

from typing import TYPE_CHECKING

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


lowerCAmelCase    : Any              =     {
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    : Dict              =     ["""MvpTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    : List[Any]              =     [
           """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MvpForCausalLM""",
           """MvpForConditionalGeneration""",
           """MvpForQuestionAnswering""",
           """MvpForSequenceClassification""",
           """MvpModel""",
           """MvpPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

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









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

_lowerCAmelCase  :      Any			 ="""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> 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\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=8      ):
	UpperCAmelCase__:	int									= height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	UpperCAmelCase__:	Optional[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     (				_a		):






					'''simple docstring'''



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

						self.register_modules(
						    unet=lowerCamelCase__				,      scheduler=lowerCamelCase__				,      movq=lowerCamelCase__				,      )
						UpperCAmelCase__:	Optional[int]									= 2 ** (len(self.movq.config.block_out_channels						) - 1)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if latents is None:
							UpperCAmelCase__:	Any									= 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__:	Tuple									= latents.to(lowerCamelCase__						)

						UpperCAmelCase__:	Union[str, Any]									= 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`"						)

						UpperCAmelCase__:	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(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."						)

						UpperCAmelCase__:	Union[str, Any]									= 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__:	int									= None
						for cpu_offloaded_model in [self.unet, self.movq]:
							UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= cpu_offload_with_hook(lowerCamelCase__				,      lowerCamelCase__				,      prev_module_hook=lowerCamelCase__						)

						# We'll offload the last model manually.
						UpperCAmelCase__:	List[Any]									= 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__				,      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				,      ):
						UpperCAmelCase__:	str									= self._execution_device

						UpperCAmelCase__:	List[Any]									= guidance_scale > 1.0

						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	List[Any]									= torch.cat(lowerCamelCase__				,      dim=0						)
						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	List[Any]									= torch.cat(lowerCamelCase__				,      dim=0						)
						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	Any									= torch.cat(lowerCamelCase__				,      dim=0						)

						UpperCAmelCase__:	Dict									= image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
							UpperCAmelCase__:	Tuple									= image_embeds.repeat_interleave(lowerCamelCase__				,      dim=0						)
							UpperCAmelCase__:	Union[str, Any]									= negative_image_embeds.repeat_interleave(lowerCamelCase__				,      dim=0						)
							UpperCAmelCase__:	Any									= hint.repeat_interleave(lowerCamelCase__				,      dim=0						)

						UpperCAmelCase__:	Union[str, Any]									= torch.cat([negative_image_embeds, image_embeds]				,      dim=0						).to(dtype=self.unet.dtype				,      device=lowerCamelCase__						)
						UpperCAmelCase__:	Dict									= torch.cat([hint, hint]				,      dim=0						).to(dtype=self.unet.dtype				,      device=lowerCamelCase__						)
						self.scheduler.set_timesteps(lowerCamelCase__				,      device=lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= self.scheduler.timesteps

						UpperCAmelCase__:	Optional[int]									= self.movq.config.latent_channels

						UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= downscale_height_and_width(lowerCamelCase__				,      lowerCamelCase__				,      self.movq_scale_factor						)

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

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

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

						if output_type == "pil":
							UpperCAmelCase__:	List[str]									= self.numpy_to_pil(lowerCamelCase__						)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=lowerCamelCase__						)