



"""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,
)


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

_lowerCAmelCase    : List[str]							=							"\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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						Tuple=8    )  ->       Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase      :     Any        =   height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     _UpperCAmelCase      :     List[Any]        =   width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor






class      UpperCAmelCase_  (    _UpperCamelCase						):



      def __init__(					self					:     List[Any]					,     A					:     UNetaDConditionModel					,     A					:     DDPMScheduler					,     A					:     VQModel					,     ):
           super().__init__()

           self.register_modules(
               unet=A					,     scheduler=A					,     movq=A					,     )
           _UpperCAmelCase      :     Optional[Any]        =   2 ** (len(self.movq.config.block_out_channels						) - 1)



      def 						snake_case_ (					self					:     int					,     A					:     List[str]					,     A					:     Tuple					,     A					:     Dict					,     A					:     Any					,     A					:     Optional[int]					,     A					:     List[Any]						):
           if latents is None:
                _UpperCAmelCase      :     int        =   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      :     Union[str, Any]        =   latents.to(A						)

           _UpperCAmelCase      :     List[str]        =   latents * scheduler.init_noise_sigma
           return latents



      def 						snake_case_ (					self					:     Dict					,     A					:     Union[str, Any]=0						):
           if is_accelerate_available():
                from accelerate import cpu_offload
           else:
                raise ImportError("Please install accelerate via `pip install accelerate`"						)

           _UpperCAmelCase      :     List[Any]        =   torch.device(f'cuda:{gpu_id}'						)

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



      def 						snake_case_ (					self					:     int					,     A					:     str=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."						)

           _UpperCAmelCase      :     Optional[int]        =   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      :     str        =   None
           for cpu_offloaded_model in [self.unet, self.movq]:
                _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   cpu_offload_with_hook(A					,     A					,     prev_module_hook=A						)

           # We'll offload the last model manually.
           _UpperCAmelCase      :     str        =   hook



      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 						snake_case_ (					self					:     Optional[Any]						):
           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					:     Optional[Any]					,     A					:     Union[torch.FloatTensor, List[torch.FloatTensor]]					,     A					:     Union[torch.FloatTensor, List[torch.FloatTensor]]					,     A					:     int = 5_1_2					,     A					:     int = 5_1_2					,     A					:     int = 1_0_0					,     A					:     float = 4.0					,     A					:     int = 1					,     A					:     Optional[Union[torch.Generator, List[torch.Generator]]] = None					,     A					:     Optional[torch.FloatTensor] = None					,     A					:     Optional[str] = "pil"					,     A					:     bool = True					,     ):
           _UpperCAmelCase      :     Optional[Any]        =   self._execution_device

           _UpperCAmelCase      :     str        =   guidance_scale > 1.0

           if isinstance(A					,     A						):
                _UpperCAmelCase      :     Tuple        =   torch.cat(A					,     dim=0						)
           _UpperCAmelCase      :     Union[str, Any]        =   image_embeds.shape[0] * num_images_per_prompt
           if isinstance(A					,     A						):
                _UpperCAmelCase      :     List[Any]        =   torch.cat(A					,     dim=0						)

           if do_classifier_free_guidance:
                _UpperCAmelCase      :     Union[str, Any]        =   image_embeds.repeat_interleave(A					,     dim=0						)
                _UpperCAmelCase      :     List[Any]        =   negative_image_embeds.repeat_interleave(A					,     dim=0						)

           _UpperCAmelCase      :     Any        =   torch.cat([negative_image_embeds, image_embeds]					,     dim=0						).to(dtype=self.unet.dtype					,     device=A						)

           self.scheduler.set_timesteps(A					,     device=A						)
           _UpperCAmelCase      :     List[str]        =   self.scheduler.timesteps

           _UpperCAmelCase      :     List[Any]        =   self.unet.config.in_channels

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   downscale_height_and_width(A					,     A					,     self.movq_scale_factor						)

           # create initial latent
           _UpperCAmelCase      :     Optional[int]        =   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      :     Any        =   torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

                _UpperCAmelCase      :     Any        =   {"image_embeds": image_embeds}
                _UpperCAmelCase      :     Dict        =   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      :     Dict        =   noise_pred.split(latents.shape[1]					,     dim=1						)
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[Any]        =   noise_pred.chunk(2						)
                     _UpperCAmelCase    ,	_UpperCAmelCase      :     Any        =   variance_pred.chunk(2						)
                     _UpperCAmelCase      :     int        =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                     _UpperCAmelCase      :     int        =   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      :     str        =   noise_pred.split(latents.shape[1]					,     dim=1						)

                # compute the previous noisy sample x_t -> x_t-1
                _UpperCAmelCase      :     Optional[Any]        =   self.scheduler.step(
                    A					,     A					,     A					,     generator=A					,     )[0]
           # post-processing
           _UpperCAmelCase      :     Union[str, Any]        =   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      :     str        =   image * 0.5 + 0.5
                _UpperCAmelCase      :     Dict        =   image.clamp(0					,     1						)
                _UpperCAmelCase      :     Dict        =   image.cpu().permute(0					,     2					,     3					,     1						).float().numpy()

           if output_type == "pil":
                _UpperCAmelCase      :     Any        =   self.numpy_to_pil(A						)

           if not return_dict:
                return (image,)

           return ImagePipelineOutput(images=A						)





"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. 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.
import re

from ..utils import cached_file


# docstyle-ignore
_lowerCAmelCase    : Any							=							"\nHuman: <<task>>\n\nAssistant: "


_lowerCAmelCase    : str							=							"huggingface-tools/default-prompts"
_lowerCAmelCase    : Union[str, Any]							=							{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[int]						,  SCREAMING_SNAKE_CASE__     :						int="run"    )  ->       int:





     '''simple docstring'''

     if prompt_or_repo_id is None:
          _UpperCAmelCase      :     Optional[int]        =   DEFAULT_PROMPTS_REPO

     # prompt is considered a repo ID when it does not contain any kind of space
     if re.search("\\s"						,  SCREAMING_SNAKE_CASE__    ) is not None:
          return prompt_or_repo_id

     _UpperCAmelCase      :     Dict        =   cached_file(
         SCREAMING_SNAKE_CASE__						,  PROMPT_FILES[mode]						,  repo_type="dataset"						,  user_agent={"agent": agent_name}    )
     with open(SCREAMING_SNAKE_CASE__						,  "r"						,  encoding="utf-8"    ) as f:
          return f.read()
