





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


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

       return new_state_dict


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       # 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)
                     _UpperCamelCase              =						state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''					)
                     _UpperCamelCase              =						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
                     _UpperCamelCase              =						kv_weight[
                         : config.hidden_sizes[i], :
                     ]
                     _UpperCamelCase              =						kv_bias[: config.hidden_sizes[i]]
                     _UpperCamelCase              =						kv_weight[
                         config.hidden_sizes[i] :, :
                     ]
                     _UpperCamelCase              =						kv_bias[
                         config.hidden_sizes[i] :
                     ]


def     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _UpperCamelCase              =						Image.open(requests.get(lowerCAmelCase    ,       stream=lowerCAmelCase					).raw					)

       return image


@torch.no_grad()
def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						SegformerConfig()
       _UpperCamelCase              =						False

       # set attributes based on model_name
       _UpperCamelCase              =						'''huggingface/label-files'''
       if "segformer" in model_name:
              _UpperCamelCase              =						model_name[len('''segformer.'''					) : len('''segformer.'''					) + 2]
              if "ade" in model_name:
                     _UpperCamelCase              =						150
                     _UpperCamelCase              =						'''ade20k-id2label.json'''
                     _UpperCamelCase              =						(1, 150, 128, 128)
              elif "city" in model_name:
                     _UpperCamelCase              =						19
                     _UpperCamelCase              =						'''cityscapes-id2label.json'''
                     _UpperCamelCase              =						(1, 19, 128, 128)
              else:
                     raise ValueError(f'''Model {model_name} not supported'''					)
       elif "mit" in model_name:
              _UpperCamelCase              =						True
              _UpperCamelCase              =						model_name[4:6]
              _UpperCamelCase              =						1_000
              _UpperCamelCase              =						'''imagenet-1k-id2label.json'''
              _UpperCamelCase              =						(1, 1_000)
       else:
              raise ValueError(f'''Model {model_name} not supported'''					)

       # set config attributes
       _UpperCamelCase              =						json.load(open(hf_hub_download(lowerCAmelCase    ,       lowerCAmelCase    ,       repo_type='''dataset'''					)    ,       '''r'''					)					)
       _UpperCamelCase              =						{int(lowerCAmelCase					): v for k, v in idalabel.items()}
       _UpperCamelCase              =						idalabel
       _UpperCamelCase              =						{v: k for k, v in idalabel.items()}
       if size == "b0":
              pass
       elif size == "b1":
              _UpperCamelCase              =						[64, 128, 320, 512]
              _UpperCamelCase              =						256
       elif size == "b2":
              _UpperCamelCase              =						[64, 128, 320, 512]
              _UpperCamelCase              =						768
              _UpperCamelCase              =						[3, 4, 6, 3]
       elif size == "b3":
              _UpperCamelCase              =						[64, 128, 320, 512]
              _UpperCamelCase              =						768
              _UpperCamelCase              =						[3, 4, 18, 3]
       elif size == "b4":
              _UpperCamelCase              =						[64, 128, 320, 512]
              _UpperCamelCase              =						768
              _UpperCamelCase              =						[3, 8, 27, 3]
       elif size == "b5":
              _UpperCamelCase              =						[64, 128, 320, 512]
              _UpperCamelCase              =						768
              _UpperCamelCase              =						[3, 6, 40, 3]
       else:
              raise ValueError(f'''Size {size} not supported'''					)

       # load image processor (only resize + normalize)
       _UpperCamelCase              =						SegformerImageProcessor(
           image_scale=(512, 512)    ,       keep_ratio=lowerCAmelCase    ,       align=lowerCAmelCase    ,       do_random_crop=lowerCAmelCase					)

       # prepare image
       _UpperCamelCase              =						prepare_img()
       _UpperCamelCase              =						image_processor(images=lowerCAmelCase    ,       return_tensors='''pt'''					).pixel_values

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

       # load original state dict
       if encoder_only:
              _UpperCamelCase              =						torch.load(lowerCAmelCase    ,       map_location=torch.device('''cpu'''					)					)
       else:
              _UpperCamelCase              =						torch.load(lowerCAmelCase    ,       map_location=torch.device('''cpu'''					)					)['''state_dict''']

       # rename keys
       _UpperCamelCase              =						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:
              _UpperCamelCase              =						False
              _UpperCamelCase              =						SegformerForImageClassification(lowerCAmelCase					)
       else:
              _UpperCamelCase              =						SegformerForSemanticSegmentation(lowerCAmelCase					)
       model.load_state_dict(lowerCAmelCase					)
       model.eval()

