

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


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

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

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> 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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.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)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

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







def 	lowerCamelCase__			(   __A						:Union[str, Any]  ,__A						:Any  ,__A						:Dict=8		):


       """simple docstring"""




       __snake_case					  =       height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       __snake_case					  =       width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor







def 	lowerCamelCase__			(   __A						:Union[str, Any]  ,__A						:Union[str, Any]=5_1_2  ,__A						:Optional[Any]=5_1_2		):


       """simple docstring"""




       __snake_case					  =       pil_image.resize((w, h)  ,resample=Image.BICUBIC  ,reducing_gap=1		)
       __snake_case					  =       np.array(pil_image.convert("""RGB"""		)		)
       __snake_case					  =       arr.astype(np.floataa		) / 127.5 - 1
       __snake_case					  =       np.transpose(__A  ,[2, 0, 1]		)
       __snake_case					  =       torch.from_numpy(__A		).unsqueeze(0		)
       return image




class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       def __init__(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   )			->       Optional[int]:






              """simple docstring"""


              super().__init__()

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


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )			->       Dict:






              """simple docstring"""


              __snake_case					  =       min(int(num_inference_steps * strength      )			,   _UpperCamelCase      )

              __snake_case					  =       max(num_inference_steps - init_timestep			,   0      )
              __snake_case					  =       self.scheduler.timesteps[t_start:]

              return timesteps, num_inference_steps - t_start


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase=None      )			->       List[Any]:






              """simple docstring"""


              if not isinstance(_UpperCamelCase			,   (torch.Tensor, PIL.Image.Image, list)      ):
                     raise ValueError(
                         F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase      )}'      )

              __snake_case					  =       image.to(device=_UpperCamelCase			,   dtype=_UpperCamelCase      )

              __snake_case					  =       batch_size * num_images_per_prompt

              if image.shape[1] == 4:
                     __snake_case					  =       image

              else:
                     if isinstance(_UpperCamelCase			,   _UpperCamelCase      ) and len(_UpperCamelCase      ) != batch_size:
                            raise ValueError(
                                F'You have passed a list of generators of length {len(_UpperCamelCase      )}, but requested an effective batch'
                                F' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

                     elif isinstance(_UpperCamelCase			,   _UpperCamelCase      ):
                            __snake_case					  =       [
                                self.movq.encode(image[i : i + 1]      ).latent_dist.sample(generator[i]      ) for i in range(_UpperCamelCase      )
                            ]
                            __snake_case					  =       torch.cat(_UpperCamelCase			,   dim=0      )
                     else:
                            __snake_case					  =       self.movq.encode(_UpperCamelCase      ).latent_dist.sample(_UpperCamelCase      )

                     __snake_case					  =       self.movq.config.scaling_factor * init_latents

              __snake_case					  =       torch.cat([init_latents]			,   dim=0      )

              __snake_case					  =       init_latents.shape
              __snake_case					  =       randn_tensor(_UpperCamelCase			,   generator=_UpperCamelCase			,   device=_UpperCamelCase			,   dtype=_UpperCamelCase      )

              # get latents
              __snake_case					  =       self.scheduler.add_noise(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )

              __snake_case					  =       init_latents

              return latents


       def  a		(		self			,   _UpperCamelCase=0      )			->       str:






              """simple docstring"""


              if is_accelerate_available():
                     from accelerate import cpu_offload
              else:
                     raise ImportError("""Please install accelerate via `pip install accelerate`"""      )

              __snake_case					  =       torch.device(F'cuda:{gpu_id}'      )

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


       def  a		(		self			,   _UpperCamelCase=0      )			->       List[str]:






              """simple docstring"""


              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					  =       torch.device(F'cuda:{gpu_id}'      )

              if self.device.type != "cpu":
                     self.to("""cpu"""			,   silence_dtype_warnings=_UpperCamelCase      )
                     torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

