

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()
_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    logging.get_logger(__name__)
def        UpperCAmelCase_     (		_A   ,	_A=False       ):





     '''simple docstring'''




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

     return new_state_dict
def        UpperCAmelCase_     (		_A   ,	_A       ):





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





     '''simple docstring'''




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

     return image
@torch.no_grad()
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					SegformerConfig()
     SCREAMING_SNAKE_CASE__					=					False

     # set attributes based on model_name
     SCREAMING_SNAKE_CASE__					=					'''huggingface/label-files'''
     if "segformer" in model_name:
          SCREAMING_SNAKE_CASE__					=					model_name[len('''segformer.'''       ) : len('''segformer.'''       ) + 2]
          if "ade" in model_name:
               SCREAMING_SNAKE_CASE__					=					1_50
               SCREAMING_SNAKE_CASE__					=					'''ade20k-id2label.json'''
               SCREAMING_SNAKE_CASE__					=					(1, 1_50, 1_28, 1_28)
          elif "city" in model_name:
               SCREAMING_SNAKE_CASE__					=					19
               SCREAMING_SNAKE_CASE__					=					'''cityscapes-id2label.json'''
               SCREAMING_SNAKE_CASE__					=					(1, 19, 1_28, 1_28)
          else:
               raise ValueError(F'''Model {model_name} not supported'''       )
     elif "mit" in model_name:
          SCREAMING_SNAKE_CASE__					=					True
          SCREAMING_SNAKE_CASE__					=					model_name[4:6]
          SCREAMING_SNAKE_CASE__					=					10_00
          SCREAMING_SNAKE_CASE__					=					'''imagenet-1k-id2label.json'''
          SCREAMING_SNAKE_CASE__					=					(1, 10_00)
     else:
          raise ValueError(F'''Model {model_name} not supported'''       )

     # set config attributes
     SCREAMING_SNAKE_CASE__					=					json.load(open(hf_hub_download(_A   ,	_A   ,	repo_type='''dataset'''       )   ,	'''r'''       )       )
     SCREAMING_SNAKE_CASE__					=					{int(_A       ): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__					=					idalabel
     SCREAMING_SNAKE_CASE__					=					{v: k for k, v in idalabel.items()}
     if size == "b0":
          pass
     elif size == "b1":
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					2_56
     elif size == "b2":
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					7_68
          SCREAMING_SNAKE_CASE__					=					[3, 4, 6, 3]
     elif size == "b3":
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					7_68
          SCREAMING_SNAKE_CASE__					=					[3, 4, 18, 3]
     elif size == "b4":
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					7_68
          SCREAMING_SNAKE_CASE__					=					[3, 8, 27, 3]
     elif size == "b5":
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					7_68
          SCREAMING_SNAKE_CASE__					=					[3, 6, 40, 3]
     else:
          raise ValueError(F'''Size {size} not supported'''       )

     # load image processor (only resize + normalize)
     SCREAMING_SNAKE_CASE__					=					SegformerImageProcessor(
         image_scale=(5_12, 5_12)   ,	keep_ratio=_A   ,	align=_A   ,	do_random_crop=_A       )

     # prepare image
     SCREAMING_SNAKE_CASE__					=					prepare_img()
     SCREAMING_SNAKE_CASE__					=					image_processor(images=_A   ,	return_tensors='''pt'''       ).pixel_values

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

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

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

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

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

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

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						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.'''
       )
       _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    parser.parse_args()
       convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      KandinskyImgaImgPipeline
  a     =      ["prompt", "image_embeds", "negative_image_embeds", "image"]
  a     =      [
      "prompt",
      "negative_prompt",
      "image_embeds",
      "negative_image_embeds",
      "image",
  ]
  a     =      [
      "generator",
      "height",
      "width",
      "strength",
      "guidance_scale",
      "negative_prompt",
      "num_inference_steps",
      "return_dict",
      "guidance_scale",
      "num_images_per_prompt",
      "output_type",
      "return_dict",
  ]
  a     =      False






  @property
  def    lowercase_	(							self		:	str      )	->							List[str]:
       return 32






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							int:
       return 32






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							int:
       return self.time_input_dim