       # forward pass
       _UpperCamelCase              =						model(lowerCAmelCase					)
       _UpperCamelCase              =						outputs.logits

       # set expected_slice based on model name
       # ADE20k checkpoints
       if model_name == "segformer.b0.512x512.ade.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
                      [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
                      [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
                  ]					)
       elif model_name == "segformer.b1.512x512.ade.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
                      [[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
                      [[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
                  ]					)
       elif model_name == "segformer.b2.512x512.ade.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
                      [[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
                      [[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
                  ]					)
       elif model_name == "segformer.b3.512x512.ade.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -1_0.3_8_5_5, -1_0.5_7_0_4]],
                      [[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
                      [[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
                  ]					)
       elif model_name == "segformer.b4.512x512.ade.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
                      [[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
                      [[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
                  ]					)
       elif model_name == "segformer.b5.640x640.ade.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-9.5_5_2_4, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.5_8_4_2, -1_2.8_8_5_1, -1_3.9_4_1_4]],
                      [[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
                      [[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
                  ]					)
       # Cityscapes checkpoints
       elif model_name == "segformer.b0.1024x1024.city.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
                      [[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
                      [[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
                  ]					)
       elif model_name == "segformer.b0.512x1024.city.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
                      [[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
                      [[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
                  ]					)
       elif model_name == "segformer.b0.640x1280.city.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [
                          [-1.1_372E01, -1.2_787E01, -1.3_477E01],
                          [-1.2_536E01, -1.4_194E01, -1.4_409E01],
                          [-1.3_217E01, -1.4_888E01, -1.5_327E01],
                      ],
                      [
                          [-1.4_791E01, -1.7_122E01, -1.8_277E01],
                          [-1.7_163E01, -1.9_192E01, -1.9_533E01],
                          [-1.7_897E01, -1.9_991E01, -2.0_315E01],
                      ],
                      [
                          [7.6_723E-01, 4.1_921E-01, -7.7_878E-02],
                          [4.7_772E-01, 9.5_557E-03, -2.8_082E-01],
                          [3.6_032E-01, -2.4_826E-01, -5.1_168E-01],
                      ],
                  ]					)
       elif model_name == "segformer.b0.768x768.city.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-9.4_9_5_9, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
                      [[-9.8_9_0_5, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
                      [[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
                  ]					)
       elif model_name == "segformer.b1.1024x1024.city.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
                      [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
                      [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
                  ]					)
       elif model_name == "segformer.b2.1024x1024.city.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
                      [[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
                      [[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
                  ]					)
       elif model_name == "segformer.b3.1024x1024.city.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
                      [[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
                      [[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
                  ]					)
       elif model_name == "segformer.b4.1024x1024.city.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
                      [[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
                      [[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
                  ]					)
       elif model_name == "segformer.b5.1024x1024.city.160k":
              _UpperCamelCase              =						torch.tensor(
                  [
                      [[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
                      [[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
                      [[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
                  ]					)
       else:
              _UpperCamelCase              =						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__":
       lowercase    : Any    	=  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."""
       )
       lowercase    : str    	=  parser.parse_args()
       convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)













import datasets

from .evaluate import evaluate


lowercase    : str    	=  """\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
"""

lowercase    : Tuple    	=  """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

lowercase    : List[str]    	=  """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION )
class  __A(						datasets.Metric ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return datasets.MetricInfo(
                description=_DESCRIPTION,       citation=_CITATION,       inputs_description=_KWARGS_DESCRIPTION,       features=datasets.Features(
                    {
                        '''predictions''': {
                            '''id''': datasets.Value('''string'''      ),
                            '''prediction_text''': datasets.features.Sequence(datasets.Value('''string'''      )      ),
                        },
                        '''references''': {
                            '''id''': datasets.Value('''string'''      ),
                            '''answers''': datasets.features.Sequence(
                                {
                                    '''text''': datasets.Value('''string'''      ),
                                    '''answer_start''': datasets.Value('''int32'''      ),
                                }      ),
                        },
                    }      ),       codebase_urls=['''https://www.atticusprojectai.org/cuad'''],       reference_urls=['''https://www.atticusprojectai.org/cuad'''],       )



     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
            _UpperCamelCase              =						[
                {
                    '''paragraphs''': [
                        {
                            '''qas''': [
                                {
                                    '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                                    '''id''': ref['''id'''],
                                }
                                for ref in references
                            ]
                        }
                    ]
                }
            ]
            _UpperCamelCase              =						evaluate(dataset=A,       predictions=A      )
            return score






