


'''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,
)


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

__A :  str    							=				"\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     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       str							,   A__ :       Dict=8 ):
  '''simple docstring'''





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






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(       self   :  int   ,						lowerCamelCase   :  UNetaDConditionModel   ,						lowerCamelCase   :  DDPMScheduler   ,						lowerCamelCase   :  VQModel   ,						)       ->  Dict:
      super().__init__()

      self.register_modules(
          unet=lowerCamelCase   ,						scheduler=lowerCamelCase   ,						movq=lowerCamelCase   ,						)
      lowerCAmelCase_      :				Any      =       2 ** (len(self.movq.config.block_out_channels		) - 1)

    def __lowercase       (       self   :  int   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  int		)       ->  Union[str, Any]:
      if latents is None:
        lowerCAmelCase_      :				Optional[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}'		)
        lowerCAmelCase_      :				Union[str, Any]      =       latents.to(lowerCamelCase		)

      lowerCAmelCase_      :				Tuple      =       latents * scheduler.init_noise_sigma
      return latents

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

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

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

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  List[str]=0		)       ->  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."""		)

      lowerCAmelCase_      :				int      =       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)

      lowerCAmelCase_      :				Optional[Any]      =       None
      for cpu_offloaded_model in [self.unet, self.movq]:
        lowerCAmelCase_,							lowerCAmelCase_      :				Dict      =       cpu_offload_with_hook(lowerCamelCase   ,						lowerCamelCase   ,						prev_module_hook=lowerCamelCase		)

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

    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def __lowercase       (       self   :  List[Any]		)       ->  List[str]:
      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   :  List[Any]   ,						lowerCamelCase   :  Union[torch.FloatTensor, List[torch.FloatTensor]]   ,						lowerCamelCase   :  Union[torch.FloatTensor, List[torch.FloatTensor]]   ,						lowerCamelCase   :  torch.FloatTensor   ,						lowerCamelCase   :  int = 5_12   ,						lowerCamelCase   :  int = 5_12   ,						lowerCamelCase   :  int = 1_00   ,						lowerCamelCase   :  float = 4.0   ,						lowerCamelCase   :  int = 1   ,						lowerCamelCase   :  Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,						lowerCamelCase   :  Optional[torch.FloatTensor] = None   ,						lowerCamelCase   :  Optional[str] = "pil"   ,						lowerCamelCase   :  bool = True   ,						)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Dict      =       self._execution_device

      lowerCAmelCase_      :				Union[str, Any]      =       guidance_scale > 1.0

      if isinstance(lowerCamelCase   ,						lowerCamelCase		):
        lowerCAmelCase_      :				Any      =       torch.cat(lowerCamelCase   ,						dim=0		)
      if isinstance(lowerCamelCase   ,						lowerCamelCase		):
        lowerCAmelCase_      :				Dict      =       torch.cat(lowerCamelCase   ,						dim=0		)
      if isinstance(lowerCamelCase   ,						lowerCamelCase		):
        lowerCAmelCase_      :				List[Any]      =       torch.cat(lowerCamelCase   ,						dim=0		)

      lowerCAmelCase_      :				Optional[Any]      =       image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
        lowerCAmelCase_      :				List[Any]      =       image_embeds.repeat_interleave(lowerCamelCase   ,						dim=0		)
        lowerCAmelCase_      :				Any      =       negative_image_embeds.repeat_interleave(lowerCamelCase   ,						dim=0		)
        lowerCAmelCase_      :				int      =       hint.repeat_interleave(lowerCamelCase   ,						dim=0		)

      lowerCAmelCase_      :				Any      =       torch.cat([negative_image_embeds, image_embeds]   ,						dim=0		).to(dtype=self.unet.dtype   ,						device=lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       torch.cat([hint, hint]   ,						dim=0		).to(dtype=self.unet.dtype   ,						device=lowerCamelCase		)
      self.scheduler.set_timesteps(lowerCamelCase   ,						device=lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       self.scheduler.timesteps

      lowerCAmelCase_      :				Dict      =       self.movq.config.latent_channels

      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       downscale_height_and_width(lowerCamelCase   ,						lowerCamelCase   ,						self.movq_scale_factor		)

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

        lowerCAmelCase_      :				int      =       {"""image_embeds""": image_embeds, """hint""": hint}
        lowerCAmelCase_      :				Tuple      =       self.unet(
            sample=lowerCamelCase   ,						timestep=lowerCamelCase   ,						encoder_hidden_states=lowerCamelCase   ,						added_cond_kwargs=lowerCamelCase   ,						return_dict=lowerCamelCase   ,						)[0]

