




"""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_						=				logging.get_logger(__name__)


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					Optional[Any]=False    ):
  """simple docstring"""

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

  return new_state_dict


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					Dict    ):
  """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)
      _snake_case							:		Tuple								=  state_dict.pop(F"segformer.encoder.block.{i}.{j}.attention.self.kv.weight"    )
      _snake_case							:		Tuple								=  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
      _snake_case							:		List[Any]								=  kv_weight[
          : config.hidden_sizes[i], :
      ]
      _snake_case							:		List[str]								=  kv_bias[: config.hidden_sizes[i]]
      _snake_case							:		Any								=  kv_weight[
          config.hidden_sizes[i] :, :
      ]
      _snake_case							:		Optional[Any]								=  kv_bias[
          config.hidden_sizes[i] :
      ]


def 					UpperCAmelCase__       ():
  """simple docstring"""

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

  return image


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					int		,   snake_case__							:					List[str]    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  SegformerConfig()
  _snake_case							:		Optional[Any]								=  False

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

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

  # load image processor (only resize + normalize)
  _snake_case							:		Optional[int]								=  SegformerImageProcessor(
      image_scale=(5_12, 5_12)		,   keep_ratio=snake_case__		,   align=snake_case__		,   do_random_crop=snake_case__    )

  # prepare image
  _snake_case							:		Dict								=  prepare_img()
  _snake_case							:		List[Any]								=  image_processor(images=snake_case__		,   return_tensors="""pt"""    ).pixel_values

  logger.info(F"Converting model {model_name}..."    )

  # load original state dict
  if encoder_only:
    _snake_case							:		Dict								=  torch.load(snake_case__		,   map_location=torch.device("""cpu"""    )    )
  else:
    _snake_case							:		str								=  torch.load(snake_case__		,   map_location=torch.device("""cpu"""    )    )["""state_dict"""]

  # rename keys
  _snake_case							:		int								=  rename_keys(snake_case__		,   encoder_only=snake_case__    )
  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(snake_case__		,   snake_case__    )

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

  # forward pass
  _snake_case							:		List[Any]								=  model(snake_case__    )
  _snake_case							:		Any								=  outputs.logits

