




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      :				Union[str, Any]				      =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      lowerCAmelCase_     (					lowerCamelCase:    List[str]       ,			lowerCamelCase:    Any       ,			lowerCamelCase:    List[Any]=8  )   ->     str:


						'''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      lowerCAmelCase_     (					lowerCamelCase:    Tuple       ,			lowerCamelCase:    int=5_12       ,			lowerCamelCase:    Optional[Any]=5_12  )   ->     List[Any]:


						'''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  ) / 127.5 - 1
						lowerCamelCase__ 					=			np.transpose(lowerCamelCase       ,			[2, 0, 1]  )
						lowerCamelCase__ 					=			torch.from_numpy(lowerCamelCase  ).unsqueeze(0  )
						return image






class      A     (						lowerCAmelCase__						):
					"""simple docstring"""

					def __init__(      self					:				List[str]		,					A__					:				UNetaDConditionModel		,					A__					:				DDPMScheduler		,					A__					:				VQModel		,					)			->    Dict:
											super().__init__()

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




					def 	__snake_case				(      self					:				List[str]		,					A__					:				Dict		,					A__					:				int		,					A__					:				Tuple     )			->    int:
											lowerCamelCase__ 					=			min(int(num_inference_steps * strength     )		,					_A     )

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

											return timesteps, num_inference_steps - t_start




					def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				Tuple		,					A__					:				List[str]		,					A__					:				List[str]		,					A__					:				Optional[int]		,					A__					:				Optional[int]		,					A__					:				List[Any]		,					A__					:				int=None     )			->    List[str]:




											if not isinstance(_A		,					(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(_A     )}"""     )

											lowerCamelCase__ 					=			image.to(device=_A		,					dtype=_A     )

											lowerCamelCase__ 					=			batch_size * num_images_per_prompt

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

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

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

																	lowerCamelCase__ 					=			self.movq.config.scaling_factor * init_latents

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

											lowerCamelCase__ 					=			init_latents.shape
											lowerCamelCase__ 					=			randn_tensor(_A		,					generator=_A		,					device=_A		,					dtype=_A     )

											# get latents
											lowerCamelCase__ 					=			self.scheduler.add_noise(_A		,					_A		,					_A     )

											lowerCamelCase__ 					=			init_latents

											return latents




					def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				str=0     )			->    Optional[int]:




											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(_A		,					_A     )




					def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Optional[int]=0     )			->    Optional[Any]:




											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=_A     )
																	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__ 					=			cpu_offload_with_hook(_A		,					_A		,					prev_module_hook=_A     )

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




					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 	__snake_case				(      self					:				List[str]     )			->    List[Any]:




											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					:				List[str]		,					A__					:				Union[torch.FloatTensor, List[torch.FloatTensor]]		,					A__					:				Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]		,					A__					:				Union[torch.FloatTensor, List[torch.FloatTensor]]		,					A__					:				torch.FloatTensor		,					A__					:				int = 5_1_2		,					A__					:				int = 5_1_2		,					A__					:				int = 1_0_0		,					A__					:				float = 4.0		,					A__					:				float = 0.3		,					A__					:				int = 1		,					A__					:				Optional[Union[torch.Generator, List[torch.Generator]]] = None		,					A__					:				Optional[str] = "pil"		,					A__					:				bool = True		,					)			->    Dict:
											lowerCamelCase__ 					=			self._execution_device

											lowerCamelCase__ 					=			guidance_scale > 1.0

											if isinstance(_A		,					_A     ):
																	lowerCamelCase__ 					=			torch.cat(_A		,					dim=0     )
											if isinstance(_A		,					_A     ):
																	lowerCamelCase__ 					=			torch.cat(_A		,					dim=0     )
											if isinstance(_A		,					_A     ):
																	lowerCamelCase__ 					=			torch.cat(_A		,					dim=0     )

											lowerCamelCase__ 					=			image_embeds.shape[0]

											if do_classifier_free_guidance:
																	lowerCamelCase__ 					=			image_embeds.repeat_interleave(_A		,					dim=0     )
																	lowerCamelCase__ 					=			negative_image_embeds.repeat_interleave(_A		,					dim=0     )
																	lowerCamelCase__ 					=			hint.repeat_interleave(_A		,					dim=0     )

