






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,
)


SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE__		:							List[str] 				=				"""
    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  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=8       ) ->			str:

     '''simple docstring'''
     UpperCAmelCase__      :  List[str]				      =			height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     UpperCAmelCase__      :  str				      =			width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor







class 				UpperCAmelCase_  (		__lowerCamelCase		):




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

          self.register_modules(
              unet=_lowerCAmelCase	,						scheduler=_lowerCAmelCase	,						movq=_lowerCAmelCase	,						)
          UpperCAmelCase__      :  Optional[Any]				      =			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__      :  str				      =			latents.to(_lowerCAmelCase		)

          UpperCAmelCase__      :  Dict				      =			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__      :  str				      =			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__      :  Any				      =			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__      :  str				      =			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 = 512	,						_lowerCAmelCase = 512	,						_lowerCAmelCase = 100	,						_lowerCAmelCase = 4.0	,						_lowerCAmelCase = 1	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = "pil"	,						_lowerCAmelCase = True	,						):
          UpperCAmelCase__      :  Union[str, Any]				      =			self._execution_device

          UpperCAmelCase__      :  Dict				      =			guidance_scale > 1.0

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

          UpperCAmelCase__      :  Optional[int]				      =			image_embeds.shape[0] * num_images_per_prompt

          if do_classifier_free_guidance:
               UpperCAmelCase__      :  str				      =			image_embeds.repeat_interleave(_lowerCAmelCase	,						dim=0		)
               UpperCAmelCase__      :  Tuple				      =			negative_image_embeds.repeat_interleave(_lowerCAmelCase	,						dim=0		)
               UpperCAmelCase__      :  Dict				      =			hint.repeat_interleave(_lowerCAmelCase	,						dim=0		)

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

          UpperCAmelCase__      :  List[Any]				      =			self.movq.config.latent_channels

          UpperCAmelCase__      ,		UpperCAmelCase__      :  List[str]				      =			downscale_height_and_width(_lowerCAmelCase	,						_lowerCAmelCase	,						self.movq_scale_factor		)

          # create initial latent
          UpperCAmelCase__      :  Tuple				      =			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__      :  int				      =			torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

               UpperCAmelCase__      :  int				      =			{"""image_embeds""": image_embeds, """hint""": hint}
               UpperCAmelCase__      :  str				      =			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__      :  int				      =			noise_pred.split(latents.shape[1]	,						dim=1		)
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			noise_pred.chunk(2		)
                    UpperCAmelCase__      ,		UpperCAmelCase__      :  Dict				      =			variance_pred.chunk(2		)
                    UpperCAmelCase__      :  Optional[Any]				      =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                    UpperCAmelCase__      :  int				      =			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__      :  Optional[int]				      =			noise_pred.split(latents.shape[1]	,						dim=1		)

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

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

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=_lowerCAmelCase		)









'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "LayoutLMv2ImageProcessor"
 lowerCAmelCase__       = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
 def __init__(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					List[str]:


      """simple docstring"""




      if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError("""You need to specify an `image_processor`."""     )
      if tokenizer is None:
           raise ValueError("""You need to specify a `tokenizer`."""     )

      super().__init__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Union[List[List[int]], List[List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[List[int], List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, PaddingStrategy] = False   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, TruncationStrategy] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			int = 0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, TensorType]] = None   ,				**__SCREAMING_SNAKE_CASE   :			Any   ,				)    ->					BatchEncoding:


      """simple docstring"""




      if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               """You cannot provide bounding boxes """
               """if you initialized the image processor with apply_ocr set to True."""     )

      if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""     )

      if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""     )

      # first, apply the image processor
      __SCREAMING_SNAKE_CASE      = self.image_processor(images=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE     )

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
                __SCREAMING_SNAKE_CASE      = [text]  # add batch dimension (as the image processor always adds a batch dimension)
           __SCREAMING_SNAKE_CASE      = features["""words"""]

      __SCREAMING_SNAKE_CASE      = self.tokenizer(
          text=text if text is not None else features["""words"""]   ,				text_pair=text_pair if text_pair is not None else None   ,				boxes=boxes if boxes is not None else features["""boxes"""]   ,				word_labels=__SCREAMING_SNAKE_CASE   ,				add_special_tokens=__SCREAMING_SNAKE_CASE   ,				padding=__SCREAMING_SNAKE_CASE   ,				truncation=__SCREAMING_SNAKE_CASE   ,				max_length=__SCREAMING_SNAKE_CASE   ,				stride=__SCREAMING_SNAKE_CASE   ,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE   ,				return_token_type_ids=__SCREAMING_SNAKE_CASE   ,				return_attention_mask=__SCREAMING_SNAKE_CASE   ,				return_overflowing_tokens=__SCREAMING_SNAKE_CASE   ,				return_special_tokens_mask=__SCREAMING_SNAKE_CASE   ,				return_offsets_mapping=__SCREAMING_SNAKE_CASE   ,				return_length=__SCREAMING_SNAKE_CASE   ,				verbose=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE   ,				)

      # add pixel values
      __SCREAMING_SNAKE_CASE      = features.pop("""pixel_values"""     )
      if return_overflowing_tokens is True:
           __SCREAMING_SNAKE_CASE      = self.get_overflowing_images(__SCREAMING_SNAKE_CASE   ,				encoded_inputs["""overflow_to_sample_mapping"""]     )
      __SCREAMING_SNAKE_CASE      = images

      return encoded_inputs
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = []
      for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]     )

      if len(__SCREAMING_SNAKE_CASE     ) != len(__SCREAMING_SNAKE_CASE     ):
           raise ValueError(
               """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
               f' {len(__SCREAMING_SNAKE_CASE     )} and {len(__SCREAMING_SNAKE_CASE     )}'     )

      return images_with_overflow
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				*__SCREAMING_SNAKE_CASE   :			str   ,				**__SCREAMING_SNAKE_CASE   :			List[str]     )    ->					List[str]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				*__SCREAMING_SNAKE_CASE   :			Dict   ,				**__SCREAMING_SNAKE_CASE   :			Any     )    ->					Union[str, Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      return ["input_ids", "bbox", "attention_mask", "image"]
 @property
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
      return self.image_processor_class


 @property
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Any:


      """simple docstring"""




      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
      return self.image_processor






