



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()
__lowerCamelCase         =			logging.get_logger(__name__)







def    _snake_case (       __snake_case					,  __snake_case=False    )     ->     Tuple:






      '''simple docstring'''







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

      return new_state_dict







def    _snake_case (       __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      for i in range(config.num_encoder_blocks    ):
            for j in range(config.depths[i]    ):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  UpperCAmelCase_  :	int				      =		state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""    )
                  UpperCAmelCase_  :	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
                  UpperCAmelCase_  :	Any				      =		kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCAmelCase_  :	Any				      =		kv_bias[: config.hidden_sizes[i]]
                  UpperCAmelCase_  :	Optional[int]				      =		kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCAmelCase_  :	List[Any]				      =		kv_bias[
                      config.hidden_sizes[i] :
                  ]







def    _snake_case (       )     ->     Optional[int]:






      '''simple docstring'''







      UpperCAmelCase_  :	str				      =		"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_  :	Any				      =		Image.open(requests.get(__snake_case					,  stream=__snake_case    ).raw    )

      return image







@torch.no_grad()
def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     Any:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		SegformerConfig()
      UpperCAmelCase_  :	Any				      =		False

      # set attributes based on model_name
      UpperCAmelCase_  :	Union[str, Any]				      =		"huggingface/label-files"
      if "segformer" in model_name:
            UpperCAmelCase_  :	Optional[Any]				      =		model_name[len("segformer."    ) : len("segformer."    ) + 2]
            if "ade" in model_name:
                  UpperCAmelCase_  :	Any				      =		1_5_0
                  UpperCAmelCase_  :	List[Any]				      =		"ade20k-id2label.json"
                  UpperCAmelCase_  :	Union[str, Any]				      =		(1, 1_5_0, 1_2_8, 1_2_8)
            elif "city" in model_name:
                  UpperCAmelCase_  :	int				      =		1_9
                  UpperCAmelCase_  :	Union[str, Any]				      =		"cityscapes-id2label.json"
                  UpperCAmelCase_  :	Dict				      =		(1, 1_9, 1_2_8, 1_2_8)
            else:
                  raise ValueError(F"""Model {model_name} not supported"""    )
      elif "mit" in model_name:
            UpperCAmelCase_  :	List[Any]				      =		True
            UpperCAmelCase_  :	str				      =		model_name[4:6]
            UpperCAmelCase_  :	str				      =		1_0_0_0
            UpperCAmelCase_  :	str				      =		"imagenet-1k-id2label.json"
            UpperCAmelCase_  :	Optional[int]				      =		(1, 1_0_0_0)
      else:
            raise ValueError(F"""Model {model_name} not supported"""    )

      # set config attributes
      UpperCAmelCase_  :	Tuple				      =		json.load(open(hf_hub_download(__snake_case					,  __snake_case					,  repo_type="dataset"    )					,  "r"    )    )
      UpperCAmelCase_  :	Optional[Any]				      =		{int(__snake_case    ): v for k, v in idalabel.items()}
      UpperCAmelCase_  :	Optional[int]				      =		idalabel
      UpperCAmelCase_  :	List[str]				      =		{v: k for k, v in idalabel.items()}
      if size == "b0":
            pass
      elif size == "b1":
            UpperCAmelCase_  :	Optional[int]				      =		[6_4, 1_2_8, 3_2_0, 5_1_2]
            UpperCAmelCase_  :	List[Any]				      =		2_5_6
      elif size == "b2":
            UpperCAmelCase_  :	Tuple				      =		[6_4, 1_2_8, 3_2_0, 5_1_2]
            UpperCAmelCase_  :	Optional[Any]				      =		7_6_8
            UpperCAmelCase_  :	Optional[Any]				      =		[3, 4, 6, 3]
      elif size == "b3":
            UpperCAmelCase_  :	List[str]				      =		[6_4, 1_2_8, 3_2_0, 5_1_2]
            UpperCAmelCase_  :	Union[str, Any]				      =		7_6_8
            UpperCAmelCase_  :	Optional[int]				      =		[3, 4, 1_8, 3]
      elif size == "b4":
            UpperCAmelCase_  :	List[str]				      =		[6_4, 1_2_8, 3_2_0, 5_1_2]
            UpperCAmelCase_  :	List[Any]				      =		7_6_8
            UpperCAmelCase_  :	Any				      =		[3, 8, 2_7, 3]
      elif size == "b5":
            UpperCAmelCase_  :	List[Any]				      =		[6_4, 1_2_8, 3_2_0, 5_1_2]
            UpperCAmelCase_  :	Dict				      =		7_6_8
            UpperCAmelCase_  :	Any				      =		[3, 6, 4_0, 3]
      else:
            raise ValueError(F"""Size {size} not supported"""    )

