


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

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					List[Any]=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     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def __init__(			self					,   A_					,   A_					,   A_					,   )  ->       int:
         super().__init__()

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






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       Tuple:
         if latents is None:
              __UpperCamelCase    							=randn_tensor(A_					,   generator=A_					,   device=A_					,   dtype=A_    )
         else:
              if latents.shape != shape:
                   raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'    )
              __UpperCamelCase    							=latents.to(A_    )

         __UpperCamelCase    							=latents * scheduler.init_noise_sigma
         return latents






    def _a					(			self					,   A_=0    )  ->       List[str]:
         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(A_					,   A_    )






    def _a					(			self					,   A_=0    )  ->       Tuple:
         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=A_    )
              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(A_					,   A_					,   prev_module_hook=A_    )

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






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

         __UpperCamelCase    							=guidance_scale > 1.0

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

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

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

         self.scheduler.set_timesteps(A_					,   device=A_    )
         __UpperCamelCase    							=self.scheduler.timesteps

         __UpperCamelCase    							=self.unet.config.in_channels

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

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

         for i, t in enumerate(self.progress_bar(A_    )    ):
              # 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}
              __UpperCamelCase    							=self.unet(
                  sample=A_					,   timestep=A_					,   encoder_hidden_states=A_					,   added_cond_kwargs=A_					,   return_dict=A_					,   )[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(
                  A_					,   A_					,   A_					,   generator=A_					,   )[0]
         # post-processing
         __UpperCamelCase    							=self.movq.decode(A_					,   force_not_quantize=A_    )['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(A_    )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=A_    )









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A		       =						{
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['VivitImageProcessor']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'VivitModel',
       'VivitPreTrainedModel',
       'VivitForVideoClassification',
   ]


if TYPE_CHECKING:
   from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_vivit import VivitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vivit import (
          VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          VivitForVideoClassification,
          VivitModel,
          VivitPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





