




# 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 copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
       import torch


lowerCamelCase	:	int				=   logging.get_logger(__name__)




@dataclass
class 						__lowercase		:




      """simple docstring"""





      def __init__(				self    ,				A=False    ,				A=False    ,				A=6.0    ,				A=None    ,				A=False    ,				A=False    ,				A=None    ,				A="fp4"    ,				A=False    ,				**A    ,				)		->    Optional[Any]:
         snake_case				:    Union[str, Any]           =	load_in_abit
         snake_case				:    Optional[Any]           =	load_in_abit
         snake_case				:    Tuple           =	llm_inta_threshold
         snake_case				:    List[str]           =	llm_inta_skip_modules
         snake_case				:    Optional[int]           =	llm_inta_enable_fpaa_cpu_offload
         snake_case				:    str           =	llm_inta_has_fpaa_weight
         snake_case				:    List[Any]           =	bnb_abit_quant_type
         snake_case				:    Any           =	bnb_abit_use_double_quant

         if bnb_abit_compute_dtype is None:
            snake_case				:    Tuple           =	torch.floataa
         elif isinstance(A    ,				A     ):
            snake_case				:    List[Any]           =	getattr(A    ,				A     )
         elif isinstance(A    ,				torch.dtype     ):
            snake_case				:    Tuple           =	bnb_abit_compute_dtype
         else:
            raise ValueError("""bnb_4bit_compute_dtype must be a string or a torch.dtype"""     )

         self.post_init()

      def      UpperCAmelCase				(				self     )		->    Dict:
         if not isinstance(self.llm_inta_threshold    ,				A     ):
            raise ValueError("""llm_int8_threshold must be a float"""     )

         if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules    ,				A     ):
            raise ValueError("""llm_int8_skip_modules must be a list of strings"""     )
         if not isinstance(self.llm_inta_enable_fpaa_cpu_offload    ,				A     ):
            raise ValueError("""llm_int8_enable_fp32_cpu_offload must be a boolean"""     )

         if not isinstance(self.llm_inta_has_fpaa_weight    ,				A     ):
            raise ValueError("""llm_int8_has_fp16_weight must be a boolean"""     )

         if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype    ,				torch.dtype     ):
            raise ValueError("""bnb_4bit_compute_dtype must be torch.dtype"""     )

         if not isinstance(self.bnb_abit_quant_type    ,				A     ):
            raise ValueError("""bnb_4bit_quant_type must be a string"""     )

         if not isinstance(self.bnb_abit_use_double_quant    ,				A     ):
            raise ValueError("""bnb_4bit_use_double_quant must be a boolean"""     )

         if self.load_in_abit and not version.parse(importlib.metadata.version("""bitsandbytes"""     )     ) >= version.parse(
             """0.39.0"""     ):
            raise ValueError(
                """4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"""     )

      def      UpperCAmelCase				(				self     )		->    Tuple:
         return self.load_in_abit or self.load_in_abit

      def      UpperCAmelCase				(				self     )		->    str:
         if self.load_in_abit:
            return "llm_int8"
         elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
            return "fp4"
         elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
            return "nf4"
         else:
            return None

      @classmethod
      def      UpperCAmelCase				(				cls    ,				A    ,				A    ,				**A     )		->    str:
         snake_case				:    List[str]           =	cls(**A     )

         snake_case				:    int           =	[]
         for key, value in kwargs.items():
            if hasattr(A    ,				A     ):
               setattr(A    ,				A    ,				A     )
               to_remove.append(A     )
         for key in to_remove:
            kwargs.pop(A    ,				A     )

         if return_unused_kwargs:
            return config, kwargs
         else:
            return config

      def      UpperCAmelCase				(				self    ,				A     )		->    Dict:
         with open(A    ,				"""w"""    ,				encoding="""utf-8"""     ) as writer:
            snake_case				:    Optional[Any]           =	self.to_dict()
            snake_case				:    Optional[Any]           =	json.dumps(A    ,				indent=2    ,				sort_keys=A     ) + """\n"""

            writer.write(A     )

      def      UpperCAmelCase				(				self     )		->    Dict[str, Any]:
         snake_case				:    Optional[int]           =	copy.deepcopy(self.__dict__     )
         snake_case				:    Union[str, Any]           =	str(output["""bnb_4bit_compute_dtype"""]     ).split("""."""     )[1]

         return output

      def __repr__(				self     )		->    Any:
         return f"""{self.__class__.__name__} {self.to_json_string()}"""

      def      UpperCAmelCase				(				self    ,				A = True     )		->    str:
         if use_diff is True:
            snake_case				:    Optional[int]           =	self.to_diff_dict()
         else:
            snake_case				:    Tuple           =	self.to_dict()
         return json.dumps(A    ,				indent=2    ,				sort_keys=A     ) + "\n"

      def      UpperCAmelCase				(				self     )		->    Dict[str, Any]:
         snake_case				:    Union[str, Any]           =	self.to_dict()

