




'''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,
)


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

UpperCAmelCase_				:     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 	_UpperCamelCase       (_lowerCamelCase							:   Tuple      ,		_lowerCamelCase							:   Dict      ,		_lowerCamelCase							:   str=8		)->      Optional[Any]:




     '''simple docstring'''





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







def 	_UpperCamelCase       (_lowerCamelCase							:   str      ,		_lowerCamelCase							:   Dict=5_12      ,		_lowerCamelCase							:   Union[str, Any]=5_12		)->      Tuple:




     '''simple docstring'''





     __snake_case			    =     pil_image.resize((w, h)      ,		resample=Image.BICUBIC      ,		reducing_gap=1		)
     __snake_case			    =     np.array(pil_image.convert('''RGB'''		)		)
     __snake_case			    =     arr.astype(np.floataa		) / 127.5 - 1
     __snake_case			    =     np.transpose(_lowerCamelCase      ,		[2, 0, 1]		)
     __snake_case			    =     torch.from_numpy(_lowerCamelCase		).unsqueeze(0		)
     return image







class 			lowerCAmelCase      (       __lowerCAmelCase):


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     )					->     Optional[int]:



            '''simple docstring'''




            super().__init__()

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


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Dict:



            '''simple docstring'''




            __snake_case			    =     min(int(num_inference_steps * strength     )			,     __SCREAMING_SNAKE_CASE     )

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

            return timesteps, num_inference_steps - t_start


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None     )					->     Dict:



            '''simple docstring'''




            if not isinstance(__SCREAMING_SNAKE_CASE			,     (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(__SCREAMING_SNAKE_CASE     )}'''     )

            __snake_case			    =     image.to(device=__SCREAMING_SNAKE_CASE			,     dtype=__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     batch_size * num_images_per_prompt

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

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

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

                 __snake_case			    =     self.movq.config.scaling_factor * init_latents

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

            __snake_case			    =     init_latents.shape
            __snake_case			    =     randn_tensor(__SCREAMING_SNAKE_CASE			,     generator=__SCREAMING_SNAKE_CASE			,     device=__SCREAMING_SNAKE_CASE			,     dtype=__SCREAMING_SNAKE_CASE     )

            # get latents
            __snake_case			    =     self.scheduler.add_noise(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )

            __snake_case			    =     init_latents

            return latents


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE=0     )					->     str:



            '''simple docstring'''




            if is_accelerate_available():
                 from accelerate import cpu_offload
            else:
                 raise ImportError('''Please install accelerate via `pip install accelerate`'''     )

            __snake_case			    =     torch.device(F'''cuda:{gpu_id}'''     )

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


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE=0     )					->     int:



            '''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.'''     )

            __snake_case			    =     torch.device(F'''cuda:{gpu_id}'''     )

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

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

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


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



            '''simple docstring'''




            if not hasattr(self.unet			,     '''_hf_hook'''     ):
                 return self.device
            for module in self.unet.modules():
                 if (
                     hasattr(__SCREAMING_SNAKE_CASE			,     '''_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(__SCREAMING_SNAKE_CASE     )
       def __call__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = 512			,     __SCREAMING_SNAKE_CASE = 512			,     __SCREAMING_SNAKE_CASE = 100			,     __SCREAMING_SNAKE_CASE = 4.0			,     __SCREAMING_SNAKE_CASE = 0.3			,     __SCREAMING_SNAKE_CASE = 1			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = "pil"			,     __SCREAMING_SNAKE_CASE = True			,     )					->     Dict:



            '''simple docstring'''




            __snake_case			    =     self._execution_device

            __snake_case			    =     guidance_scale > 1.0

            if isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     ):
                 __snake_case			    =     torch.cat(__SCREAMING_SNAKE_CASE			,     dim=0     )
            if isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     ):
                 __snake_case			    =     torch.cat(__SCREAMING_SNAKE_CASE			,     dim=0     )
            if isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     ):
                 __snake_case			    =     torch.cat(__SCREAMING_SNAKE_CASE			,     dim=0     )

            __snake_case			    =     image_embeds.shape[0]

            if do_classifier_free_guidance:
                 __snake_case			    =     image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE			,     dim=0     )
                 __snake_case			    =     negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE			,     dim=0     )
                 __snake_case			    =     hint.repeat_interleave(__SCREAMING_SNAKE_CASE			,     dim=0     )

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

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

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

            __snake_case			    =     self.movq.encode(__SCREAMING_SNAKE_CASE     )['''latents''']
            __snake_case			    =     latents.repeat_interleave(__SCREAMING_SNAKE_CASE			,     dim=0     )
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			,     device=__SCREAMING_SNAKE_CASE     )
            __snake_case    ,			__snake_case			    =     self.get_timesteps(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )
            __snake_case			    =     timesteps[:1].repeat(batch_size * num_images_per_prompt     )
            __snake_case    ,			__snake_case			    =     downscale_height_and_width(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     self.movq_scale_factor     )
            __snake_case			    =     self.prepare_latents(
                __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     image_embeds.dtype			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )
            for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE     )     ):
                 # expand the latents if we are doing classifier free guidance
                 __snake_case			    =     torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

                 __snake_case			    =     {'''image_embeds''': image_embeds, '''hint''': hint}
                 __snake_case			    =     self.unet(
                     sample=__SCREAMING_SNAKE_CASE			,     timestep=__SCREAMING_SNAKE_CASE			,     encoder_hidden_states=__SCREAMING_SNAKE_CASE			,     added_cond_kwargs=__SCREAMING_SNAKE_CASE			,     return_dict=__SCREAMING_SNAKE_CASE			,     )[0]

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

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

                 __snake_case			    =     self.scheduler.step(
                     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     generator=__SCREAMING_SNAKE_CASE			,     )[0]

            # post-processing
            __snake_case			    =     self.movq.decode(__SCREAMING_SNAKE_CASE			,     force_not_quantize=__SCREAMING_SNAKE_CASE     )['''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"]:
                 __snake_case			    =     image * 0.5 + 0.5
                 __snake_case			    =     image.clamp(0			,     1     )
                 __snake_case			    =     image.cpu().permute(0			,     2			,     3			,     1     ).float().numpy()

            if output_type == "pil":
                 __snake_case			    =     self.numpy_to_pil(__SCREAMING_SNAKE_CASE     )

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE     )












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_				:     Dict     			=						{
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_				:     List[Any]     			=						['''MarkupLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_				:     int     			=						[
         '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MarkupLMForQuestionAnswering''',
         '''MarkupLMForSequenceClassification''',
         '''MarkupLMForTokenClassification''',
         '''MarkupLMModel''',
         '''MarkupLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
     from .feature_extraction_markuplm import MarkupLMFeatureExtractor
     from .processing_markuplm import MarkupLMProcessor
     from .tokenization_markuplm import MarkupLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_markuplm_fast import MarkupLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_markuplm import (
              MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarkupLMForQuestionAnswering,
              MarkupLMForSequenceClassification,
              MarkupLMForTokenClassification,
              MarkupLMModel,
              MarkupLMPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase_				:     Dict     			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)






