rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight")      )
		rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias")      )
		rename_keys.append(
		    (f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight")      )
		rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight")      )
		rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias")      )
		rename_keys.append(
		    (f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean")      )
		rename_keys.append(
		    (f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var")      )

	rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight")      )
	rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight")      )
	rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias")      )
	rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean")      )
	rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var")      )

	UpperCAmelCase__:	List[str]									= {}
	for item in rename_keys:
		if item[0] in original_param_names:
			UpperCAmelCase__:	List[str]									= "efficientnet." + item[1]

	UpperCAmelCase__:	int									= "classifier.weight"
	UpperCAmelCase__:	Optional[Any]									= "classifier.bias"
	return key_mapping



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	for key, value in tf_params.items():
		if "normalization" in key:
			continue

		UpperCAmelCase__:	str									= key_mapping[key]
		if "_conv" in key and "kernel" in key:
			UpperCAmelCase__:	int									= torch.from_numpy(SCREAMING_SNAKE_CASE      ).permute(3	,2	,0	,1      )
		elif "depthwise_kernel" in key:
			UpperCAmelCase__:	Optional[Any]									= torch.from_numpy(SCREAMING_SNAKE_CASE      ).permute(2	,3	,0	,1      )
		elif "kernel" in key:
			UpperCAmelCase__:	List[Any]									= torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE      )      )
		else:
			UpperCAmelCase__:	int									= torch.from_numpy(SCREAMING_SNAKE_CASE      )

		# Replace HF parameters with original TF model parameters
		assert hf_params[hf_key].shape == new_hf_value.shape
		hf_params[hf_key].copy_(SCREAMING_SNAKE_CASE      )



@torch.no_grad()
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= model_classes[model_name](
	    include_top=SCREAMING_SNAKE_CASE	,weights="imagenet"	,input_tensor=SCREAMING_SNAKE_CASE	,input_shape=SCREAMING_SNAKE_CASE	,pooling=SCREAMING_SNAKE_CASE	,classes=1_0_0_0	,classifier_activation="softmax"	,)

	UpperCAmelCase__:	List[str]									= original_model.trainable_variables
	UpperCAmelCase__:	List[str]									= original_model.non_trainable_variables
	UpperCAmelCase__:	Any									= {param.name: param.numpy() for param in tf_params}
	for param in tf_non_train_params:
		UpperCAmelCase__:	Optional[Any]									= param.numpy()
	UpperCAmelCase__:	int									= list(tf_params.keys()      )

	# Load HuggingFace model
	UpperCAmelCase__:	str									= get_efficientnet_config(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[Any]									= EfficientNetForImageClassification(SCREAMING_SNAKE_CASE      ).eval()
	UpperCAmelCase__:	int									= hf_model.state_dict()

	# Create src-to-dst parameter name mapping dictionary
	print("Converting parameters..."      )
	UpperCAmelCase__:	int									= rename_keys(SCREAMING_SNAKE_CASE      )
	replace_params(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

	# Initialize preprocessor and preprocess input image
	UpperCAmelCase__:	Optional[int]									= convert_image_processor(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[Any]									= preprocessor(images=prepare_img()	,return_tensors="pt"      )

	# HF model inference
	hf_model.eval()
	with torch.no_grad():
		UpperCAmelCase__:	Union[str, Any]									= hf_model(**SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Union[str, Any]									= outputs.logits.detach().numpy()

	# Original model inference
	UpperCAmelCase__:	Tuple									= False
	UpperCAmelCase__:	List[Any]									= CONFIG_MAP[model_name]["image_size"]
	UpperCAmelCase__:	str									= prepare_img().resize((image_size, image_size)	,resample=PIL.Image.NEAREST      )
	UpperCAmelCase__:	Any									= image.img_to_array(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Dict									= np.expand_dims(SCREAMING_SNAKE_CASE	,axis=0      )
	UpperCAmelCase__:	int									= original_model.predict(SCREAMING_SNAKE_CASE      )

	# Check whether original and HF model outputs match  -> np.allclose
	assert np.allclose(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,atol=1e-3      ), "The predicted logits are not the same."
	print("Model outputs match!"      )

	if save_model:
		# Create folder to save model
		if not os.path.isdir(SCREAMING_SNAKE_CASE      ):
			os.mkdir(SCREAMING_SNAKE_CASE      )
		# Save converted model and image processor
		hf_model.save_pretrained(SCREAMING_SNAKE_CASE      )
		preprocessor.save_pretrained(SCREAMING_SNAKE_CASE      )

	if push_to_hub:
		# Push model and image processor to hub
		print(f"Pushing converted {model_name} to the hub..."      )
		UpperCAmelCase__:	List[str]									= f"efficientnet-{model_name}"
		preprocessor.push_to_hub(SCREAMING_SNAKE_CASE      )
		hf_model.push_to_hub(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
					_lowerCAmelCase  :      Any			 =argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""b0""",
					    type=str,
					    help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""hf_model""",
					    type=str,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
					parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