      # load image processor (only resize + normalize)
      UpperCAmelCase_  :	Union[str, Any]				      =		SegformerImageProcessor(
          image_scale=(5_1_2, 5_1_2)					,  keep_ratio=__snake_case					,  align=__snake_case					,  do_random_crop=__snake_case    )

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

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

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

      # rename keys
      UpperCAmelCase_  :	Tuple				      =		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:
            UpperCAmelCase_  :	Tuple				      =		False
            UpperCAmelCase_  :	Dict				      =		SegformerForImageClassification(__snake_case    )
      else:
            UpperCAmelCase_  :	Tuple				      =		SegformerForSemanticSegmentation(__snake_case    )
      model.load_state_dict(__snake_case    )
      model.eval()

      # forward pass
      UpperCAmelCase_  :	Union[str, Any]				      =		model(__snake_case    )
      UpperCAmelCase_  :	Union[str, Any]				      =		outputs.logits

      # set expected_slice based on model name
      # ADE20k checkpoints
      if model_name == "segformer.b0.512x512.ade.160k":
            UpperCAmelCase_  :	Any				      =		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":
            UpperCAmelCase_  :	List[Any]				      =		torch.tensor(
                [
                    [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
                    [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
                    [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
                ]    )
      elif model_name == "segformer.b2.512x512.ade.160k":
            UpperCAmelCase_  :	int				      =		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":
            UpperCAmelCase_  :	List[Any]				      =		torch.tensor(
                [
                    [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
                    [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
                    [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
                ]    )
      elif model_name == "segformer.b4.512x512.ade.160k":
            UpperCAmelCase_  :	Any				      =		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":
            UpperCAmelCase_  :	Tuple				      =		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":
            UpperCAmelCase_  :	Optional[int]				      =		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":
            UpperCAmelCase_  :	Optional[int]				      =		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":
            UpperCAmelCase_  :	List[Any]				      =		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":
            UpperCAmelCase_  :	Tuple				      =		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":
            UpperCAmelCase_  :	str				      =		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":
            UpperCAmelCase_  :	Optional[int]				      =		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":
            UpperCAmelCase_  :	str				      =		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":
            UpperCAmelCase_  :	Optional[Any]				      =		torch.tensor(
                [
                    [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
                    [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
                    [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
                ]    )
      elif model_name == "segformer.b5.1024x1024.city.160k":
            UpperCAmelCase_  :	Tuple				      =		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:
            UpperCAmelCase_  :	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]					,  __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__":
   __lowerCamelCase         =			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.'''
   )
   __lowerCamelCase         =			parser.parse_args()
   convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
   import torch

   from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,lowercase="resnet50"     ,lowercase=3     ,lowercase=32     ,lowercase=3     ,lowercase=True     ,lowercase=True     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		parent
             UpperCAmelCase_  :	int				      =		out_indices if out_indices is not None else [4]
             UpperCAmelCase_  :	Any				      =		stage_names
             UpperCAmelCase_  :	List[Any]				      =		out_features
             UpperCAmelCase_  :	List[str]				      =		backbone
             UpperCAmelCase_  :	Union[str, Any]				      =		batch_size
             UpperCAmelCase_  :	int				      =		image_size
             UpperCAmelCase_  :	str				      =		num_channels
             UpperCAmelCase_  :	Optional[Any]				      =		use_pretrained_backbone
             UpperCAmelCase_  :	Optional[int]				      =		is_training



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
             UpperCAmelCase_  :	Dict				      =		self.get_config()

             return config, pixel_values



       def  A_   (     self):

             """simple docstring"""





             return TimmBackboneConfig(
                 image_size=self.image_size     ,num_channels=self.num_channels     ,out_features=self.out_features     ,out_indices=self.out_indices     ,stage_names=self.stage_names     ,use_pretrained_backbone=self.use_pretrained_backbone     ,backbone=self.backbone     ,)



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		TimmBackbone(config=lowercase)
             model.to(lowercase)
             model.eval()
             with torch.no_grad():
                   UpperCAmelCase_  :	Union[str, Any]				      =		model(lowercase)
             self.parent.assertEqual(
                 result.feature_map[-1].shape     ,(self.batch_size, model.channels[-1], 14, 14)     ,)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.prepare_config_and_inputs()
             UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[int]				      =		config_and_inputs
             UpperCAmelCase_  :	Union[str, Any]				      =		{"pixel_values": pixel_values}
             return config, inputs_dict


@require_torch
@require_timm
class   snake_case_ (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				(TimmBackbone,) if is_torch_available() else ()
       _lowerCamelCase			    =				{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		TimmBackboneModelTester(self)
             UpperCAmelCase_  :	Tuple				      =		ConfigTester(self     ,config_class=lowercase     ,has_text_modality=lowercase)



       def  A_   (     self):