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

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

      if output_type == "pil":
        lowerCAmelCase_      :				Any      =       self.numpy_to_pil(lowerCamelCase		)

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=lowerCamelCase		)







'''simple docstring'''






import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
__A :  List[str]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       List[str] ):
  '''simple docstring'''





  print("""Loading config file...""" )

  def flatten_yaml_as_dict(A__ :       int							,   A__ :       str=""							,   A__ :       Dict="." ):
    lowerCAmelCase_      :				int      =       []
    for k, v in d.items():
      lowerCAmelCase_      :				Any      =       parent_key + sep + k if parent_key else k
      if isinstance(A__							,   collections.abc.MutableMapping ):
        items.extend(flatten_yaml_as_dict(A__							,   A__							,   sep=A__ ).items() )
      else:
        items.append((new_key, v) )
    return dict(A__ )

  lowerCAmelCase_      :				Optional[int]      =       argparse.Namespace()
  with open(A__							,   """r""" ) as yaml_file:
    try:
      lowerCAmelCase_      :				Any      =       yaml.load(A__							,   Loader=yaml.FullLoader )

      lowerCAmelCase_      :				Any      =       flatten_yaml_as_dict(A__ )
      for k, v in flat_cfg.items():
        setattr(A__							,   A__							,   A__ )
    except yaml.YAMLError as exc:
      logger.error("""Error while loading config file: {}. Error message: {}""".format(A__							,   str(A__ ) ) )
  return config







def     UpperCamelCase_      (   A__ :       Optional[int]							,   A__ :       List[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       MobileViTVaConfig()

  lowerCAmelCase_      :				Any      =       False

  # dataset
  if task_name.startswith("""imagenet1k_""" ):
    lowerCAmelCase_      :				Tuple      =       10_00
    if int(task_name.strip().split("""_""" )[-1] ) == 3_84:
      lowerCAmelCase_      :				List[str]      =       3_84
    else:
      lowerCAmelCase_      :				Optional[int]      =       2_56
    lowerCAmelCase_      :				int      =       """imagenet-1k-id2label.json"""
  elif task_name.startswith("""imagenet21k_to_1k_""" ):
    lowerCAmelCase_      :				int      =       2_10_00
    if int(task_name.strip().split("""_""" )[-1] ) == 3_84:
      lowerCAmelCase_      :				int      =       3_84
    else:
      lowerCAmelCase_      :				int      =       2_56
    lowerCAmelCase_      :				Optional[Any]      =       """imagenet-22k-id2label.json"""
  elif task_name.startswith("""ade20k_""" ):
    lowerCAmelCase_      :				Dict      =       1_51
    lowerCAmelCase_      :				Any      =       5_12
    lowerCAmelCase_      :				int      =       """ade20k-id2label.json"""
    lowerCAmelCase_      :				Union[str, Any]      =       True
  elif task_name.startswith("""voc_""" ):
    lowerCAmelCase_      :				Any      =       21
    lowerCAmelCase_      :				List[str]      =       5_12
    lowerCAmelCase_      :				Union[str, Any]      =       """pascal-voc-id2label.json"""
    lowerCAmelCase_      :				int      =       True

  # orig_config
  lowerCAmelCase_      :				Optional[int]      =       load_orig_config_file(A__ )
  assert getattr(A__							,   """model.classification.name"""							,   -1 ) == "mobilevit_v2", "Invalid model"
  lowerCAmelCase_      :				Union[str, Any]      =       getattr(A__							,   """model.classification.mitv2.width_multiplier"""							,   1.0 )
  assert (
      getattr(A__							,   """model.classification.mitv2.attn_norm_layer"""							,   -1 ) == "layer_norm_2d"
  ), "Norm layers other than layer_norm_2d is not supported"
  lowerCAmelCase_      :				List[Any]      =       getattr(A__							,   """model.classification.activation.name"""							,   """swish""" )
  # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

  if is_segmentation_model:
    lowerCAmelCase_      :				List[str]      =       getattr(A__							,   """model.segmentation.output_stride"""							,   16 )
    if "_deeplabv3" in task_name:
      lowerCAmelCase_      :				Optional[int]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_rates"""							,   [12, 24, 36] )
      lowerCAmelCase_      :				Optional[Any]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_out_channels"""							,   5_12 )
      lowerCAmelCase_      :				Optional[int]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_dropout"""							,   0.1 )

    # id2label
  lowerCAmelCase_      :				Any      =       """huggingface/label-files"""
  lowerCAmelCase_      :				Any      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
  lowerCAmelCase_      :				Any      =       {int(A__ ): v for k, v in idalabel.items()}
  lowerCAmelCase_      :				Optional[int]      =       idalabel
  lowerCAmelCase_      :				Tuple      =       {v: k for k, v in idalabel.items()}

  return config







def     UpperCamelCase_      (   A__ :       int							,   A__ :       Dict							,   A__ :       Tuple ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       dct.pop(A__ )
  lowerCAmelCase_      :				Optional[int]      =       val







def     UpperCamelCase_      (   A__ :       int							,   A__ :       int=False ):
  '''simple docstring'''





  if base_model:
    lowerCAmelCase_      :				List[Any]      =       """"""
  else:
    lowerCAmelCase_      :				Any      =       """mobilevitv2."""

  lowerCAmelCase_      :				int      =       []
  for k in state_dict.keys():
    if k[:8] == "encoder.":
      lowerCAmelCase_      :				List[Any]      =       k[8:]
    else:
      lowerCAmelCase_      :				Optional[Any]      =       k

    if ".block." in k:
      lowerCAmelCase_      :				Dict      =       k_new.replace(""".block."""							,   """.""" )
    if ".conv." in k:
      lowerCAmelCase_      :				Any      =       k_new.replace(""".conv."""							,   """.convolution.""" )
    if ".norm." in k:
      lowerCAmelCase_      :				List[Any]      =       k_new.replace(""".norm."""							,   """.normalization.""" )

    if "conv_1." in k:
      lowerCAmelCase_      :				Any      =       k_new.replace("""conv_1."""							,   f'{model_prefix}conv_stem.' )
    for i in [1, 2]:
      if f'layer_{i}.' in k:
        lowerCAmelCase_      :				Optional[Any]      =       k_new.replace(f'layer_{i}.'							,   f'{model_prefix}encoder.layer.{i-1}.layer.' )
    if ".exp_1x1." in k:
      lowerCAmelCase_      :				Optional[int]      =       k_new.replace(""".exp_1x1."""							,   """.expand_1x1.""" )
    if ".red_1x1." in k:
      lowerCAmelCase_      :				List[str]      =       k_new.replace(""".red_1x1."""							,   """.reduce_1x1.""" )

    for i in [3, 4, 5]:
      if f'layer_{i}.0.' in k:
        lowerCAmelCase_      :				Optional[int]      =       k_new.replace(f'layer_{i}.0.'							,   f'{model_prefix}encoder.layer.{i-1}.downsampling_layer.' )
      if f'layer_{i}.1.local_rep.0.' in k:
        lowerCAmelCase_      :				str      =       k_new.replace(f'layer_{i}.1.local_rep.0.'							,   f'{model_prefix}encoder.layer.{i-1}.conv_kxk.' )
      if f'layer_{i}.1.local_rep.1.' in k:
        lowerCAmelCase_      :				Optional[Any]      =       k_new.replace(f'layer_{i}.1.local_rep.1.'							,   f'{model_prefix}encoder.layer.{i-1}.conv_1x1.' )

    for i in [3, 4, 5]:
      if i == 3:
        lowerCAmelCase_      :				Optional[Any]      =       [0, 1]
      elif i == 4:
        lowerCAmelCase_      :				Any      =       [0, 1, 2, 3]
      elif i == 5:
        lowerCAmelCase_      :				Any      =       [0, 1, 2]

      for j in j_in:
        if f'layer_{i}.1.global_rep.{j}.' in k:
          lowerCAmelCase_      :				Optional[int]      =       k_new.replace(
              f'layer_{i}.1.global_rep.{j}.'							,   f'{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.' )
      if f'layer_{i}.1.global_rep.{j+1}.' in k:
        lowerCAmelCase_      :				Optional[Any]      =       k_new.replace(
            f'layer_{i}.1.global_rep.{j+1}.'							,   f'{model_prefix}encoder.layer.{i-1}.layernorm.' )

      if f'layer_{i}.1.conv_proj.' in k:
        lowerCAmelCase_      :				str      =       k_new.replace(f'layer_{i}.1.conv_proj.'							,   f'{model_prefix}encoder.layer.{i-1}.conv_projection.' )

