


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,
)


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

__SCREAMING_SNAKE_CASE	:							Tuple           =  '''
    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 		snake_case_   (				lowercase__     :    Union[str, Any]       ,     lowercase__     :    List[Any]       ,     lowercase__     :    Optional[Any]=8     ):






				'''simple docstring'''






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

def 		snake_case_   (				lowercase__     :    List[Any]       ,     lowercase__     :    Union[str, Any]=5_12       ,     lowercase__     :    List[str]=5_12     ):






				'''simple docstring'''






				_lowerCAmelCase  						=pil_image.resize((w, h)       ,     resample=Image.BICUBIC       ,     reducing_gap=1     )
				_lowerCAmelCase  						=np.array(pil_image.convert("""RGB"""     )     )
				_lowerCAmelCase  						=arr.astype(np.floataa     ) / 1_2_7.5 - 1
				_lowerCAmelCase  						=np.transpose(lowercase__       ,     [2, 0, 1]     )
				_lowerCAmelCase  						=torch.from_numpy(lowercase__     ).unsqueeze(0     )
				return image





class        __lowerCamelCase      (				lowerCamelCase_ ):


				"""simple docstring"""







				def __init__(				self     :    Optional[int]	,							lowerCamelCase_     :    UNetaDConditionModel	,							lowerCamelCase_     :    DDPMScheduler	,							lowerCamelCase_     :    VQModel	,							):
								super().__init__()

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






				def        lowerCAmelCase__			(				self     :    List[Any]	,							lowerCamelCase_     :    List[str]	,							lowerCamelCase_     :    Union[str, Any]	,							lowerCamelCase_     :    str					):
								# get the original timestep using init_timestep
								_lowerCAmelCase  						=min(int(num_inference_steps * strength					)	,							lowerCamelCase_					)

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

								return timesteps, num_inference_steps - t_start






				def        lowerCAmelCase__			(				self     :    Optional[Any]	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    Dict	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    List[str]	,							lowerCamelCase_     :    List[str]=None					):
								if not isinstance(lowerCamelCase_	,							(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(lowerCamelCase_					)}"					)

								_lowerCAmelCase  						=image.to(device=lowerCamelCase_	,							dtype=lowerCamelCase_					)

								_lowerCAmelCase  						=batch_size * num_images_per_prompt

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

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

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

												_lowerCAmelCase  						=self.movq.config.scaling_factor * init_latents

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

								_lowerCAmelCase  						=init_latents.shape
								_lowerCAmelCase  						=randn_tensor(lowerCamelCase_	,							generator=lowerCamelCase_	,							device=lowerCamelCase_	,							dtype=lowerCamelCase_					)

								# get latents
								_lowerCAmelCase  						=self.scheduler.add_noise(lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_					)

								_lowerCAmelCase  						=init_latents

								return latents






				def        lowerCAmelCase__			(				self     :    Any	,							lowerCamelCase_     :    List[Any]=0					):
								if is_accelerate_available():
												from accelerate import cpu_offload
								else:
												raise ImportError("""Please install accelerate via `pip install accelerate`"""					)

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

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






				def        lowerCAmelCase__			(				self     :    int	,							lowerCamelCase_     :    Dict=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."""					)

								_lowerCAmelCase  						=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)

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

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






				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def        lowerCAmelCase__			(				self     :    Dict					):
								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     :    Union[str, Any]	,							lowerCamelCase_     :    Union[torch.FloatTensor, List[torch.FloatTensor]]	,							lowerCamelCase_     :    Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]	,							lowerCamelCase_     :    Union[torch.FloatTensor, List[torch.FloatTensor]]	,							lowerCamelCase_     :    torch.FloatTensor	,							lowerCamelCase_     :    int = 512	,							lowerCamelCase_     :    int = 512	,							lowerCamelCase_     :    int = 100	,							lowerCamelCase_     :    float = 4.0	,							lowerCamelCase_     :    float = 0.3	,							lowerCamelCase_     :    int = 1	,							lowerCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None	,							lowerCamelCase_     :    Optional[str] = "pil"	,							lowerCamelCase_     :    bool = True	,							):
								_lowerCAmelCase  						=self._execution_device

								_lowerCAmelCase  						=guidance_scale > 1.0

								if isinstance(lowerCamelCase_	,							lowerCamelCase_					):
												_lowerCAmelCase  						=torch.cat(lowerCamelCase_	,							dim=0					)
								if isinstance(lowerCamelCase_	,							lowerCamelCase_					):
												_lowerCAmelCase  						=torch.cat(lowerCamelCase_	,							dim=0					)
								if isinstance(lowerCamelCase_	,							lowerCamelCase_					):
												_lowerCAmelCase  						=torch.cat(lowerCamelCase_	,							dim=0					)

								_lowerCAmelCase  						=image_embeds.shape[0]

								if do_classifier_free_guidance:
												_lowerCAmelCase  						=image_embeds.repeat_interleave(lowerCamelCase_	,							dim=0					)
												_lowerCAmelCase  						=negative_image_embeds.repeat_interleave(lowerCamelCase_	,							dim=0					)
												_lowerCAmelCase  						=hint.repeat_interleave(lowerCamelCase_	,							dim=0					)

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

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

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

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

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

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

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

												_lowerCAmelCase  						=self.scheduler.step(
												    lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_	,							generator=lowerCamelCase_	,							)[0]

								# post-processing
								_lowerCAmelCase  						=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"]:
												_lowerCAmelCase  						=image * 0.5 + 0.5
												_lowerCAmelCase  						=image.clamp(0	,							1					)
												_lowerCAmelCase  						=image.cpu().permute(0	,							2	,							3	,							1					).float().numpy()

								if output_type == "pil":
												_lowerCAmelCase  						=self.numpy_to_pil(lowerCamelCase_					)

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=lowerCamelCase_					)





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__SCREAMING_SNAKE_CASE	:							str           =  {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Any           =  ['''ErnieMTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  [
		    '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ErnieMForMultipleChoice''',
		    '''ErnieMForQuestionAnswering''',
		    '''ErnieMForSequenceClassification''',
		    '''ErnieMForTokenClassification''',
		    '''ErnieMModel''',
		    '''ErnieMPreTrainedModel''',
		    '''ErnieMForInformationExtraction''',
		]


if TYPE_CHECKING:
		from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ernie_m import ErnieMTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ernie_m import (
				    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ErnieMForInformationExtraction,
				    ErnieMForMultipleChoice,
				    ErnieMForQuestionAnswering,
				    ErnieMForSequenceClassification,
				    ErnieMForTokenClassification,
				    ErnieMModel,
				    ErnieMPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE	:							str           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