         # get the default config dict
         snake_case				:    int           =	BitsAndBytesConfig().to_dict()

         snake_case				:    Optional[Any]           =	{}

         # only serialize values that differ from the default config
         for key, value in config_dict.items():
            if value != default_config_dict[key]:
               snake_case				:    Any           =	value

         return serializable_config_dict











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

lowerCamelCase	:	Dict				=   '\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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase=8					)  ->       Optional[int]:
   snake_case				:    Dict           =	height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   snake_case				:    int           =	width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





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

         self.register_modules(
             unet=A    ,				scheduler=A    ,				movq=A    ,				)
         snake_case				:    Tuple           =	2 ** (len(self.movq.config.block_out_channels     ) - 1)

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A     )		->    str:
         if latents is None:
            snake_case				:    List[str]           =	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}"""     )
            snake_case				:    Union[str, Any]           =	latents.to(A     )

         snake_case				:    Optional[Any]           =	latents * scheduler.init_noise_sigma
         return latents

      def      UpperCAmelCase				(				self    ,				A=0     )		->    List[str]:
         if is_accelerate_available():
            from accelerate import cpu_offload
         else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

         snake_case				:    Dict           =	torch.device(f"""cuda:{gpu_id}"""     )

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

      def      UpperCAmelCase				(				self    ,				A=0     )		->    Optional[Any]:
         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				:    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)

         snake_case				:    Tuple           =	None
         for cpu_offloaded_model in [self.unet, self.movq]:
            snake_case     , snake_case				:    Tuple           =	cpu_offload_with_hook(A    ,				A    ,				prev_module_hook=A     )

         # We'll offload the last model manually.
         snake_case				:    Tuple           =	hook

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def      UpperCAmelCase				(				self     )		->    str:
         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    ,				A = 5_1_2    ,				A = 5_1_2    ,				A = 1_0_0    ,				A = 4.0    ,				A = 1    ,				A = None    ,				A = None    ,				A = "pil"    ,				A = True    ,				)		->    Any:
         snake_case				:    int           =	self._execution_device

         snake_case				:    Tuple           =	guidance_scale > 1.0

         if isinstance(A    ,				A     ):
            snake_case				:    str           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    int           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    Tuple           =	torch.cat(A    ,				dim=0     )

         snake_case				:    List[Any]           =	image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
            snake_case				:    Optional[int]           =	image_embeds.repeat_interleave(A    ,				dim=0     )
            snake_case				:    Dict           =	negative_image_embeds.repeat_interleave(A    ,				dim=0     )
            snake_case				:    Optional[Any]           =	hint.repeat_interleave(A    ,				dim=0     )

         snake_case				:    List[str]           =	torch.cat([negative_image_embeds, image_embeds]    ,				dim=0     ).to(dtype=self.unet.dtype    ,				device=A     )
         snake_case				:    Optional[Any]           =	torch.cat([hint, hint]    ,				dim=0     ).to(dtype=self.unet.dtype    ,				device=A     )
         self.scheduler.set_timesteps(A    ,				device=A     )
         snake_case				:    Tuple           =	self.scheduler.timesteps

         snake_case				:    Tuple           =	self.movq.config.latent_channels

         snake_case     , snake_case				:    Optional[int]           =	downscale_height_and_width(A    ,				A    ,				self.movq_scale_factor     )

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

            snake_case				:    Any           =	{"""image_embeds""": image_embeds, """hint""": hint}
            snake_case				:    List[Any]           =	self.unet(
                sample=A    ,				timestep=A    ,				encoder_hidden_states=A    ,				added_cond_kwargs=A    ,				return_dict=A    ,				)[0]

            if do_classifier_free_guidance:
               snake_case     , snake_case				:    Union[str, Any]           =	noise_pred.split(latents.shape[1]    ,				dim=1     )
               snake_case     , snake_case				:    Tuple           =	noise_pred.chunk(2     )
               snake_case     , snake_case				:    Optional[Any]           =	variance_pred.chunk(2     )
               snake_case				:    Optional[Any]           =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
               snake_case				:    Dict           =	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				:    int           =	noise_pred.split(latents.shape[1]    ,				dim=1     )

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

         if output_type == "pil":
            snake_case				:    Tuple           =	self.numpy_to_pil(A     )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=A     )