    if "pre_norm_attn.0." in k:
      lowerCAmelCase_      :				Dict      =       k_new.replace("""pre_norm_attn.0."""							,   """layernorm_before.""" )
    if "pre_norm_attn.1." in k:
      lowerCAmelCase_      :				Optional[int]      =       k_new.replace("""pre_norm_attn.1."""							,   """attention.""" )
    if "pre_norm_ffn.0." in k:
      lowerCAmelCase_      :				Any      =       k_new.replace("""pre_norm_ffn.0."""							,   """layernorm_after.""" )
    if "pre_norm_ffn.1." in k:
      lowerCAmelCase_      :				int      =       k_new.replace("""pre_norm_ffn.1."""							,   """ffn.conv1.""" )
    if "pre_norm_ffn.3." in k:
      lowerCAmelCase_      :				List[Any]      =       k_new.replace("""pre_norm_ffn.3."""							,   """ffn.conv2.""" )

    if "classifier.1." in k:
      lowerCAmelCase_      :				Optional[int]      =       k_new.replace("""classifier.1."""							,   """classifier.""" )

    if "seg_head." in k:
      lowerCAmelCase_      :				Optional[Any]      =       k_new.replace("""seg_head."""							,   """segmentation_head.""" )
    if ".aspp_layer." in k:
      lowerCAmelCase_      :				Dict      =       k_new.replace(""".aspp_layer."""							,   """.""" )
    if ".aspp_pool." in k:
      lowerCAmelCase_      :				List[Any]      =       k_new.replace(""".aspp_pool."""							,   """.""" )

    rename_keys.append((k, k_new) )
  return rename_keys







def     UpperCamelCase_      (   A__ :       Tuple ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       []
  for k in state_dict.keys():
    if k.startswith("""seg_head.aux_head.""" ):
      keys_to_ignore.append(A__ )
  for k in keys_to_ignore:
    state_dict.pop(A__							,   A__ )







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
  lowerCAmelCase_      :				Optional[Any]      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       Union[str, Any]							,   A__ :       str							,   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       get_mobilevitva_config(A__							,   A__ )

  # load original state_dict
  lowerCAmelCase_      :				int      =       torch.load(A__							,   map_location="""cpu""" )

  # load huggingface model
  if task_name.startswith("""ade20k_""" ) or task_name.startswith("""voc_""" ):
    lowerCAmelCase_      :				Union[str, Any]      =       MobileViTVaForSemanticSegmentation(A__ ).eval()
    lowerCAmelCase_      :				Union[str, Any]      =       False
  else:
    lowerCAmelCase_      :				List[Any]      =       MobileViTVaForImageClassification(A__ ).eval()
    lowerCAmelCase_      :				Optional[int]      =       False

  # remove and rename some keys of load the original model
  lowerCAmelCase_      :				Tuple      =       checkpoint
  remove_unused_keys(A__ )
  lowerCAmelCase_      :				List[Any]      =       create_rename_keys(A__							,   base_model=A__ )
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(A__							,   A__							,   A__ )

  # load modified state_dict
  model.load_state_dict(A__ )

  # Check outputs on an image, prepared by MobileViTImageProcessor
  lowerCAmelCase_      :				Dict      =       MobileViTImageProcessor(crop_size=config.image_size							,   size=config.image_size + 32 )
  lowerCAmelCase_      :				Dict      =       image_processor(images=prepare_img()							,   return_tensors="""pt""" )
  lowerCAmelCase_      :				int      =       model(**A__ )

  # verify classification model
  if task_name.startswith("""imagenet""" ):
    lowerCAmelCase_      :				Optional[Any]      =       outputs.logits
    lowerCAmelCase_      :				Tuple      =       logits.argmax(-1 ).item()
    print("""Predicted class:"""							,   model.config.idalabel[predicted_class_idx] )
    if task_name.startswith("""imagenet1k_256""" ) and config.width_multiplier == 1.0:
      # expected_logits for base variant
      lowerCAmelCase_      :				int      =       torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01] )
      assert torch.allclose(logits[0, :3]							,   A__							,   atol=1E-4 )

  Path(A__ ).mkdir(exist_ok=A__ )
  print(f'Saving model {task_name} to {pytorch_dump_folder_path}' )
  model.save_pretrained(A__ )
  print(f'Saving image processor to {pytorch_dump_folder_path}' )
  image_processor.save_pretrained(A__ )


if __name__ == "__main__":
  __A :  Dict    							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--task",
      default="imagenet1k_256",
      type=str,
      help=(
          "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
          "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
      ),
      choices=[
          "imagenet1k_256",
          "imagenet1k_384",
          "imagenet21k_to_1k_256",
          "imagenet21k_to_1k_384",
          "ade20k_deeplabv3",
          "voc_deeplabv3",
      ],
  )

  parser.add_argument(
      "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
  )
  parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
  )

  __A :  Union[str, Any]    							=				parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )



