


"""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()
__snake_case    					=					logging.get_logger(__name__)


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

 return new_state_dict


def    _lowerCamelCase   (     lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Optional[Any]  ):
 # for each of the encoder blocks:
 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)
   lowercase__       :       Any				=						state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''  )
   lowercase__       :       List[str]				=						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
   lowercase__       :       List[Any]				=						kv_weight[
       : config.hidden_sizes[i], :
   ]
   lowercase__       :       Any				=						kv_bias[: config.hidden_sizes[i]]
   lowercase__       :       Dict				=						kv_weight[
       config.hidden_sizes[i] :, :
   ]
   lowercase__       :       List[str]				=						kv_bias[
       config.hidden_sizes[i] :
   ]


def    _lowerCamelCase   (     ):
 lowercase__       :       Union[str, Any]				=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowercase__       :       Dict				=						Image.open(requests.get(lowerCamelCase__       ,		stream=lowerCamelCase__  ).raw  )

 return image


@torch.no_grad()
def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[Any]       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							List[str]  ):
 lowercase__       :       Tuple				=						SegformerConfig()
 lowercase__       :       str				=						False

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

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

 # load image processor (only resize + normalize)
 lowercase__       :       List[str]				=						SegformerImageProcessor(
     image_scale=(5_12, 5_12)       ,		keep_ratio=lowerCamelCase__       ,		align=lowerCamelCase__       ,		do_random_crop=lowerCamelCase__  )

 # prepare image
 lowercase__       :       Dict				=						prepare_img()
 lowercase__       :       Dict				=						image_processor(images=lowerCamelCase__       ,		return_tensors="""pt"""  ).pixel_values

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

 # load original state dict
 if encoder_only:
  lowercase__       :       Dict				=						torch.load(lowerCamelCase__       ,		map_location=torch.device("""cpu"""  )  )
 else:
  lowercase__       :       Optional[Any]				=						torch.load(lowerCamelCase__       ,		map_location=torch.device("""cpu"""  )  )["""state_dict"""]

 # rename keys
 lowercase__       :       Tuple				=						rename_keys(lowerCamelCase__       ,		encoder_only=lowerCamelCase__  )
 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(lowerCamelCase__       ,		lowerCamelCase__  )

 # create HuggingFace model and load state dict
 if encoder_only:
  lowercase__       :       Optional[int]				=						False
  lowercase__       :       Dict				=						SegformerForImageClassification(lowerCamelCase__  )
 else:
  lowercase__       :       List[Any]				=						SegformerForSemanticSegmentation(lowerCamelCase__  )
 model.load_state_dict(lowerCamelCase__  )
 model.eval()

 # forward pass
 lowercase__       :       Any				=						model(lowerCamelCase__  )
 lowercase__       :       List[Any]				=						outputs.logits

 # set expected_slice based on model name
 # ADE20k checkpoints
 if model_name == "segformer.b0.512x512.ade.160k":
  lowercase__       :       Optional[int]				=						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":
  lowercase__       :       Dict				=						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":
  lowercase__       :       str				=						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":
  lowercase__       :       Tuple				=						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":
  lowercase__       :       Dict				=						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":
  lowercase__       :       Union[str, Any]				=						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":
  lowercase__       :       Tuple				=						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":
  lowercase__       :       Dict				=						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":
  lowercase__       :       Tuple				=						torch.tensor(
      [
          [
              [-1.1372E01, -1.2787E01, -1.3477E01],
              [-1.2536E01, -1.4194E01, -1.4409E01],
              [-1.3217E01, -1.4888E01, -1.5327E01],
          ],
          [
              [-1.4791E01, -1.7122E01, -1.8277E01],
              [-1.7163E01, -1.9192E01, -1.9533E01],
              [-1.7897E01, -1.9991E01, -2.0315E01],
          ],
          [
              [7.6723E-01, 4.1921E-01, -7.7878E-02],
              [4.7772E-01, 9.5557E-03, -2.8082E-01],
              [3.6032E-01, -2.4826E-01, -5.1168E-01],
          ],
      ]  )
 elif model_name == "segformer.b0.768x768.city.160k":
  lowercase__       :       int				=						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":
  lowercase__       :       Tuple				=						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":
  lowercase__       :       Union[str, 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":
  lowercase__       :       List[Any]				=						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":
  lowercase__       :       Dict				=						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":
  lowercase__       :       Optional[int]				=						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:
  lowercase__       :       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]       ,		lowerCamelCase__       ,		atol=1E-2  )

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


if __name__ == "__main__":
   __snake_case    					=					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.'
   )
   __snake_case    					=					parser.parse_args()
   convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



"""simple docstring"""


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case    					=					{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					[
       'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'InformerForPrediction',
       'InformerModel',
       'InformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   __snake_case    					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)