




from __future__ import annotations

from math import gcd




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase = 2						,lowercase = 1						,lowercase = 3						,)  ->       int | None:
   # A value less than 2 can cause an infinite loop in the algorithm.
   if num < 2:
      raise ValueError("""The input value cannot be less than 2"""					)

   # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
   # algorithm struggles to find factors that are divisible by two.
   # As a workaround, we specifically check for two and even inputs.
   #   See: https://math.stackexchange.com/a/2856214/165820
   if num > 2 and num % 2 == 0:
      return 2

   # Pollard's Rho algorithm requires a function that returns pseudorandom
   # values between 0 <= X < ``num``.  It doesn't need to be random in the
   # sense that the output value is cryptographically secure or difficult
   # to calculate, it only needs to be random in the sense that all output
   # values should be equally likely to appear.
   # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
   # However, the success of Pollard's algorithm isn't guaranteed and is
   # determined in part by the initial seed and the chosen random function.
   # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
   # where ``C`` is a value that we can modify between each attempt.
   def rand_fn(lowercase						,lowercase						,lowercase					) -> int:
      return (pow(lowercase						,2					) + step) % modulus

   for _ in range(lowercase					):
      # These track the position within the cycle detection logic.
      snake_case				:    Any           =	seed
      snake_case				:    Optional[Any]           =	seed

      while True:
         # At each iteration, the tortoise moves one step and the hare moves two.
         snake_case				:    int           =	rand_fn(lowercase						,lowercase						,lowercase					)
         snake_case				:    Optional[Any]           =	rand_fn(lowercase						,lowercase						,lowercase					)
         snake_case				:    Any           =	rand_fn(lowercase						,lowercase						,lowercase					)

         # At some point both the tortoise and the hare will enter a cycle whose
         # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
         # the tortoise and hare will end up on the same value modulo ``p``.
         # We can detect when this happens because the position difference between
         # the tortoise and the hare will share a common divisor with ``num``.
         snake_case				:    List[str]           =	gcd(hare - tortoise						,lowercase					)

         if divisor == 1:
            # No common divisor yet, just keep searching.
            continue
         else:
            # We found a common divisor!
            if divisor == num:
               # Unfortunately, the divisor is ``num`` itself and is useless.
               break
            else:
               # The divisor is a nontrivial factor of ``num``!
               return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
      snake_case				:    Tuple           =	hare

      # The new step value for the random function can just be incremented.
      # At first the results will be similar to what the old function would
      # have produced, but the value will quickly diverge after a bit.
      step += 1

   # We haven't found a divisor within the requested number of attempts.
   # We were unlucky or ``num`` itself is actually prime.
   return None


if __name__ == "__main__":
       import argparse

       lowerCamelCase	:	List[str]				=   argparse.ArgumentParser()
       parser.add_argument(
           'num',
           type=int,
           help='The value to find a divisor of',
       )
       parser.add_argument(
           '--attempts',
           type=int,
           default=3,
           help='The number of attempts before giving up',
       )
       lowerCamelCase	:	Optional[int]				=   parser.parse_args()

       lowerCamelCase	:	str				=   pollard_rho(args.num, attempts=args.attempts)
       if divisor is None:
              print(f"""{args.num} is probably prime""")
       else:
              lowerCamelCase	:	int				=   args.num // divisor
              print(f"""{args.num} = {divisor} * {quotient}""")











import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	:	List[Any]				=   logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       int:
   snake_case				:    Dict           =	SwinConfig.from_pretrained(
       """microsoft/swin-tiny-patch4-window7-224"""						,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
   snake_case				:    List[str]           =	MaskFormerConfig(backbone_config=lowercase					)

   snake_case				:    List[Any]           =	"""huggingface/label-files"""
   if "ade20k-full" in model_name:
      # this should be ok
      snake_case				:    Dict           =	847
      snake_case				:    List[str]           =	"""maskformer-ade20k-full-id2label.json"""
   elif "ade" in model_name:
      # this should be ok
      snake_case				:    Union[str, Any]           =	150
      snake_case				:    List[Any]           =	"""ade20k-id2label.json"""
   elif "coco-stuff" in model_name:
      # this should be ok
      snake_case				:    Union[str, Any]           =	171
      snake_case				:    int           =	"""maskformer-coco-stuff-id2label.json"""
   elif "coco" in model_name:
      # TODO
      snake_case				:    Optional[Any]           =	133
      snake_case				:    Optional[Any]           =	"""coco-panoptic-id2label.json"""
   elif "cityscapes" in model_name:
      # this should be ok
      snake_case				:    Tuple           =	19
      snake_case				:    int           =	"""cityscapes-id2label.json"""
   elif "vistas" in model_name:
      # this should be ok
      snake_case				:    int           =	65
      snake_case				:    Any           =	"""mapillary-vistas-id2label.json"""

