






'''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()
lowerCamelCase_												=					logging.get_logger(__name__)

def 					__lowercase						(			__lowercase   ,  __lowercase=False       )       ->						List[str]:

      '''simple docstring'''


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

      return new_state_dict

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						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)
                  _A			     =	state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''       )
                  _A			     =	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			     =	kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  _A			     =	kv_bias[: config.hidden_sizes[i]]
                  _A			     =	kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  _A			     =	kv_bias[
                      config.hidden_sizes[i] :
                  ]

def 					__lowercase						(			)       ->						List[str]:

      '''simple docstring'''


      _A			     =	"http://images.cocodataset.org/val2017/000000039769.jpg"
      _A			     =	Image.open(requests.get(__lowercase   ,  stream=__lowercase       ).raw       )

      return image

@torch.no_grad()
def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase       )       ->						str:

      '''simple docstring'''


      _A			     =	SegformerConfig()
      _A			     =	False

      # set attributes based on model_name
      _A			     =	"huggingface/label-files"
      if "segformer" in model_name:
            _A			     =	model_name[len("segformer."       ) : len("segformer."       ) + 2]
            if "ade" in model_name:
                  _A			     =	150
                  _A			     =	"ade20k-id2label.json"
                  _A			     =	(1, 150, 128, 128)
            elif "city" in model_name:
                  _A			     =	19
                  _A			     =	"cityscapes-id2label.json"
                  _A			     =	(1, 19, 128, 128)
            else:
                  raise ValueError(F'''Model {model_name} not supported'''       )
      elif "mit" in model_name:
            _A			     =	True
            _A			     =	model_name[4:6]
            _A			     =	1000
            _A			     =	"imagenet-1k-id2label.json"
            _A			     =	(1, 1000)
      else:
            raise ValueError(F'''Model {model_name} not supported'''       )

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

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

      # prepare image
      _A			     =	prepare_img()
      _A			     =	image_processor(images=__lowercase   ,  return_tensors="pt"       ).pixel_values

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

      # load original state dict
      if encoder_only:
            _A			     =	torch.load(__lowercase   ,  map_location=torch.device("cpu"       )       )
      else:
            _A			     =	torch.load(__lowercase   ,  map_location=torch.device("cpu"       )       )["state_dict"]

      # rename keys
      _A			     =	rename_keys(__lowercase   ,  encoder_only=__lowercase       )
      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(__lowercase   ,  __lowercase       )

      # create HuggingFace model and load state dict
      if encoder_only:
            _A			     =	False
            _A			     =	SegformerForImageClassification(__lowercase       )
      else:
            _A			     =	SegformerForSemanticSegmentation(__lowercase       )
      model.load_state_dict(__lowercase       )
      model.eval()

      # forward pass
      _A			     =	model(__lowercase       )
      _A			     =	outputs.logits

      # set expected_slice based on model name
      # ADE20k checkpoints
      if model_name == "segformer.b0.512x512.ade.160k":
            _A			     =	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":
            _A			     =	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":
            _A			     =	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":
            _A			     =	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":
            _A			     =	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":
            _A			     =	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":
            _A			     =	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":
            _A			     =	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":
            _A			     =	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			     =	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":
            _A			     =	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":
            _A			     =	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":
            _A			     =	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":
            _A			     =	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":
            _A			     =	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:
            _A			     =	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]   ,  __lowercase   ,  atol=1e-2       )

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


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 argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase				:		Union[str, Any]     =   "src/diffusers"

# Matches is_xxx_available()
lowerCamelCase				:		Dict     =   re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
lowerCamelCase				:		Union[str, Any]     =   re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


lowerCamelCase				:		Any     =   "\n{0} = None\n"

lowerCamelCase				:		List[str]     =   "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


lowerCamelCase				:		str     =   "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    _re_backend.findall(lowercase     )
      if len(lowercase     ) == 0:
            return None

      return "_and_".join(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      with open(os.path.join(lowercase	,							'__init__.py'     )	,							'r'	,							encoding='utf-8'	,							newline='\n'     ) as f:
            lowerCamelCase_		     =    f.readlines()

