



'''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_      :				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_      :				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_      :				Optional[Any]      =       noise_pred.split(latents.shape[1]   ,						dim=1		)
          lowerCAmelCase_      :				Tuple      =       noise_pred.chunk(2		)
          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_      :				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 json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__A :  Optional[Any]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       str=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       OrderedDict()
  for key, value in state_dict.items():
    if encoder_only and not key.startswith("""head""" ):
      lowerCAmelCase_      :				str      =       """segformer.encoder.""" + key
    if key.startswith("""backbone""" ):
      lowerCAmelCase_      :				str      =       key.replace("""backbone"""							,   """segformer.encoder""" )
    if "patch_embed" in key:
      # replace for example patch_embed1 by patch_embeddings.0
      lowerCAmelCase_      :				List[str]      =       key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
      lowerCAmelCase_      :				List[Any]      =       key.replace(f'patch_embed{idx}'							,   f'patch_embeddings.{int(A__ )-1}' )
    if "norm" in key:
      lowerCAmelCase_      :				Any      =       key.replace("""norm"""							,   """layer_norm""" )
    if "segformer.encoder.layer_norm" in key:
      # replace for example layer_norm1 by layer_norm.0
      lowerCAmelCase_      :				Tuple      =       key[key.find("""segformer.encoder.layer_norm""" ) + len("""segformer.encoder.layer_norm""" )]
      lowerCAmelCase_      :				int      =       key.replace(f'layer_norm{idx}'							,   f'layer_norm.{int(A__ )-1}' )
    if "layer_norm1" in key:
      lowerCAmelCase_      :				str      =       key.replace("""layer_norm1"""							,   """layer_norm_1""" )
    if "layer_norm2" in key:
      lowerCAmelCase_      :				Union[str, Any]      =       key.replace("""layer_norm2"""							,   """layer_norm_2""" )
    if "block" in key:
      # replace for example block1 by block.0
      lowerCAmelCase_      :				Any      =       key[key.find("""block""" ) + len("""block""" )]
      lowerCAmelCase_      :				str      =       key.replace(f'block{idx}'							,   f'block.{int(A__ )-1}' )
    if "attn.q" in key:
      lowerCAmelCase_      :				List[Any]      =       key.replace("""attn.q"""							,   """attention.self.query""" )
    if "attn.proj" in key:
      lowerCAmelCase_      :				Optional[int]      =       key.replace("""attn.proj"""							,   """attention.output.dense""" )
    if "attn" in key:
      lowerCAmelCase_      :				str      =       key.replace("""attn"""							,   """attention.self""" )
    if "fc1" in key:
      lowerCAmelCase_      :				Optional[Any]      =       key.replace("""fc1"""							,   """dense1""" )
    if "fc2" in key:
      lowerCAmelCase_      :				Optional[Any]      =       key.replace("""fc2"""							,   """dense2""" )
    if "linear_pred" in key:
      lowerCAmelCase_      :				List[Any]      =       key.replace("""linear_pred"""							,   """classifier""" )
    if "linear_fuse" in key:
      lowerCAmelCase_      :				Optional[Any]      =       key.replace("""linear_fuse.conv"""							,   """linear_fuse""" )
      lowerCAmelCase_      :				Any      =       key.replace("""linear_fuse.bn"""							,   """batch_norm""" )
    if "linear_c" in key:
      # replace for example linear_c4 by linear_c.3
      lowerCAmelCase_      :				str      =       key[key.find("""linear_c""" ) + len("""linear_c""" )]
      lowerCAmelCase_      :				Dict      =       key.replace(f'linear_c{idx}'							,   f'linear_c.{int(A__ )-1}' )
    if key.startswith("""head""" ):
      lowerCAmelCase_      :				int      =       key.replace("""head"""							,   """classifier""" )
    lowerCAmelCase_      :				int      =       value

  return new_state_dict







def     UpperCamelCase_      (   A__ :       int							,   A__ :       Union[str, Any] ):
  '''simple docstring'''





  for i in range(config.num_encoder_blocks ):
    for j in range(config.depths[i] ):
      # read in weights + bias of keys and values (which is a single matrix in the original implementation)
      lowerCAmelCase_      :				int      =       state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight' )
      lowerCAmelCase_      :				Optional[int]      =       state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.bias' )
      # next, add keys and values (in that order) to the state dict
      lowerCAmelCase_      :				List[str]      =       kv_weight[
          : config.hidden_sizes[i], :
      ]
      lowerCAmelCase_      :				Optional[Any]      =       kv_bias[: config.hidden_sizes[i]]
      lowerCAmelCase_      :				Union[str, Any]      =       kv_weight[
          config.hidden_sizes[i] :, :
      ]
      lowerCAmelCase_      :				str      =       kv_bias[
          config.hidden_sizes[i] :
      ]