					_lowerCAmelCase  :      List[str]			 =parser.parse_args()
					convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


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

_lowerCAmelCase  :      Any			 ="""
    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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=8      ):
	UpperCAmelCase__:	int									= height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	UpperCAmelCase__:	Optional[Any]									= width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



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

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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if latents is None:
							UpperCAmelCase__:	Any									= randn_tensor(lowerCamelCase__				,      generator=lowerCamelCase__				,      device=lowerCamelCase__				,      dtype=lowerCamelCase__						)
						else:
							if latents.shape != shape:
								raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"						)
							UpperCAmelCase__:	Tuple									= latents.to(lowerCamelCase__						)

						UpperCAmelCase__:	Union[str, Any]									= latents * scheduler.init_noise_sigma
						return latents





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

						UpperCAmelCase__:	Any									= torch.device(F"cuda:{gpu_id}"						)

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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__=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."						)

						UpperCAmelCase__:	Union[str, Any]									= torch.device(F"cuda:{gpu_id}"						)

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

						UpperCAmelCase__:	int									= None
						for cpu_offloaded_model in [self.unet, self.movq]:
							UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= cpu_offload_with_hook(lowerCamelCase__				,      lowerCamelCase__				,      prev_module_hook=lowerCamelCase__						)

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





					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def    _UpperCAmelCase    (				self						):
						if not hasattr(self.unet				,      "_hf_hook"						):
							return self.device
						for module in self.unet.modules():
							if (
							    hasattr(lowerCamelCase__				,      "_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(lowerCamelCase__						)
					def __call__(				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = 5_1_2				,      lowerCamelCase__ = 5_1_2				,      lowerCamelCase__ = 1_0_0				,      lowerCamelCase__ = 4.0				,      lowerCamelCase__ = 1				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "pil"				,      lowerCamelCase__ = True				,      ):
						UpperCAmelCase__:	str									= self._execution_device

						UpperCAmelCase__:	List[Any]									= guidance_scale > 1.0

						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	List[Any]									= torch.cat(lowerCamelCase__				,      dim=0						)
						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	List[Any]									= torch.cat(lowerCamelCase__				,      dim=0						)
						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	Any									= torch.cat(lowerCamelCase__				,      dim=0						)

						UpperCAmelCase__:	Dict									= image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
							UpperCAmelCase__:	Tuple									= image_embeds.repeat_interleave(lowerCamelCase__				,      dim=0						)
							UpperCAmelCase__:	Union[str, Any]									= negative_image_embeds.repeat_interleave(lowerCamelCase__				,      dim=0						)
							UpperCAmelCase__:	Any									= hint.repeat_interleave(lowerCamelCase__				,      dim=0						)

						UpperCAmelCase__:	Union[str, Any]									= torch.cat([negative_image_embeds, image_embeds]				,      dim=0						).to(dtype=self.unet.dtype				,      device=lowerCamelCase__						)
						UpperCAmelCase__:	Dict									= torch.cat([hint, hint]				,      dim=0						).to(dtype=self.unet.dtype				,      device=lowerCamelCase__						)
						self.scheduler.set_timesteps(lowerCamelCase__				,      device=lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= self.scheduler.timesteps

						UpperCAmelCase__:	Optional[int]									= self.movq.config.latent_channels

						UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= downscale_height_and_width(lowerCamelCase__				,      lowerCamelCase__				,      self.movq_scale_factor						)

						# create initial latent
						UpperCAmelCase__:	Any									= self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)				,      image_embeds.dtype				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      self.scheduler				,      )

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

							UpperCAmelCase__:	List[str]									= {"image_embeds": image_embeds, "hint": hint}
							UpperCAmelCase__:	Any									= self.unet(
							    sample=lowerCamelCase__				,      timestep=lowerCamelCase__				,      encoder_hidden_states=lowerCamelCase__				,      added_cond_kwargs=lowerCamelCase__				,      return_dict=lowerCamelCase__				,      )[0]

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

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

						if output_type == "pil":
							UpperCAmelCase__:	List[str]									= self.numpy_to_pil(lowerCamelCase__						)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=lowerCamelCase__						)