
"""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_       :						Union[str, Any]     					=	logging.get_logger(__name__)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							int=False				)  ->    Optional[int]:

  '''simple docstring'''





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

  return new_state_dict





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							Any				)  ->    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)
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''				)
      _SCREAMING_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
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = kv_weight[
          : config.hidden_sizes[i], :
      ]
      _SCREAMING_SNAKE_CASE   :				List[Any]							    = kv_bias[: config.hidden_sizes[i]]
      _SCREAMING_SNAKE_CASE   :				List[str]							    = kv_weight[
          config.hidden_sizes[i] :, :
      ]
      _SCREAMING_SNAKE_CASE   :				Dict							    = kv_bias[
          config.hidden_sizes[i] :
      ]





def    _lowerCAmelCase    (     )  ->    str:

  '''simple docstring'''





  _SCREAMING_SNAKE_CASE   :				Any							    = "http://images.cocodataset.org/val2017/000000039769.jpg"
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = Image.open(requests.get(UpperCamelCase__,						stream=UpperCamelCase__				).raw				)

  return image





@torch.no_grad()
def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							Dict,						lowerCamelCase__							:							Union[str, Any]				)  ->    int:

  '''simple docstring'''





  _SCREAMING_SNAKE_CASE   :				str							    = SegformerConfig()
  _SCREAMING_SNAKE_CASE   :				Dict							    = False

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

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

  # load image processor (only resize + normalize)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = SegformerImageProcessor(
      image_scale=(5_1_2, 5_1_2),						keep_ratio=UpperCamelCase__,						align=UpperCamelCase__,						do_random_crop=UpperCamelCase__				)

  # prepare image
  _SCREAMING_SNAKE_CASE   :				Any							    = prepare_img()
  _SCREAMING_SNAKE_CASE   :				str							    = image_processor(images=UpperCamelCase__,						return_tensors="pt"				).pixel_values

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

  # load original state dict
  if encoder_only:
    _SCREAMING_SNAKE_CASE   :				str							    = torch.load(UpperCamelCase__,						map_location=torch.device("cpu"				)				)
  else:
    _SCREAMING_SNAKE_CASE   :				int							    = torch.load(UpperCamelCase__,						map_location=torch.device("cpu"				)				)["state_dict"]

  # rename keys
  _SCREAMING_SNAKE_CASE   :				int							    = rename_keys(UpperCamelCase__,						encoder_only=UpperCamelCase__				)
  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(UpperCamelCase__,						UpperCamelCase__				)

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

  # forward pass
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model(UpperCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = outputs.logits

  # set expected_slice based on model name
  # ADE20k checkpoints
  if model_name == "segformer.b0.512x512.ade.160k":
    _SCREAMING_SNAKE_CASE   :				str							    = torch.tensor(
        [
            [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
            [[-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":
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = torch.tensor(
        [
            [[-7.5820, -8.7231, -8.3215], [-8.0600, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5208, -9.4103, -9.6239]],
            [[-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":
    _SCREAMING_SNAKE_CASE   :				Union[str, 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":
    _SCREAMING_SNAKE_CASE   :				Tuple							    = torch.tensor(
        [
            [[-9.0878, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3144, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2294, -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":
    _SCREAMING_SNAKE_CASE   :				List[str]							    = 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":
    _SCREAMING_SNAKE_CASE   :				Dict							    = torch.tensor(
        [
            [[-9.5524, -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], [-9.5842, -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":
    _SCREAMING_SNAKE_CASE   :				Any							    = 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]],
            [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
        ]				)
  elif model_name == "segformer.b0.512x1024.city.160k":
    _SCREAMING_SNAKE_CASE   :				List[str]							    = torch.tensor(
        [
            [[-7.8217, -9.8767, -1_0.1_7_1_7], [-9.4438, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7939, -1_2.3_4_9_5, -1_2.1_0_7_9]],
            [[-7.1514, -9.5336, -1_0.0_8_6_0], [-9.7776, -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]],
            [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
        ]				)
  elif model_name == "segformer.b0.640x1280.city.160k":
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 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":
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = torch.tensor(
        [
            [[-9.4959, -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]],
            [[-9.8905, -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]],
            [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
        ]				)
  elif model_name == "segformer.b1.1024x1024.city.160k":
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = 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]],
            [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
        ]				)
  elif model_name == "segformer.b2.1024x1024.city.160k":
    _SCREAMING_SNAKE_CASE   :				Any							    = 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]],
            [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
        ]				)
  elif model_name == "segformer.b3.1024x1024.city.160k":
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 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]],
            [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
        ]				)
  elif model_name == "segformer.b4.1024x1024.city.160k":
    _SCREAMING_SNAKE_CASE   :				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]],
            [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
        ]				)
  elif model_name == "segformer.b5.1024x1024.city.160k":
    _SCREAMING_SNAKE_CASE   :				Union[str, 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]],
            [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
        ]				)
  else:
    _SCREAMING_SNAKE_CASE   :				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],						UpperCamelCase__,						atol=1E-2				)

