
'''simple docstring'''



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,
)


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

_lowercase    =		"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

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

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""


def A				(__lowerCamelCase				:Tuple			,     __lowerCamelCase				:Any			,     __lowerCamelCase				:Dict=8		):
						_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 A				(__lowerCamelCase				:List[Any]			,     __lowerCamelCase				:Optional[Any]=512			,     __lowerCamelCase				:str=512		):
						_lowerCAmelCase  						=			pil_image.resize((w, h)			,     resample=Image.BICUBIC			,     reducing_gap=1		)
						_lowerCAmelCase  						=			np.array(pil_image.convert("""RGB"""		)		)
						_lowerCAmelCase  						=			arr.astype(np.floataa		) / 127.5 - 1
						_lowerCAmelCase  						=			np.transpose(__lowerCamelCase			,     [2, 0, 1]		)
						_lowerCAmelCase  						=			torch.from_numpy(__lowerCamelCase		).unsqueeze(0		)
						return image





class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       ):

												"""simple docstring"""



												super().__init__()

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




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			min(int(num_inference_steps * strength				)   ,       _lowercase				)

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

												return timesteps, num_inference_steps - t_start




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase=None				):

												"""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				)}'				)

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

												_lowerCAmelCase  						=			batch_size * num_images_per_prompt

												if image.shape[1] == 4:
																		_lowerCAmelCase  						=			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				):
																								_lowerCAmelCase  						=			[
																								    self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(_lowercase				)
																								]
																								_lowerCAmelCase  						=			torch.cat(_lowercase   ,       dim=0				)
																		else:
																								_lowerCAmelCase  						=			self.movq.encode(_lowercase				).latent_dist.sample(_lowercase				)

																		_lowerCAmelCase  						=			self.movq.config.scaling_factor * init_latents

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

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

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

												_lowerCAmelCase  						=			init_latents

												return latents




						def  _lowercase     ( self   ,       _lowercase=0				):

												"""simple docstring"""



												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(_lowercase   ,       _lowercase				)




						def  _lowercase     ( self   ,       _lowercase=0				):

												"""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."""				)

												_lowerCAmelCase  						=			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)

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

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




						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def  _lowercase     ( self				):

												"""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 = 512   ,       _lowercase = 512   ,       _lowercase = 100   ,       _lowercase = 4.0   ,       _lowercase = 0.3   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			self._execution_device

												_lowerCAmelCase  						=			guidance_scale > 1.0

												if isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			torch.cat(_lowercase   ,       dim=0				)
												_lowerCAmelCase  						=			image_embeds.shape[0]
												if isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			torch.cat(_lowercase   ,       dim=0				)

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

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

												if not isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			[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'				)

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

												_lowerCAmelCase  						=			self.movq.encode(_lowercase				)["""latents"""]
												_lowerCAmelCase  						=			latents.repeat_interleave(_lowercase   ,       dim=0				)
												self.scheduler.set_timesteps(_lowercase   ,       device=_lowercase				)
												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.get_timesteps(_lowercase   ,       _lowercase   ,       _lowercase				)
												_lowerCAmelCase  						=			timesteps[:1].repeat(batch_size * num_images_per_prompt				)
												_lowerCAmelCase						,   _lowerCAmelCase  						=			downscale_height_and_width(_lowercase   ,       _lowercase   ,       self.movq_scale_factor				)
												_lowerCAmelCase  						=			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
																		_lowerCAmelCase  						=			torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

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

												# post-processing
												_lowerCAmelCase  						=			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"]:
																		_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(_lowercase				)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_lowercase    =		{"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		["""DeiTFeatureExtractor"""]
						_lowercase    =		["""DeiTImageProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """DeiTForImageClassification""",
						    """DeiTForImageClassificationWithTeacher""",
						    """DeiTForMaskedImageModeling""",
						    """DeiTModel""",
						    """DeiTPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFDeiTForImageClassification""",
						    """TFDeiTForImageClassificationWithTeacher""",
						    """TFDeiTForMaskedImageModeling""",
						    """TFDeiTModel""",
						    """TFDeiTPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deit import DeiTFeatureExtractor
												from .image_processing_deit import DeiTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deit import (
												    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeiTForImageClassification,
												    DeiTForImageClassificationWithTeacher,
												    DeiTForMaskedImageModeling,
												    DeiTModel,
												    DeiTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deit import (
												    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDeiTForImageClassification,
												    TFDeiTForImageClassificationWithTeacher,
												    TFDeiTForMaskedImageModeling,
												    TFDeiTModel,
												    TFDeiTPreTrainedModel,
												)


else:
						import sys

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