


'''simple docstring'''

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,
)


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

lowercase__				     =		"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"






def __UpperCamelCase   (			__lowerCamelCase		: Dict      ,  __lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: List[Any]=8				)      ->     Optional[int]:







     '''simple docstring'''






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






class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							)       ->    Optional[int]:
          super().__init__()

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






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    List[str]:
          if latents is None:
               _a						=		randn_tensor(__UpperCamelCase     ,							generator=__UpperCamelCase     ,							device=__UpperCamelCase     ,							dtype=__UpperCamelCase		)
          else:
               if latents.shape != shape:
                    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"		)
               _a						=		latents.to(__UpperCamelCase		)

          _a						=		latents * scheduler.init_noise_sigma
          return latents






     def  a_					(			self     ,							__UpperCamelCase=0		)       ->    int:
          if is_accelerate_available():
               from accelerate import cpu_offload
          else:
               raise ImportError("Please install accelerate via `pip install accelerate`"		)

          _a						=		torch.device(f"cuda:{gpu_id}"		)

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






     def  a_					(			self     ,							__UpperCamelCase=0		)       ->    Optional[int]:
          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."		)

          _a						=		torch.device(f"cuda:{gpu_id}"		)

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

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

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






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def  a_					(			self		)       ->    int:
          if not hasattr(self.unet     ,							"_hf_hook"		):
               return self.device
          for module in self.unet.modules():
               if (
                   hasattr(__UpperCamelCase     ,							"_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(__UpperCamelCase		)
     def __call__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = 512     ,							__UpperCamelCase = 512     ,							__UpperCamelCase = 100     ,							__UpperCamelCase = 4.0     ,							__UpperCamelCase = 1     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = "pil"     ,							__UpperCamelCase = True     ,							)       ->    List[Any]:
          _a						=		self._execution_device

          _a						=		guidance_scale > 1.0

          if isinstance(__UpperCamelCase     ,							__UpperCamelCase		):
               _a						=		torch.cat(__UpperCamelCase     ,							dim=0		)
          if isinstance(__UpperCamelCase     ,							__UpperCamelCase		):
               _a						=		torch.cat(__UpperCamelCase     ,							dim=0		)
          if isinstance(__UpperCamelCase     ,							__UpperCamelCase		):
               _a						=		torch.cat(__UpperCamelCase     ,							dim=0		)

          _a						=		image_embeds.shape[0] * num_images_per_prompt

          if do_classifier_free_guidance:
               _a						=		image_embeds.repeat_interleave(__UpperCamelCase     ,							dim=0		)
               _a						=		negative_image_embeds.repeat_interleave(__UpperCamelCase     ,							dim=0		)
               _a						=		hint.repeat_interleave(__UpperCamelCase     ,							dim=0		)

          _a						=		torch.cat([negative_image_embeds, image_embeds]     ,							dim=0		).to(dtype=self.unet.dtype     ,							device=__UpperCamelCase		)
          _a						=		torch.cat([hint, hint]     ,							dim=0		).to(dtype=self.unet.dtype     ,							device=__UpperCamelCase		)
          self.scheduler.set_timesteps(__UpperCamelCase     ,							device=__UpperCamelCase		)
          _a						=		self.scheduler.timesteps

          _a						=		self.movq.config.latent_channels

          _a			,			_a						=		downscale_height_and_width(__UpperCamelCase     ,							__UpperCamelCase     ,							self.movq_scale_factor		)

          # create initial latent
          _a						=		self.prepare_latents(
              (batch_size, num_channels_latents, height, width)     ,							image_embeds.dtype     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							self.scheduler     ,							)

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

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

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

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

          if output_type == "pil":
               _a						=		self.numpy_to_pil(__UpperCamelCase		)

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=__UpperCamelCase		)





'''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 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				['''image_processor''', '''tokenizer''']
     UpperCAmelCase 					=				'''LayoutLMv2ImageProcessor'''
     UpperCAmelCase 					=				('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






     def __init__(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							**__UpperCamelCase		)       ->    str:
          if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead."     ,							__UpperCamelCase     ,							)
               _a						=		kwargs.pop("feature_extractor"		)

          _a						=		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__(__UpperCamelCase     ,							__UpperCamelCase		)






     def __call__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = True     ,							__UpperCamelCase = False     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 0     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = True     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    BatchEncoding:
          # verify input
          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
          _a						=		self.image_processor(images=__UpperCamelCase     ,							return_tensors=__UpperCamelCase		)

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

          _a						=		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=__UpperCamelCase     ,							add_special_tokens=__UpperCamelCase     ,							padding=__UpperCamelCase     ,							truncation=__UpperCamelCase     ,							max_length=__UpperCamelCase     ,							stride=__UpperCamelCase     ,							pad_to_multiple_of=__UpperCamelCase     ,							return_token_type_ids=__UpperCamelCase     ,							return_attention_mask=__UpperCamelCase     ,							return_overflowing_tokens=__UpperCamelCase     ,							return_special_tokens_mask=__UpperCamelCase     ,							return_offsets_mapping=__UpperCamelCase     ,							return_length=__UpperCamelCase     ,							verbose=__UpperCamelCase     ,							return_tensors=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

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

          return encoded_inputs






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          _a						=		[]
          for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx]		)

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

          return images_with_overflow






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Tuple:
          return self.tokenizer.batch_decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    List[Any]:
          return self.tokenizer.decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     @property
     def  a_					(			self		)       ->    int:
          return ["input_ids", "bbox", "attention_mask", "image"]






     @property
     def  a_					(			self		)       ->    Dict:
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor_class






     @property
     def  a_					(			self		)       ->    int:
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor

