'''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,
)


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

A_     :						Tuple    							='''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''



def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							Tuple	,   __snake_case  :							Dict=8)			->					Optional[Any]:
   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






class     __UpperCAmelCase       ( __a		):





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

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





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     if latents is None:
        lowerCAmelCase_			=      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}''' )
        lowerCAmelCase_			=      latents.to(_lowerCamelCase )

     lowerCAmelCase_			=      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`''' )

     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(_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.''' )

     lowerCAmelCase_			=      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)

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

     # We'll offload the last model manually.
     lowerCAmelCase_			=      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 = 512    ,     _lowerCamelCase = 512    ,     _lowerCamelCase = 100    ,     _lowerCamelCase = 4.0    ,     _lowerCamelCase = 1    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = "pil"    ,     _lowerCamelCase = True    ,     ):
     lowerCAmelCase_			=      self._execution_device

     lowerCAmelCase_			=      guidance_scale > 1.0

     if isinstance(_lowerCamelCase    ,     _lowerCamelCase ):
        lowerCAmelCase_			=      torch.cat(_lowerCamelCase    ,     dim=0 )
     lowerCAmelCase_			=      image_embeds.shape[0] * num_images_per_prompt
     if isinstance(_lowerCamelCase    ,     _lowerCamelCase ):
        lowerCAmelCase_			=      torch.cat(_lowerCamelCase    ,     dim=0 )

     if do_classifier_free_guidance:
        lowerCAmelCase_			=      image_embeds.repeat_interleave(_lowerCamelCase    ,     dim=0 )
        lowerCAmelCase_			=      negative_image_embeds.repeat_interleave(_lowerCamelCase    ,     dim=0 )

     lowerCAmelCase_			=      torch.cat([negative_image_embeds, image_embeds]    ,     dim=0 ).to(dtype=self.unet.dtype    ,     device=_lowerCamelCase )

     self.scheduler.set_timesteps(_lowerCamelCase    ,     device=_lowerCamelCase )
     lowerCAmelCase_			=      self.scheduler.timesteps

     lowerCAmelCase_			=      self.unet.config.in_channels

     lowerCAmelCase_	,lowerCAmelCase_			=      downscale_height_and_width(_lowerCamelCase    ,     _lowerCamelCase    ,     self.movq_scale_factor )

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

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

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

        # compute the previous noisy sample x_t -> x_t-1
        lowerCAmelCase_			=      self.scheduler.step(
            _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     generator=_lowerCamelCase    ,     )[0]
     # post-processing
     lowerCAmelCase_			=      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"]:
        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(_lowerCamelCase )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowerCamelCase )






'''simple docstring'''

from typing import TYPE_CHECKING

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


A_     :						Optional[Any]    							={
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Dict    							=['''SqueezeBertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Optional[int]    							=[
     '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SqueezeBertForMaskedLM''',
     '''SqueezeBertForMultipleChoice''',
     '''SqueezeBertForQuestionAnswering''',
     '''SqueezeBertForSequenceClassification''',
     '''SqueezeBertForTokenClassification''',
     '''SqueezeBertModel''',
     '''SqueezeBertModule''',
     '''SqueezeBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_squeezebert import (
     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SqueezeBertConfig,
     SqueezeBertOnnxConfig,
 )
 from .tokenization_squeezebert import SqueezeBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_squeezebert import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
      SqueezeBertModule,
      SqueezeBertPreTrainedModel,
  )

else:
 import sys

 A_     :						List[str]    							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