  # set expected_slice based on model name
  # ADE20k checkpoints
  if model_name == "segformer.b0.512x512.ade.160k":
    _snake_case							:		Tuple								=  torch.tensor(
        [
            [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
            [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
            [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
        ]    )
  elif model_name == "segformer.b1.512x512.ade.160k":
    _snake_case							:		Union[str, Any]								=  torch.tensor(
        [
            [[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -10.35_29, -10.03_04], [-7.52_08, -9.41_03, -9.62_39]],
            [[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
            [[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
        ]    )
  elif model_name == "segformer.b2.512x512.ade.160k":
    _snake_case							:		List[Any]								=  torch.tensor(
        [
            [[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
            [[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
            [[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
        ]    )
  elif model_name == "segformer.b3.512x512.ade.160k":
    _snake_case							:		Union[str, Any]								=  torch.tensor(
        [
            [[-9.08_78, -10.20_81, -10.18_91], [-9.31_44, -10.79_41, -10.98_43], [-9.22_94, -10.38_55, -10.57_04]],
            [[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
            [[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
        ]    )
  elif model_name == "segformer.b4.512x512.ade.160k":
    _snake_case							:		Union[str, Any]								=  torch.tensor(
        [
            [[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
            [[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
            [[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
        ]    )
  elif model_name == "segformer.b5.640x640.ade.160k":
    _snake_case							:		List[Any]								=  torch.tensor(
        [
            [[-9.55_24, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.58_42, -12.88_51, -13.94_14]],
            [[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
            [[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
        ]    )
  # Cityscapes checkpoints
  elif model_name == "segformer.b0.1024x1024.city.160k":
    _snake_case							:		Optional[Any]								=  torch.tensor(
        [
            [[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
            [[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
            [[0.53_74, 0.10_67, -0.47_42], [0.11_41, -0.22_55, -0.70_99], [-0.30_00, -0.59_24, -1.31_05]],
        ]    )
  elif model_name == "segformer.b0.512x1024.city.160k":
    _snake_case							:		Optional[Any]								=  torch.tensor(
        [
            [[-7.82_17, -9.87_67, -10.17_17], [-9.44_38, -10.90_58, -11.40_47], [-9.79_39, -12.34_95, -12.10_79]],
            [[-7.15_14, -9.53_36, -10.08_60], [-9.77_76, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
            [[0.30_21, 0.08_05, -0.23_10], [-0.03_28, -0.16_05, -0.27_14], [-0.14_08, -0.54_77, -0.69_76]],
        ]    )
  elif model_name == "segformer.b0.640x1280.city.160k":
    _snake_case							:		str								=  torch.tensor(
        [
            [
                [-1.1_3_7_2e0_1, -1.2_7_8_7e0_1, -1.3_4_7_7e0_1],
                [-1.2_5_3_6e0_1, -1.4_1_9_4e0_1, -1.4_4_0_9e0_1],
                [-1.3_2_1_7e0_1, -1.4_8_8_8e0_1, -1.5_3_2_7e0_1],
            ],
            [
                [-1.4_7_9_1e0_1, -1.7_1_2_2e0_1, -1.8_2_7_7e0_1],
                [-1.7_1_6_3e0_1, -1.9_1_9_2e0_1, -1.9_5_3_3e0_1],
                [-1.7_8_9_7e0_1, -1.9_9_9_1e0_1, -2.0_3_1_5e0_1],
            ],
            [
                [7.6_7_2_3e-0_1, 4.1_9_2_1e-0_1, -7.7_8_7_8e-0_2],
                [4.7_7_7_2e-0_1, 9.5_5_5_7e-0_3, -2.8_0_8_2e-0_1],
                [3.6_0_3_2e-0_1, -2.4_8_2_6e-0_1, -5.1_1_6_8e-0_1],
            ],
        ]    )
  elif model_name == "segformer.b0.768x768.city.160k":
    _snake_case							:		Any								=  torch.tensor(
        [
            [[-9.49_59, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
            [[-9.89_05, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
            [[0.22_13, 0.01_92, -0.24_66], [-0.17_31, -0.42_13, -0.48_74], [-0.31_26, -0.65_41, -1.13_89]],
        ]    )
  elif model_name == "segformer.b1.1024x1024.city.160k":
    _snake_case							:		Tuple								=  torch.tensor(
        [
            [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
            [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
            [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
        ]    )
  elif model_name == "segformer.b2.1024x1024.city.160k":
    _snake_case							:		List[Any]								=  torch.tensor(
        [
            [[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
            [[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
            [[-4.51_78, -5.50_37, -6.51_09], [-5.08_84, -7.21_74, -8.03_34], [-4.41_56, -5.81_17, -7.29_70]],
        ]    )
  elif model_name == "segformer.b3.1024x1024.city.160k":
    _snake_case							:		int								=  torch.tensor(
        [
            [[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
            [[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
            [[-4.73_49, -4.95_88, -5.09_66], [-4.32_10, -6.93_25, -7.25_91], [-3.43_12, -4.74_84, -7.19_17]],
        ]    )
  elif model_name == "segformer.b4.1024x1024.city.160k":
    _snake_case							:		Tuple								=  torch.tensor(
        [
            [[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
            [[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
            [[1.04_91, 0.82_89, 1.03_10], [1.10_44, 0.52_19, 0.80_55], [1.08_99, 0.69_26, 0.55_90]],
        ]    )
  elif model_name == "segformer.b5.1024x1024.city.160k":
    _snake_case							:		List[Any]								=  torch.tensor(
        [
            [[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
            [[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
            [[-1.79_90, -2.09_51, -1.77_84], [-2.63_97, -3.82_45, -3.96_86], [-1.52_64, -2.81_26, -2.93_16]],
        ]    )
  else:
    _snake_case							:		Optional[int]								=  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]		,   snake_case__		,   atol=1e-2    )

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


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








"""simple docstring"""


from __future__ import annotations


def 					UpperCAmelCase__       (snake_case__							:					list[int]		,   snake_case__							:					int    ):
  """simple docstring"""

  if len(snake_case__    ) < k or k < 0:
    raise ValueError("""Invalid Input"""    )
  _snake_case							:		Optional[int]								=  sum(array[:k]    )
  for i in range(len(snake_case__    ) - k    ):
    _snake_case							:		Optional[Any]								=  current_sum - array[i] + array[i + k]
    _snake_case							:		List[str]								=  max(snake_case__		,   snake_case__    )
  return max_sum


if __name__ == "__main__":
     from doctest import testmod
     from random import randint

     testmod()
     A_						=				[randint(-10_00, 10_00) for i in range(1_00)]
     A_						=				randint(0, 1_10)
     print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')