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





  lowerCAmelCase_      :				Union[str, Any]      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCAmelCase_      :				Optional[Any]      =       Image.open(requests.get(A__							,   stream=A__ ).raw )

  return image







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Optional[Any]							,   A__ :       List[Any]							,   A__ :       Tuple ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       SegformerConfig()
  lowerCAmelCase_      :				Optional[Any]      =       False

  # set attributes based on model_name
  lowerCAmelCase_      :				int      =       """huggingface/label-files"""
  if "segformer" in model_name:
    lowerCAmelCase_      :				Optional[int]      =       model_name[len("""segformer.""" ) : len("""segformer.""" ) + 2]
    if "ade" in model_name:
      lowerCAmelCase_      :				List[Any]      =       1_50
      lowerCAmelCase_      :				int      =       """ade20k-id2label.json"""
      lowerCAmelCase_      :				Tuple      =       (1, 1_50, 1_28, 1_28)
    elif "city" in model_name:
      lowerCAmelCase_      :				List[str]      =       19
      lowerCAmelCase_      :				Dict      =       """cityscapes-id2label.json"""
      lowerCAmelCase_      :				List[str]      =       (1, 19, 1_28, 1_28)
    else:
      raise ValueError(f'Model {model_name} not supported' )
  elif "mit" in model_name:
    lowerCAmelCase_      :				Dict      =       True
    lowerCAmelCase_      :				Optional[int]      =       model_name[4:6]
    lowerCAmelCase_      :				Union[str, Any]      =       10_00
    lowerCAmelCase_      :				int      =       """imagenet-1k-id2label.json"""
    lowerCAmelCase_      :				Optional[Any]      =       (1, 10_00)
  else:
    raise ValueError(f'Model {model_name} not supported' )

  # set config attributes
  lowerCAmelCase_      :				Optional[Any]      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
  lowerCAmelCase_      :				List[Any]      =       {int(A__ ): v for k, v in idalabel.items()}
  lowerCAmelCase_      :				List[str]      =       idalabel
  lowerCAmelCase_      :				List[Any]      =       {v: k for k, v in idalabel.items()}
  if size == "b0":
    pass
  elif size == "b1":
    lowerCAmelCase_      :				Any      =       [64, 1_28, 3_20, 5_12]
    lowerCAmelCase_      :				int      =       2_56
  elif size == "b2":
    lowerCAmelCase_      :				Any      =       [64, 1_28, 3_20, 5_12]
    lowerCAmelCase_      :				List[str]      =       7_68
    lowerCAmelCase_      :				Any      =       [3, 4, 6, 3]
  elif size == "b3":
    lowerCAmelCase_      :				List[str]      =       [64, 1_28, 3_20, 5_12]
    lowerCAmelCase_      :				Union[str, Any]      =       7_68
    lowerCAmelCase_      :				Union[str, Any]      =       [3, 4, 18, 3]
  elif size == "b4":
    lowerCAmelCase_      :				Tuple      =       [64, 1_28, 3_20, 5_12]
    lowerCAmelCase_      :				Tuple      =       7_68
    lowerCAmelCase_      :				Tuple      =       [3, 8, 27, 3]
  elif size == "b5":
    lowerCAmelCase_      :				Union[str, Any]      =       [64, 1_28, 3_20, 5_12]
    lowerCAmelCase_      :				str      =       7_68
    lowerCAmelCase_      :				Any      =       [3, 6, 40, 3]
  else:
    raise ValueError(f'Size {size} not supported' )

  # load image processor (only resize + normalize)
  lowerCAmelCase_      :				List[Any]      =       SegformerImageProcessor(
      image_scale=(5_12, 5_12)							,   keep_ratio=A__							,   align=A__							,   do_random_crop=A__ )

  # prepare image
  lowerCAmelCase_      :				Optional[Any]      =       prepare_img()
  lowerCAmelCase_      :				Union[str, Any]      =       image_processor(images=A__							,   return_tensors="""pt""" ).pixel_values

  logger.info(f'Converting model {model_name}...' )

  # load original state dict
  if encoder_only:
    lowerCAmelCase_      :				str      =       torch.load(A__							,   map_location=torch.device("""cpu""" ) )
  else:
    lowerCAmelCase_      :				List[str]      =       torch.load(A__							,   map_location=torch.device("""cpu""" ) )["""state_dict"""]

  # rename keys
  lowerCAmelCase_      :				Dict      =       rename_keys(A__							,   encoder_only=A__ )
  if not encoder_only:
    del state_dict["decode_head.conv_seg.weight"]
    del state_dict["decode_head.conv_seg.bias"]

  # key and value matrices need special treatment
  read_in_k_v(A__							,   A__ )

  # create HuggingFace model and load state dict
  if encoder_only:
    lowerCAmelCase_      :				Dict      =       False
    lowerCAmelCase_      :				List[Any]      =       SegformerForImageClassification(A__ )
  else:
    lowerCAmelCase_      :				str      =       SegformerForSemanticSegmentation(A__ )
  model.load_state_dict(A__ )
  model.eval()