   snake_case				:    Optional[Any]           =	json.load(open(hf_hub_download(lowercase						,lowercase						,repo_type="""dataset"""					)						,"""r"""					)					)
   snake_case				:    List[Any]           =	{int(lowercase					): v for k, v in idalabel.items()}

   return config




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   snake_case				:    Union[str, Any]           =	[]
   # stem
   # fmt: off
   rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")					)
   rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")					)
   rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")					)
   rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")					)
   # stages
   for i in range(len(config.backbone_config.depths					)					):
      for j in range(config.backbone_config.depths[i]					):
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")					)

      if i < 3:
         rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")					)
         rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")					)
      rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")					)
      rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")					)

   # FPN
   rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")					)
   rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")					)
   rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")					)
   for source_index, target_index in zip(range(3						,0						,-1					)						,range(0						,3					)					):
      rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")					)
      rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")					)
      rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")					)
      rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")					)
      rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")					)
      rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")					)
   rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")					)
   rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")					)

   # Transformer decoder
   for idx in range(config.decoder_config.decoder_layers					):
      # self-attention out projection
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")					)
      # cross-attention out projection
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")					)
      # MLP 1
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")					)
      # MLP 2
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")					)
      # layernorm 1 (self-attention layernorm)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")					)
      # layernorm 2 (cross-attention layernorm)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")					)
      # layernorm 3 (final layernorm)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")					)

   rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")					)
   rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")					)

   # heads on top
   rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")					)

   rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")					)
   rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")					)

   rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")					)
   rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")					)

   for i in range(3					):
      rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")					)
      rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")					)
   # fmt: on

   return rename_keys




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       str:
   snake_case				:    Tuple           =	dct.pop(lowercase					)
   snake_case				:    int           =	val




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:
   snake_case				:    List[str]           =	[int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
   for i in range(len(backbone_config.depths					)					):
      snake_case				:    Optional[Any]           =	num_features[i]
      for j in range(backbone_config.depths[i]					):
         # fmt: off
         # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
         snake_case				:    Tuple           =	state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
         snake_case				:    Optional[Any]           =	state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""					)
         # next, add query, keys and values (in that order) to the state dict
         snake_case				:    Optional[Any]           =	in_proj_weight[:dim, :]
         snake_case				:    Optional[int]           =	in_proj_bias[: dim]
         snake_case				:    Union[str, Any]           =	in_proj_weight[
             dim : dim * 2, :
         ]
         snake_case				:    Tuple           =	in_proj_bias[
             dim : dim * 2
         ]
         snake_case				:    List[Any]           =	in_proj_weight[
             -dim :, :
         ]
         snake_case				:    Any           =	in_proj_bias[-dim :]
         # fmt: on




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Any:
   # fmt: off
   snake_case				:    int           =	config.decoder_config.hidden_size
   for idx in range(config.decoder_config.decoder_layers					):
      # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
      snake_case				:    Any           =	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""					)
      snake_case				:    Tuple           =	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""					)
      # next, add query, keys and values (in that order) to the state dict
      snake_case				:    Optional[int]           =	in_proj_weight[: hidden_size, :]
      snake_case				:    Any           =	in_proj_bias[:config.hidden_size]
      snake_case				:    Any           =	in_proj_weight[hidden_size : hidden_size * 2, :]
      snake_case				:    int           =	in_proj_bias[hidden_size : hidden_size * 2]
      snake_case				:    Any           =	in_proj_weight[-hidden_size :, :]
      snake_case				:    Union[str, Any]           =	in_proj_bias[-hidden_size :]
      # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
      snake_case				:    Dict           =	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""					)
      snake_case				:    str           =	state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""					)
      # next, add query, keys and values (in that order) to the state dict
      snake_case				:    Dict           =	in_proj_weight[: hidden_size, :]
      snake_case				:    Dict           =	in_proj_bias[:config.hidden_size]
      snake_case				:    List[Any]           =	in_proj_weight[hidden_size : hidden_size * 2, :]
      snake_case				:    Optional[Any]           =	in_proj_bias[hidden_size : hidden_size * 2]
      snake_case				:    Tuple           =	in_proj_weight[-hidden_size :, :]
      snake_case				:    str           =	in_proj_bias[-hidden_size :]
   # fmt: on




def 						SCREAMING_SNAKE_CASE__ (   )  ->       torch.Tensor:
   snake_case				:    Any           =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case				:    Any           =	Image.open(requests.get(lowercase						,stream=lowercase					).raw					)
   return im




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase = False					)  ->       Dict:
   snake_case				:    List[str]           =	get_maskformer_config(lowercase					)

   # load original state_dict
   with open(lowercase						,"""rb"""					) as f:
      snake_case				:    Optional[Any]           =	pickle.load(lowercase					)
   snake_case				:    Optional[Any]           =	data["""model"""]

   # for name, param in state_dict.items():
   #     print(name, param.shape)

   # rename keys
   snake_case				:    str           =	create_rename_keys(lowercase					)
   for src, dest in rename_keys:
      rename_key(lowercase						,lowercase						,lowercase					)
   read_in_swin_q_k_v(lowercase						,config.backbone_config					)
   read_in_decoder_q_k_v(lowercase						,lowercase					)

   # update to torch tensors
   for key, value in state_dict.items():
      snake_case				:    List[Any]           =	torch.from_numpy(lowercase					)

   # load 🤗 model
   snake_case				:    int           =	MaskFormerForInstanceSegmentation(lowercase					)
   model.eval()

   for name, param in model.named_parameters():
      print(lowercase						,param.shape					)

   snake_case     , snake_case				:    Optional[int]           =	model.load_state_dict(lowercase						,strict=lowercase					)
   assert missing_keys == [
       "model.pixel_level_module.encoder.model.layernorm.weight",
       "model.pixel_level_module.encoder.model.layernorm.bias",
   ]
   assert len(lowercase					) == 0, f"""Unexpected keys: {unexpected_keys}"""

   # verify results
   snake_case				:    List[str]           =	prepare_img()
   if "vistas" in model_name:
      snake_case				:    Optional[int]           =	65
   elif "cityscapes" in model_name:
      snake_case				:    int           =	65535
   else:
      snake_case				:    List[str]           =	255
   snake_case				:    List[Any]           =	True if """ade""" in model_name else False
   snake_case				:    Optional[int]           =	MaskFormerImageProcessor(ignore_index=lowercase						,reduce_labels=lowercase					)

   snake_case				:    Tuple           =	image_processor(lowercase						,return_tensors="""pt"""					)

   snake_case				:    str           =	model(**lowercase					)

   print("""Logits:"""						,outputs.class_queries_logits[0, :3, :3]					)

   if model_name == "maskformer-swin-tiny-ade":
      snake_case				:    Union[str, Any]           =	torch.tensor(
          [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]					)
   assert torch.allclose(outputs.class_queries_logits[0, :3, :3]						,lowercase						,atol=1E-4					)
   print("""Looks ok!"""					)

   if pytorch_dump_folder_path is not None:
      print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""					)
      Path(lowercase					).mkdir(exist_ok=lowercase					)
      model.save_pretrained(lowercase					)
      image_processor.save_pretrained(lowercase					)

   if push_to_hub:
      print("""Pushing model and image processor to the hub..."""					)
      model.push_to_hub(f"""nielsr/{model_name}"""					)
      image_processor.push_to_hub(f"""nielsr/{model_name}"""					)


if __name__ == "__main__":
       lowerCamelCase	:	Optional[Any]				=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='maskformer-swin-tiny-ade',
           type=str,
           help=('Name of the MaskFormer model you\'d like to convert',),
       )
       parser.add_argument(
           '--checkpoint_path',
           default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
           type=str,
           help='Path to the original state dict (.pth file).',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )

       lowerCamelCase	:	List[str]				=   parser.parse_args()
       convert_maskformer_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )