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


if __name__ == "__main__":
      lowercase_       :						str     					=	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_       :						Optional[int]     					=	parser.parse_args()
      convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""
from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)
@flax.struct.dataclass
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			42
@flax_register_to_config
class 			UpperCamelCase      (							nn.Module      ,       __SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE  ):
     A__      =			32
     A__      =			4
     A__      =			4
     A__      =			(
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "DownBlock2D",
     )
     A__      =			("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
     A__      =			False
     A__      =			(320, 640, 1280, 1280)
     A__      =			2
     A__      =			8
     A__      =			None
     A__      =			1280
     A__      =			0.0
     A__      =			False
     A__      =			jnp.floataa
     A__      =			True
     A__      =			0
     A__      =			False
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = (1, self.in_channels, self.sample_size, self.sample_size)
       _SCREAMING_SNAKE_CASE   :				int							    = jnp.zeros(snake_case__  ,     dtype=jnp.floataa				)
       _SCREAMING_SNAKE_CASE   :				str							    = jnp.ones((1,)  ,     dtype=jnp.intaa				)
       _SCREAMING_SNAKE_CASE   :				Any							    = jnp.zeros((1, 1, self.cross_attention_dim)  ,     dtype=jnp.floataa				)

       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Tuple							    = jax.random.split(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = {"params": params_rng, "dropout": dropout_rng}

       return self.init(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)["params"]
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.block_out_channels
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = block_out_channels[0] * 4

       if self.num_attention_heads is not None:
         raise ValueError(
             "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."				)

       # If `num_attention_heads` is not defined (which is the case for most models)
       # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
       # The reason for this behavior is to correct for incorrectly named variables that were introduced
       # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
       # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
       # which is why we correct for the naming here.
       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.num_attention_heads or self.attention_head_dim

       # input
       _SCREAMING_SNAKE_CASE   :				int							    = nn.Conv(
           block_out_channels[0]  ,     kernel_size=(3, 3)  ,     strides=(1, 1)  ,     padding=((1, 1), (1, 1))  ,     dtype=self.dtype  ,     )

       # time
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = FlaxTimesteps(
           block_out_channels[0]  ,     flip_sin_to_cos=self.flip_sin_to_cos  ,     freq_shift=self.config.freq_shift				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = FlaxTimestepEmbedding(snake_case__  ,     dtype=self.dtype				)

       _SCREAMING_SNAKE_CASE   :				str							    = self.only_cross_attention
       if isinstance(snake_case__  ,     snake_case__				):
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = (only_cross_attention,) * len(self.down_block_types				)

       if isinstance(snake_case__  ,     snake_case__				):
         _SCREAMING_SNAKE_CASE   :				int							    = (num_attention_heads,) * len(self.down_block_types				)

       # down
       _SCREAMING_SNAKE_CASE   :				Tuple							    = []
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = block_out_channels[0]
       for i, down_block_type in enumerate(self.down_block_types				):
         _SCREAMING_SNAKE_CASE   :				str							    = output_channel
         _SCREAMING_SNAKE_CASE   :				List[str]							    = block_out_channels[i]
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = i == len(snake_case__				) - 1

         if down_block_type == "CrossAttnDownBlock2D":
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = FlaxCrossAttnDownBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     dropout=self.dropout  ,     num_layers=self.layers_per_block  ,     num_attention_heads=num_attention_heads[i]  ,     add_downsample=not is_final_block  ,     use_linear_projection=self.use_linear_projection  ,     only_cross_attention=only_cross_attention[i]  ,     use_memory_efficient_attention=self.use_memory_efficient_attention  ,     dtype=self.dtype  ,     )
         else:
           _SCREAMING_SNAKE_CASE   :				str							    = FlaxDownBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     dropout=self.dropout  ,     num_layers=self.layers_per_block  ,     add_downsample=not is_final_block  ,     dtype=self.dtype  ,     )

         down_blocks.append(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = down_blocks

       # mid
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = FlaxUNetMidBlockaDCrossAttn(
           in_channels=block_out_channels[-1]  ,     dropout=self.dropout  ,     num_attention_heads=num_attention_heads[-1]  ,     use_linear_projection=self.use_linear_projection  ,     use_memory_efficient_attention=self.use_memory_efficient_attention  ,     dtype=self.dtype  ,     )

       # up
       _SCREAMING_SNAKE_CASE   :				int							    = []
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = list(reversed(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Any							    = list(reversed(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Any							    = list(reversed(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = reversed_block_out_channels[0]
       for i, up_block_type in enumerate(self.up_block_types				):
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = output_channel
         _SCREAMING_SNAKE_CASE   :				List[str]							    = reversed_block_out_channels[i]
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = reversed_block_out_channels[min(i + 1  ,     len(snake_case__				) - 1				)]

         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = i == len(snake_case__				) - 1

         if up_block_type == "CrossAttnUpBlock2D":
           _SCREAMING_SNAKE_CASE   :				int							    = FlaxCrossAttnUpBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     prev_output_channel=snake_case__  ,     num_layers=self.layers_per_block + 1  ,     num_attention_heads=reversed_num_attention_heads[i]  ,     add_upsample=not is_final_block  ,     dropout=self.dropout  ,     use_linear_projection=self.use_linear_projection  ,     only_cross_attention=only_cross_attention[i]  ,     use_memory_efficient_attention=self.use_memory_efficient_attention  ,     dtype=self.dtype  ,     )
         else:
           _SCREAMING_SNAKE_CASE   :				str							    = FlaxUpBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     prev_output_channel=snake_case__  ,     num_layers=self.layers_per_block + 1  ,     add_upsample=not is_final_block  ,     dropout=self.dropout  ,     dtype=self.dtype  ,     )

         up_blocks.append(snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Any							    = output_channel
       _SCREAMING_SNAKE_CASE   :				int							    = up_blocks

       # out
       _SCREAMING_SNAKE_CASE   :				str							    = nn.GroupNorm(num_groups=32  ,     epsilon=1E-5				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = nn.Conv(
           self.out_channels  ,     kernel_size=(3, 3)  ,     strides=(1, 1)  ,     padding=((1, 1), (1, 1))  ,     dtype=self.dtype  ,     )




     def __call__(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__ = True  ,     snake_case__ = False  ,     ):




       """simple docstring"""


       if not isinstance(snake_case__  ,     jnp.ndarray				):
         _SCREAMING_SNAKE_CASE   :				Any							    = jnp.array([timesteps]  ,     dtype=jnp.intaa				)
       elif isinstance(snake_case__  ,     jnp.ndarray				) and len(timesteps.shape				) == 0:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = timesteps.astype(dtype=jnp.floataa				)
         _SCREAMING_SNAKE_CASE   :				Tuple							    = jnp.expand_dims(snake_case__  ,     0				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.time_proj(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.time_embedding(snake_case__				)

       # 2. pre-process
       _SCREAMING_SNAKE_CASE   :				Dict							    = jnp.transpose(snake_case__  ,     (0, 2, 3, 1)				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.conv_in(snake_case__				)

       # 3. down
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = (sample,)
       for down_block in self.down_blocks:
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = down_block(snake_case__  ,     snake_case__  ,     snake_case__  ,     deterministic=not train				)
         else:
           _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = down_block(snake_case__  ,     snake_case__  ,     deterministic=not train				)
         down_block_res_samples += res_samples

       if down_block_additional_residuals is not None:
         _SCREAMING_SNAKE_CASE   :				str							    = ()

         for down_block_res_sample, down_block_additional_residual in zip(
             snake_case__  ,     snake_case__				):
           down_block_res_sample += down_block_additional_residual
           new_down_block_res_samples += (down_block_res_sample,)

         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_down_block_res_samples

       # 4. mid
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.mid_block(snake_case__  ,     snake_case__  ,     snake_case__  ,     deterministic=not train				)

       if mid_block_additional_residual is not None:
         sample += mid_block_additional_residual

       # 5. up
       for up_block in self.up_blocks:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = down_block_res_samples[-(self.layers_per_block + 1) :]
         _SCREAMING_SNAKE_CASE   :				int							    = down_block_res_samples[: -(self.layers_per_block + 1)]
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = up_block(
               snake_case__  ,     temb=snake_case__  ,     encoder_hidden_states=snake_case__  ,     res_hidden_states_tuple=snake_case__  ,     deterministic=not train  ,     )
         else:
           _SCREAMING_SNAKE_CASE   :				List[str]							    = up_block(snake_case__  ,     temb=snake_case__  ,     res_hidden_states_tuple=snake_case__  ,     deterministic=not train				)

        # 6. post-process
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.conv_norm_out(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = nn.silu(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.conv_out(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = jnp.transpose(snake_case__  ,     (0, 3, 1, 2)				)

       if not return_dict:
         return (sample,)

       return FlaxUNetaDConditionOutput(sample=snake_case__				)