  # forward pass
  lowerCAmelCase_      :				Tuple      =       model(A__ )
  lowerCAmelCase_      :				Union[str, Any]      =       outputs.logits

  # set expected_slice based on model name
  # ADE20k checkpoints
  if model_name == "segformer.b0.512x512.ade.160k":
    lowerCAmelCase_      :				Tuple      =       torch.tensor(
        [
            [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
            [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
            [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
        ] )
  elif model_name == "segformer.b1.512x512.ade.160k":
    lowerCAmelCase_      :				List[Any]      =       torch.tensor(
        [
            [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
            [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
            [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
        ] )
  elif model_name == "segformer.b2.512x512.ade.160k":
    lowerCAmelCase_      :				List[Any]      =       torch.tensor(
        [
            [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
            [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
            [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
        ] )
  elif model_name == "segformer.b3.512x512.ade.160k":
    lowerCAmelCase_      :				List[Any]      =       torch.tensor(
        [
            [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
            [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
            [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
        ] )
  elif model_name == "segformer.b4.512x512.ade.160k":
    lowerCAmelCase_      :				List[str]      =       torch.tensor(
        [
            [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
            [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
            [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
        ] )
  elif model_name == "segformer.b5.640x640.ade.160k":
    lowerCAmelCase_      :				List[str]      =       torch.tensor(
        [
            [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
            [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
            [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
        ] )
  # Cityscapes checkpoints
  elif model_name == "segformer.b0.1024x1024.city.160k":
    lowerCAmelCase_      :				Dict      =       torch.tensor(
        [
            [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
            [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
            [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
        ] )
  elif model_name == "segformer.b0.512x1024.city.160k":
    lowerCAmelCase_      :				List[Any]      =       torch.tensor(
        [
            [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
            [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
            [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
        ] )
  elif model_name == "segformer.b0.640x1280.city.160k":
    lowerCAmelCase_      :				Dict      =       torch.tensor(
        [
            [
                [-1.13_72E01, -1.27_87E01, -1.34_77E01],
                [-1.25_36E01, -1.41_94E01, -1.44_09E01],
                [-1.32_17E01, -1.48_88E01, -1.53_27E01],
            ],
            [
                [-1.47_91E01, -1.71_22E01, -1.82_77E01],
                [-1.71_63E01, -1.91_92E01, -1.95_33E01],
                [-1.78_97E01, -1.99_91E01, -2.03_15E01],
            ],
            [
                [7.67_23E-01, 4.19_21E-01, -7.78_78E-02],
                [4.77_72E-01, 9.55_57E-03, -2.80_82E-01],
                [3.60_32E-01, -2.48_26E-01, -5.11_68E-01],
            ],
        ] )
  elif model_name == "segformer.b0.768x768.city.160k":
    lowerCAmelCase_      :				str      =       torch.tensor(
        [
            [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
            [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
            [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
        ] )
  elif model_name == "segformer.b1.1024x1024.city.160k":
    lowerCAmelCase_      :				Optional[int]      =       torch.tensor(
        [
            [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
            [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
            [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
        ] )
  elif model_name == "segformer.b2.1024x1024.city.160k":
    lowerCAmelCase_      :				List[Any]      =       torch.tensor(
        [
            [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
            [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
            [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
        ] )
  elif model_name == "segformer.b3.1024x1024.city.160k":
    lowerCAmelCase_      :				int      =       torch.tensor(
        [
            [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
            [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
            [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
        ] )
  elif model_name == "segformer.b4.1024x1024.city.160k":
    lowerCAmelCase_      :				Union[str, Any]      =       torch.tensor(
        [
            [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
            [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
            [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
        ] )
  elif model_name == "segformer.b5.1024x1024.city.160k":
    lowerCAmelCase_      :				List[Any]      =       torch.tensor(
        [
            [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
            [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
            [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
        ] )
  else:
    lowerCAmelCase_      :				Optional[Any]      =       logits.argmax(-1 ).item()
    print("""Predicted class:"""							,   model.config.idalabel[predicted_class_idx] )

  # verify logits
  if not encoder_only:
    assert logits.shape == expected_shape
    assert torch.allclose(logits[0, :3, :3, :3]							,   A__							,   atol=1E-2 )

  # finally, save model and image processor
  logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
  Path(A__ ).mkdir(exist_ok=A__ )
  model.save_pretrained(A__ )
  image_processor.save_pretrained(A__ )


if __name__ == "__main__":
  __A :  Tuple    							=				argparse.ArgumentParser()

  parser.add_argument(
      "--model_name",
      default="segformer.b0.512x512.ade.160k",
      type=str,
      help="Name of the model you'd like to convert.",
  )
  parser.add_argument(
      "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
  )
  __A :  Tuple    							=				parser.parse_args()
  convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