      # Get to the point we do the actual imports for type checking
      lowerCamelCase_		     =    0
      lowerCamelCase_		     =    {}
      # Go through the end of the file
      while line_index < len(lowercase     ):
            # If the line contains is_backend_available, we grab all objects associated with the `else` block
            lowerCamelCase_		     =    find_backend(lines[line_index]     )
            if backend is not None:
                  while not lines[line_index].startswith('else:'     ):
                        line_index += 1
                  line_index += 1
                  lowerCamelCase_		     =    []
                  # Until we unindent, add backend objects to the list
                  while line_index < len(lowercase     ) and len(lines[line_index]     ) > 1:
                        lowerCamelCase_		     =    lines[line_index]
                        lowerCamelCase_		     =    _re_single_line_import.search(lowercase     )
                        if single_line_import_search is not None:
                              objects.extend(single_line_import_search.groups()[0].split(', '     )     )
                        elif line.startswith(' ' * 8     ):
                              objects.append(line[8:-2]     )
                        line_index += 1

                  if len(lowercase     ) > 0:
                        lowerCamelCase_		     =    objects
            else:
                  line_index += 1

      return backend_specific_objects

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]	,							lowercase	:       str     ):


      '''simple docstring'''



      if name.isupper():
            return DUMMY_CONSTANT.format(lowercase     )
      elif name.islower():
            return DUMMY_FUNCTION.format(lowercase	,							lowercase     )
      else:
            return DUMMY_CLASS.format(lowercase	,							lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]=None     ):


      '''simple docstring'''



      if backend_specific_objects is None:
            lowerCamelCase_		     =    read_init()
      # For special correspondence backend to module name as used in the function requires_modulename
      lowerCamelCase_		     =    {}

      for backend, objects in backend_specific_objects.items():
            lowerCamelCase_		     =    '[' + ', '.join(f"""\"{b}\"""" for b in backend.split('_and_'     )     ) + ']'
            lowerCamelCase_		     =    '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
            dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
            dummy_file += "\n".join([create_dummy_object(lowercase	,							lowercase     ) for o in objects]     )
            lowerCamelCase_		     =    dummy_file

      return dummy_files

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]=False     ):


      '''simple docstring'''



      lowerCamelCase_		     =    create_dummy_files()
      # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
      lowerCamelCase_		     =    {'torch': 'pt'}

      # Locate actual dummy modules and read their content.
      lowerCamelCase_		     =    os.path.join(lowercase	,							'utils'     )
      lowerCamelCase_		     =    {
          backend: os.path.join(lowercase	,							f"""dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py"""     )
          for backend in dummy_files.keys()
      }

      lowerCamelCase_		     =    {}
      for backend, file_path in dummy_file_paths.items():
            if os.path.isfile(lowercase     ):
                  with open(lowercase	,							'r'	,							encoding='utf-8'	,							newline='\n'     ) as f:
                        lowerCamelCase_		     =    f.read()
            else:
                  lowerCamelCase_		     =    ''

      for backend in dummy_files.keys():
            if dummy_files[backend] != actual_dummies[backend]:
                  if overwrite:
                        print(
                            f"""Updating diffusers.utils.dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py as the main """
                            '__init__ has new objects.'     )
                        with open(dummy_file_paths[backend]	,							'w'	,							encoding='utf-8'	,							newline='\n'     ) as f:
                              f.write(dummy_files[backend]     )
                  else:
                        raise ValueError(
                            'The main __init__ has objects that are not present in '
                            f"""diffusers.utils.dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py. Run `make fix-copies` """
                            'to fix this.'     )


if __name__ == "__main__":
 lowerCamelCase				:		List[Any]     =   argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 lowerCamelCase				:		Tuple     =   parser.parse_args()

 check_dummies(args.fix_and_overwrite)