             """simple docstring"""





             self.config_tester.create_and_test_config_to_json_string()
             self.config_tester.create_and_test_config_to_json_file()
             self.config_tester.create_and_test_config_from_and_save_pretrained()
             self.config_tester.create_and_test_config_with_num_labels()
             self.config_tester.check_config_can_be_init_without_params()
             self.config_tester.check_config_arguments_init()



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		"resnet18"
             UpperCAmelCase_  :	Tuple				      =		"microsoft/resnet-18"

             UpperCAmelCase_  :	int				      =		AutoBackbone.from_pretrained(lowercase     ,use_timm_backbone=lowercase)
             UpperCAmelCase_  :	Dict				      =		AutoBackbone.from_pretrained(lowercase)

             self.assertEqual(len(timm_model.out_features)     ,len(transformers_model.out_features))
             self.assertEqual(len(timm_model.stage_names)     ,len(transformers_model.stage_names))
             self.assertEqual(timm_model.channels     ,transformers_model.channels)
             # Out indices are set to the last layer by default. For timm models, we don't know
             # the number of layers in advance, so we set it to (-1,), whereas for transformers
             # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
             self.assertEqual(timm_model.out_indices     ,(-1,))
             self.assertEqual(transformers_model.out_indices     ,[len(timm_model.stage_names) - 1])

             UpperCAmelCase_  :	Dict				      =		AutoBackbone.from_pretrained(lowercase     ,use_timm_backbone=lowercase     ,out_indices=[1, 2, 3])
             UpperCAmelCase_  :	List[str]				      =		AutoBackbone.from_pretrained(lowercase     ,out_indices=[1, 2, 3])

             self.assertEqual(timm_model.out_indices     ,transformers_model.out_indices)
             self.assertEqual(len(timm_model.out_features)     ,len(transformers_model.out_features))
             self.assertEqual(timm_model.channels     ,transformers_model.channels)



       @unittest.skip("TimmBackbone doesn't support feed forward chunking")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone initialization is managed on the timm side")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("model weights aren't tied in TimmBackbone.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("model weights aren't tied in TimmBackbone.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't support output_attentions.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Safetensors is not supported by timm.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
       def  A_   (     self):

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	int				      =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	Union[str, Any]				      =		model_class(lowercase)
                   UpperCAmelCase_  :	Optional[Any]				      =		inspect.signature(model.forward)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   UpperCAmelCase_  :	str				      =		[*signature.parameters.keys()]

                   UpperCAmelCase_  :	Any				      =		["pixel_values"]
                   self.assertListEqual(arg_names[:1]     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	Tuple				      =		self.model_tester.prepare_config_and_inputs_for_common()
             UpperCAmelCase_  :	Dict				      =		True
             UpperCAmelCase_  :	str				      =		self.has_attentions

             # no need to test all models as different heads yield the same functionality
             UpperCAmelCase_  :	str				      =		self.all_model_classes[0]
             UpperCAmelCase_  :	List[Any]				      =		model_class(lowercase)
             model.to(lowercase)

             UpperCAmelCase_  :	Any				      =		self._prepare_for_class(lowercase     ,lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		model(**lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		outputs[0][-1]

             # Encoder-/Decoder-only models
             UpperCAmelCase_  :	List[Any]				      =		outputs.hidden_states[0]
             hidden_states.retain_grad()

             if self.has_attentions:
                   UpperCAmelCase_  :	int				      =		outputs.attentions[0]
                   attentions.retain_grad()

             output.flatten()[0].backward(retain_graph=lowercase)

             self.assertIsNotNone(hidden_states.grad)

             if self.has_attentions:
                   self.assertIsNotNone(attentions.grad)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	Any				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Tuple				      =		model(**lowercase)

                   self.assertEqual(len(result.feature_maps)     ,len(config.out_indices))
                   self.assertEqual(len(model.channels)     ,len(config.out_indices))

                   # Check output of last stage is taken if out_features=None, out_indices=None
                   UpperCAmelCase_  :	Tuple				      =		copy.deepcopy(lowercase)
                   UpperCAmelCase_  :	List[Any]				      =		None
                   UpperCAmelCase_  :	Optional[Any]				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Tuple				      =		model(**lowercase)

                   self.assertEqual(len(result.feature_maps)     ,1)
                   self.assertEqual(len(model.channels)     ,1)

                   # Check backbone can be initialized with fresh weights
                   UpperCAmelCase_  :	str				      =		copy.deepcopy(lowercase)
                   UpperCAmelCase_  :	Optional[Any]				      =		False
                   UpperCAmelCase_  :	List[str]				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Union[str, Any]				      =		model(**lowercase)