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

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

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

											lowerCamelCase__ 					=			self.movq.encode(_A     )['''latents''']
											lowerCamelCase__ 					=			latents.repeat_interleave(_A		,					dim=0     )
											self.scheduler.set_timesteps(_A		,					device=_A     )
											lowerCamelCase__ 					=			self.get_timesteps(_A		,					_A		,					_A     )
											lowerCamelCase__ 					=			timesteps[:1].repeat(batch_size * num_images_per_prompt     )
											lowerCamelCase__ 					=			downscale_height_and_width(_A		,					_A		,					self.movq_scale_factor     )
											lowerCamelCase__ 					=			self.prepare_latents(
											    _A		,					_A		,					_A		,					_A		,					image_embeds.dtype		,					_A		,					_A     )
											for i, t in enumerate(self.progress_bar(_A     )     ):
																	# 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=_A		,					timestep=_A		,					encoder_hidden_states=_A		,					added_cond_kwargs=_A		,					return_dict=_A		,					)[0]

																	if do_classifier_free_guidance:
																							lowerCamelCase__ 					=			noise_pred.split(latents.shape[1]		,					dim=1     )
																							lowerCamelCase__ 					=			noise_pred.chunk(2     )
																							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__ 					=			noise_pred.split(latents.shape[1]		,					dim=1     )

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

																	lowerCamelCase__ 					=			self.scheduler.step(
																	    _A		,					_A		,					_A		,					generator=_A		,					)[0]

											# post-processing
											lowerCamelCase__ 					=			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"]:
																	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(_A     )

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=_A     )





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import tensorflow as tf






class      A     (						TensorFormatter[Mapping, "tf.Tensor", Mapping]						):
     """simple docstring"""

     def __init__(      self					:				int		,					A__					:				Tuple=None		,					**A__					:				Tuple     )			->    Dict:
           super().__init__(features=A__     )
           lowerCamelCase__ 					=			tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				List[str]     )			->    Dict:
           import tensorflow as tf

           if isinstance(A__		,					A__     ) and column:
                 if all(
                     isinstance(A__		,					tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                       return tf.stack(A__     )
                 elif all(
                     isinstance(A__		,					(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
                     for x in column     ):
                       # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                       return tf.ragged.stack(A__     )

           return column




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Optional[int]     )			->    int:
           import tensorflow as tf

           if value is None:
                 return value

           lowerCamelCase__ 					=			{}

           if isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.integer     ):
                 lowerCamelCase__ 					=			{'dtype': tf.intaa}
           elif isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.floating     ):
                 lowerCamelCase__ 					=			{'dtype': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(A__		,					PIL.Image.Image     ):
                       lowerCamelCase__ 					=			np.asarray(A__     )

           return tf.convert_to_tensor(A__		,					**{**default_dtype, **self.tf_tensor_kwargs}     )




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Dict     )			->    List[str]:
           import tensorflow as tf

           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(A__		,					torch.Tensor     ):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
           if hasattr(A__		,					'__array__'     ) and not isinstance(A__		,					tf.Tensor     ):
                 lowerCamelCase__ 					=			data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A__		,					np.ndarray     ):
                 if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                       return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           elif isinstance(A__		,					(list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           return self._tensorize(A__     )




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				dict     )			->    List[str]:
           return map_nested(self._recursive_tensorize		,					A__		,					map_list=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_row(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_row(A__     )
           return self.recursive_tensorize(A__     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				pa.Table     )			->    "tf.Tensor":
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_column(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_column(A__		,					pa_table.column_names[0]     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           lowerCamelCase__ 					=			self._consolidate(A__     )
           return column




     def 	__snake_case				(      self					:				int		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_batch(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_batch(A__     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           for column_name in batch:
                 lowerCamelCase__ 					=			self._consolidate(batch[column_name]     )
           return batch