
'''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,
)


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

snake_case_ :  int     =					'\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  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       Dict=8):
   UpperCamelCase       =  height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   UpperCamelCase       =  width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   ):

      '''simple docstring'''







      super().__init__()

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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      if latents is None:
         UpperCamelCase       =  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       =  latents.to(lowerCamelCase__   )

      UpperCamelCase       =  latents * scheduler.init_noise_sigma
      return latents







   def       UpperCAmelCase				(			self ,   lowerCamelCase__=0   ):

      '''simple docstring'''







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

      UpperCamelCase       =  torch.device(f'cuda:{gpu_id}'   )

      UpperCamelCase       =  [
          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   ):

      '''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.'''   )

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

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







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

      '''simple docstring'''







      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__ = 5_1_2 ,   lowerCamelCase__ = 5_1_2 ,   lowerCamelCase__ = 1_0_0 ,   lowerCamelCase__ = 4.0 ,   lowerCamelCase__ = 1 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = "pil" ,   lowerCamelCase__ = True ,   ):

      '''simple docstring'''







      UpperCamelCase       =  self._execution_device

      UpperCamelCase       =  guidance_scale > 1.0

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

      UpperCamelCase       =  image_embeds.shape[0] * num_images_per_prompt

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

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

      UpperCamelCase       =  self.movq.config.latent_channels

      UpperCamelCase     , UpperCamelCase       =  downscale_height_and_width(lowerCamelCase__ ,   lowerCamelCase__ ,   self.movq_scale_factor   )

      # create initial latent
      UpperCamelCase       =  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       =  torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

         UpperCamelCase       =  {'''image_embeds''': image_embeds, '''hint''': hint}
         UpperCamelCase       =  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       =  noise_pred.split(latents.shape[1] ,   dim=1   )
            UpperCamelCase     , UpperCamelCase       =  noise_pred.chunk(2   )
            UpperCamelCase     , UpperCamelCase       =  variance_pred.chunk(2   )
            UpperCamelCase       =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
            UpperCamelCase       =  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       =  noise_pred.split(latents.shape[1] ,   dim=1   )

         # compute the previous noisy sample x_t -> x_t-1
         UpperCamelCase       =  self.scheduler.step(
             lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   generator=lowerCamelCase__ ,   )[0]
      # post-processing
      UpperCamelCase       =  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       =  image * 0.5 + 0.5
         UpperCamelCase       =  image.clamp(0 ,   1   )
         UpperCamelCase       =  image.cpu().permute(0 ,   2 ,   3 ,   1   ).float().numpy()

      if output_type == "pil":
         UpperCamelCase       =  self.numpy_to_pil(lowerCamelCase__   )

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=lowerCamelCase__   )







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ :  Any     =					{
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Union[str, Any]     =					['ElectraTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Tuple     =					[
       'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ElectraForCausalLM',
       'ElectraForMaskedLM',
       'ElectraForMultipleChoice',
       'ElectraForPreTraining',
       'ElectraForQuestionAnswering',
       'ElectraForSequenceClassification',
       'ElectraForTokenClassification',
       'ElectraModel',
       'ElectraPreTrainedModel',
       'load_tf_weights_in_electra',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  int     =					[
       'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFElectraForMaskedLM',
       'TFElectraForMultipleChoice',
       'TFElectraForPreTraining',
       'TFElectraForQuestionAnswering',
       'TFElectraForSequenceClassification',
       'TFElectraForTokenClassification',
       'TFElectraModel',
       'TFElectraPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Optional[int]     =					[
       'FlaxElectraForCausalLM',
       'FlaxElectraForMaskedLM',
       'FlaxElectraForMultipleChoice',
       'FlaxElectraForPreTraining',
       'FlaxElectraForQuestionAnswering',
       'FlaxElectraForSequenceClassification',
       'FlaxElectraForTokenClassification',
       'FlaxElectraModel',
       'FlaxElectraPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
   from .tokenization_electra import ElectraTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_electra_fast import ElectraTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_electra import (
          ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
          ElectraPreTrainedModel,
          load_tf_weights_in_electra,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_electra import (
          TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
          TFElectraPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
          FlaxElectraPreTrainedModel,
      )

else:
   import sys

   snake_case_ :  Union[str, Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