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

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


       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def  a		(		self      )			->       Optional[int]:






              """simple docstring"""


              if not hasattr(self.unet			,   """_hf_hook"""      ):
                     return self.device
              for module in self.unet.modules():
                     if (
                         hasattr(_UpperCamelCase			,   """_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(_UpperCamelCase      )
       def __call__(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase = 5_12			,   _UpperCamelCase = 5_12			,   _UpperCamelCase = 1_00			,   _UpperCamelCase = 4.0			,   _UpperCamelCase = 0.3			,   _UpperCamelCase = 1			,   _UpperCamelCase = None			,   _UpperCamelCase = "pil"			,   _UpperCamelCase = True			,   )			->       str:






              """simple docstring"""


              __snake_case					  =       self._execution_device

              __snake_case					  =       guidance_scale > 1.0

              if isinstance(_UpperCamelCase			,   _UpperCamelCase      ):
                     __snake_case					  =       torch.cat(_UpperCamelCase			,   dim=0      )
              if isinstance(_UpperCamelCase			,   _UpperCamelCase      ):
                     __snake_case					  =       torch.cat(_UpperCamelCase			,   dim=0      )
              if isinstance(_UpperCamelCase			,   _UpperCamelCase      ):
                     __snake_case					  =       torch.cat(_UpperCamelCase			,   dim=0      )

              __snake_case					  =       image_embeds.shape[0]

              if do_classifier_free_guidance:
                     __snake_case					  =       image_embeds.repeat_interleave(_UpperCamelCase			,   dim=0      )
                     __snake_case					  =       negative_image_embeds.repeat_interleave(_UpperCamelCase			,   dim=0      )
                     __snake_case					  =       hint.repeat_interleave(_UpperCamelCase			,   dim=0      )

              __snake_case					  =       torch.cat([negative_image_embeds, image_embeds]			,   dim=0      ).to(dtype=self.unet.dtype			,   device=_UpperCamelCase      )
              __snake_case					  =       torch.cat([hint, hint]			,   dim=0      ).to(dtype=self.unet.dtype			,   device=_UpperCamelCase      )

              if not isinstance(_UpperCamelCase			,   _UpperCamelCase      ):
                     __snake_case					  =       [image]
              if not all(isinstance(_UpperCamelCase			,   (PIL.Image.Image, torch.Tensor)      ) for i in image      ):
                     raise ValueError(
                         F'Input is in incorrect format: {[type(_UpperCamelCase      ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'      )

              __snake_case					  =       torch.cat([prepare_image(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      ) for i in image]			,   dim=0      )
              __snake_case					  =       image.to(dtype=image_embeds.dtype			,   device=_UpperCamelCase      )

              __snake_case					  =       self.movq.encode(_UpperCamelCase      )["""latents"""]
              __snake_case					  =       latents.repeat_interleave(_UpperCamelCase			,   dim=0      )
              self.scheduler.set_timesteps(_UpperCamelCase			,   device=_UpperCamelCase      )
              __snake_case  ,						__snake_case					  =       self.get_timesteps(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )
              __snake_case					  =       timesteps[:1].repeat(batch_size * num_images_per_prompt      )
              __snake_case  ,						__snake_case					  =       downscale_height_and_width(_UpperCamelCase			,   _UpperCamelCase			,   self.movq_scale_factor      )
              __snake_case					  =       self.prepare_latents(
                  _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   image_embeds.dtype			,   _UpperCamelCase			,   _UpperCamelCase      )
              for i, t in enumerate(self.progress_bar(_UpperCamelCase      )      ):
                     # expand the latents if we are doing classifier free guidance
                     __snake_case					  =       torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

