


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


def 					lowercase      (    A_  ,       A_=False	)->				List[str]:
  '''simple docstring'''







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

  return new_state_dict


def 					lowercase      (    A_  ,       A_	)->				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)
      a						:		List[str]											=      state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''	)
      a						:		Optional[Any]											=      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
      a						:		int											=      kv_weight[
          : config.hidden_sizes[i], :
      ]
      a						:		Tuple											=      kv_bias[: config.hidden_sizes[i]]
      a						:		Optional[int]											=      kv_weight[
          config.hidden_sizes[i] :, :
      ]
      a						:		Tuple											=      kv_bias[
          config.hidden_sizes[i] :
      ]


def 					lowercase      (    )->				Optional[Any]:
  '''simple docstring'''







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

  return image


@torch.no_grad()
def 					lowercase      (    A_  ,       A_  ,       A_	)->				str:
  '''simple docstring'''







  a						:		Dict											=      SegformerConfig()
  a						:		List[Any]											=      False

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

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

  # load image processor (only resize + normalize)
  a						:		Optional[Any]											=      SegformerImageProcessor(
      image_scale=(512, 512)  ,       keep_ratio=__snake_case  ,       align=__snake_case  ,       do_random_crop=__snake_case	)

  # prepare image
  a						:		Union[str, Any]											=      prepare_img()
  a						:		Optional[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:
    a						:		List[Any]											=      torch.load(__snake_case  ,       map_location=torch.device("cpu"	)	)
  else:
    a						:		Tuple											=      torch.load(__snake_case  ,       map_location=torch.device("cpu"	)	)["state_dict"]

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

  # forward pass
  a						:		Optional[Any]											=      model(__snake_case	)
  a						:		Optional[int]											=      outputs.logits

  # set expected_slice based on model name
  # ADE20k checkpoints
  if model_name == "segformer.b0.512x512.ade.160k":
    a						:		Optional[Any]											=      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":
    a						:		Optional[Any]											=      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":
    a						:		Optional[Any]											=      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":
    a						:		List[str]											=      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":
    a						:		Union[str, Any]											=      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":
    a						:		int											=      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":
    a						:		List[str]											=      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":
    a						:		Tuple											=      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":
    a						:		int											=      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":
    a						:		Any											=      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":
    a						:		Optional[Any]											=      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":
    a						:		Optional[int]											=      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":
    a						:		Optional[int]											=      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":
    a						:		List[str]											=      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":
    a						:		Any											=      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:
    a						:		Union[str, 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__":
 __lowercase    =				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    =				parser.parse_args()
 convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""







import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowercase    =				"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
__lowercase    =				direct_transformers_import(PATH_TO_TRANSFORMERS)

__lowercase    =				transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowercase    =				{
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        """CLIPSegConfig""": True,
        """DeformableDetrConfig""": True,
        """DetaConfig""": True,
        """DinatConfig""": True,
        """DonutSwinConfig""": True,
        """EfficientFormerConfig""": True,
        """FSMTConfig""": True,
        """JukeboxConfig""": True,
        """LayoutLMv2Config""": True,
        """MaskFormerSwinConfig""": True,
        """MT5Config""": True,
        """NatConfig""": True,
        """OneFormerConfig""": True,
        """PerceiverConfig""": True,
        """RagConfig""": True,
        """SpeechT5Config""": True,
        """SwinConfig""": True,
        """Swin2SRConfig""": True,
        """Swinv2Config""": True,
        """SwitchTransformersConfig""": True,
        """TableTransformerConfig""": True,
        """TapasConfig""": True,
        """TransfoXLConfig""": True,
        """UniSpeechConfig""": True,
        """UniSpeechSatConfig""": True,
        """WavLMConfig""": True,
        """WhisperConfig""": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        """JukeboxPriorConfig""": True,
        # TODO: @Younes (for `is_decoder`)
        """Pix2StructTextConfig""": True,
    }
)


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		List[str]											=      False
  for attribute in attributes:
    for modeling_source in source_strings:
      # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
      if (
          F'''config.{attribute}''' in modeling_source
          or F'''getattr(config, "{attribute}"''' in modeling_source
          or F'''getattr(self.config, "{attribute}"''' in modeling_source
      ):
        a						:		str											=      True
      # Deal with multi-line cases
      elif (
          re.search(
              RF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'''  ,       A_  ,       )
          is not None
      ):
        a						:		List[Any]											=      True
      # `SequenceSummary` is called with `SequenceSummary(config)`
      elif attribute in [
          "summary_type",
          "summary_use_proj",
          "summary_activation",
          "summary_last_dropout",
          "summary_proj_to_labels",
          "summary_first_dropout",
      ]:
        if "SequenceSummary" in modeling_source:
          a						:		str											=      True
      if attribute_used:
        break
    if attribute_used:
      break