                     __snake_case					  =       {"""image_embeds""": image_embeds, """hint""": hint}
                     __snake_case					  =       self.unet(
                         sample=_UpperCamelCase			,   timestep=_UpperCamelCase			,   encoder_hidden_states=_UpperCamelCase			,   added_cond_kwargs=_UpperCamelCase			,   return_dict=_UpperCamelCase			,   )[0]

                     if do_classifier_free_guidance:
                            __snake_case  ,						__snake_case					  =       noise_pred.split(latents.shape[1]			,   dim=1      )
                            __snake_case  ,						__snake_case					  =       noise_pred.chunk(2      )
                            __snake_case  ,						__snake_case					  =       variance_pred.chunk(2      )
                            __snake_case					  =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                            __snake_case					  =       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					  =       noise_pred.split(latents.shape[1]			,   dim=1      )

                     # compute the previous noisy sample x_t -> x_t-1

                     __snake_case					  =       self.scheduler.step(
                         _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   generator=_UpperCamelCase			,   )[0]

              # post-processing
              __snake_case					  =       self.movq.decode(_UpperCamelCase			,   force_not_quantize=_UpperCamelCase      )["""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					  =       image * 0.5 + 0.5
                     __snake_case					  =       image.clamp(0			,   1      )
                     __snake_case					  =       image.cpu().permute(0			,   2			,   3			,   1      ).float().numpy()

              if output_type == "pil":
                     __snake_case					  =       self.numpy_to_pil(_UpperCamelCase      )

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=_UpperCamelCase      )




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__		   =    logging.get_logger(__name__)

UpperCamelCase__		   =    {
    '''apple/mobilevitv2-1.0''': '''https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json''',
}

class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       "mobilevitv2"


       def __init__(		self			,   _UpperCamelCase=3			,   _UpperCamelCase=2_56			,   _UpperCamelCase=2			,   _UpperCamelCase=2.0			,   _UpperCamelCase="swish"			,   _UpperCamelCase=3			,   _UpperCamelCase=32			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.02			,   _UpperCamelCase=1E-5			,   _UpperCamelCase=5_12			,   _UpperCamelCase=[6, 12, 18]			,   _UpperCamelCase=0.1			,   _UpperCamelCase=2_55			,   _UpperCamelCase=[2, 4, 3]			,   _UpperCamelCase=[1_28, 1_92, 2_56]			,   _UpperCamelCase=1.0			,   _UpperCamelCase=2			,   _UpperCamelCase=0.0			,   _UpperCamelCase=0.0			,   **_UpperCamelCase			,   )			->       List[Any]:






              """simple docstring"""


              super().__init__(**_UpperCamelCase      )

              __snake_case					  =       num_channels
              __snake_case					  =       image_size
              __snake_case					  =       patch_size
              __snake_case					  =       expand_ratio
              __snake_case					  =       hidden_act
              __snake_case					  =       conv_kernel_size
              __snake_case					  =       output_stride
              __snake_case					  =       initializer_range
              __snake_case					  =       layer_norm_eps
              __snake_case					  =       n_attn_blocks
              __snake_case					  =       base_attn_unit_dims
              __snake_case					  =       width_multiplier
              __snake_case					  =       ffn_multiplier
              __snake_case					  =       ffn_dropout
              __snake_case					  =       attn_dropout
              __snake_case					  =       classifier_dropout_prob

              # decode head attributes for semantic segmentation
              __snake_case					  =       aspp_out_channels
              __snake_case					  =       atrous_rates
              __snake_case					  =       aspp_dropout_prob
              __snake_case					  =       semantic_loss_ignore_index






class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       version.parse("1.11"     )


       @property
       def  a		(		self      )			->       Mapping[str, Mapping[int, str]]:






              """simple docstring"""


              return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]      )


       @property
       def  a		(		self      )			->       Mapping[str, Mapping[int, str]]:






              """simple docstring"""


              if self.task == "image-classification":
                     return OrderedDict([("""logits""", {0: """batch"""})]      )
              else:
                     return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]      )






       @property
       def  a		(		self      )			->       float:






              """simple docstring"""


              return 1E-4