    # common and important attributes, even if they do not always appear in the modeling files
  a						:		Tuple											=      [
      "bos_index",
      "eos_index",
      "pad_index",
      "unk_index",
      "mask_index",
      "image_size",
      "use_cache",
      "out_features",
      "out_indices",
  ]
  a						:		str											=      ["encoder_no_repeat_ngram_size"]

  # Special cases to be allowed
  a						:		int											=      True
  if not attribute_used:
    a						:		Dict											=      False
    for attribute in attributes:
      # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
      if attribute in ["is_encoder_decoder"] and default_value is True:
        a						:		Optional[int]											=      True
      elif attribute in ["tie_word_embeddings"] and default_value is False:
        a						:		List[Any]											=      True

      # Allow cases without checking the default value in the configuration class
      elif attribute in attributes_to_allow + attributes_used_in_generation:
        a						:		str											=      True
      elif attribute.endswith("_token_id"	):
        a						:		str											=      True

      # configuration class specific cases
      if not case_allowed:
        a						:		Union[str, Any]											=      SPECIAL_CASES_TO_ALLOW.get(config_class.__name__  ,       []	)
        a						:		Optional[Any]											=      allowed_cases is True or attribute in allowed_cases

  return attribute_used or case_allowed


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		Optional[int]											=      dict(inspect.signature(config_class.__init__	).parameters	)
  a						:		Any											=      [x for x in list(signature.keys()	) if x not in ["self", "kwargs"]]
  a						:		str											=      [signature[param].default for param in parameter_names]

  # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
  # as one variant is used, the test should pass
  a						:		Dict											=      {}
  if len(config_class.attribute_map	) > 0:
    a						:		int											=      {v: k for k, v in config_class.attribute_map.items()}

  # Get the path to modeling source files
  a						:		int											=      inspect.getsourcefile(A_	)
  a						:		Union[str, Any]											=      os.path.dirname(A_	)
  # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
  a						:		Optional[Any]											=      [os.path.join(A_  ,       A_	) for fn in os.listdir(A_	) if fn.startswith("modeling_"	)]

  # Get the source code strings
  a						:		Tuple											=      []
  for path in modeling_paths:
    if os.path.isfile(A_	):
      with open(A_	) as fp:
        modeling_sources.append(fp.read()	)

  a						:		Optional[Any]											=      []
  for config_param, default_value in zip(A_  ,       A_	):
    # `attributes` here is all the variant names for `config_param`
    a						:		str											=      [config_param]
    # some configuration classes have non-empty `attribute_map`, and both names could be used in the
    # corresponding modeling files. As long as one of them appears, it is fine.
    if config_param in reversed_attribute_map:
      attributes.append(reversed_attribute_map[config_param]	)

    if not check_attribute_being_used(A_  ,       A_  ,       A_  ,       A_	):
      unused_attributes.append(attributes[0]	)

  return sorted(A_	)


def 					lowercase      (    )->				str:
  '''simple docstring'''







  a						:		List[Any]											=      {}
  for _config_class in list(CONFIG_MAPPING.values()	):
    # Skip deprecated models
    if "models.deprecated" in _config_class.__module__:
      continue
    # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
    a						:		str											=      [
        cls
        for name, cls in inspect.getmembers(
            inspect.getmodule(_config_class	)  ,       lambda A_	: inspect.isclass(A_	)
            and issubclass(A_  ,       A_	)
            and inspect.getmodule(A_	) == inspect.getmodule(_config_class	)  ,       )
    ]
    for config_class in config_classes_in_module:
      a						:		Union[str, Any]											=      check_config_attributes_being_used(A_	)
      if len(A_	) > 0:
        a						:		Dict											=      unused_attributes

  if len(A_	) > 0:
    a						:		Union[str, Any]											=      "The following configuration classes contain unused attributes in the corresponding modeling files:\n"
    for name, attributes in configs_with_unused_attributes.items():
      error += F'''{name}: {attributes}\n'''

    raise ValueError(A_	)


if __name__ == "__main__":
 check_config_attributes()