  @property
  def    lowercase_	(							self		:	List[str]      )	->							int:
       return self.time_input_dim * 4






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							Any:
       return 100






  @property
  def    lowercase_	(							self		:	Any      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''      )
       return tokenizer






  @property
  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					MCLIPConfig(
           numDims=self.cross_attention_dim			,     transformerDimensions=self.text_embedder_hidden_size			,     hidden_size=self.text_embedder_hidden_size			,     intermediate_size=37			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=1005			,     )

       SCREAMING_SNAKE_CASE__					=					MultilingualCLIP(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					text_encoder.eval()

       return text_encoder






  @property
  def    lowercase_	(							self		:	str      )	->							Dict:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__					=					{
           '''in_channels''': 4,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''text_image''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''text_image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       SCREAMING_SNAKE_CASE__					=					UNetaDConditionModel(**__lowerCamelCase      )
       return model






  @property
  def    lowercase_	(							self		:	Dict      )	->							Optional[Any]:
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }






  @property
  def    lowercase_	(							self		:	Tuple      )	->							Optional[int]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					VQModel(**self.dummy_movq_kwargs      )
       return model






  def    lowercase_	(							self		:	int      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					self.dummy_text_encoder
       SCREAMING_SNAKE_CASE__					=					self.dummy_tokenizer
       SCREAMING_SNAKE_CASE__					=					self.dummy_unet
       SCREAMING_SNAKE_CASE__					=					self.dummy_movq

       SCREAMING_SNAKE_CASE__					=					{
           '''num_train_timesteps''': 1000,
           '''beta_schedule''': '''linear''',
           '''beta_start''': 0.00085,
           '''beta_end''': 0.012,
           '''clip_sample''': False,
           '''set_alpha_to_one''': False,
           '''steps_offset''': 0,
           '''prediction_type''': '''epsilon''',
           '''thresholding''': False,
       }

       SCREAMING_SNAKE_CASE__					=					DDIMScheduler(**__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					{
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Union[str, Any]=0      )	->							str:
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(__lowerCamelCase      )      ).to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(seed + 1      )      ).to(__lowerCamelCase      )
       # create init_image
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, 3, 64, 64)			,     rng=random.Random(__lowerCamelCase      )      ).to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					image.cpu().permute(0			,     2			,     3			,     1      )[0]
       SCREAMING_SNAKE_CASE__					=					Image.fromarray(np.uinta(__lowerCamelCase      )      ).convert('''RGB'''      ).resize((256, 256)      )

       if str(__lowerCamelCase      ).startswith('''mps'''      ):
            SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       else:
            SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''horse''',
           '''image''': init_image,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 10,
           '''guidance_scale''': 7.0,
           '''strength''': 0.2,
           '''output_type''': '''np''',
       }
       return inputs







  def    lowercase_	(							self		:	int      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()

       SCREAMING_SNAKE_CASE__					=					self.pipeline_class(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )

       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					pipe(**self.get_dummy_inputs(__lowerCamelCase      )      )
       SCREAMING_SNAKE_CASE__					=					output.images

       SCREAMING_SNAKE_CASE__					=					pipe(
           **self.get_dummy_inputs(__lowerCamelCase      )			,     return_dict=__lowerCamelCase			,     )[0]

       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__					=					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]      )
       assert (
           np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  def    lowercase_	(							self		:	Tuple      )	->							Dict:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  def    lowercase_	(							self		:	List[str]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinsky/kandinsky_img2img_frog.npy'''      )

       SCREAMING_SNAKE_CASE__					=					load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
       SCREAMING_SNAKE_CASE__					=					'''A red cartoon frog, 4k'''

       SCREAMING_SNAKE_CASE__					=					KandinskyPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-1-prior'''			,     torch_dtype=torch.floataa      )
       pipe_prior.to(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					KandinskyImgaImgPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-1'''			,     torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__					=					pipeline.to(__lowerCamelCase      )

       pipeline.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.Generator(device='''cpu'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					pipe_prior(
           __lowerCamelCase			,     generator=__lowerCamelCase			,     num_inference_steps=5			,     negative_prompt=''''''			,     ).to_tuple()

       SCREAMING_SNAKE_CASE__					=					pipeline(
           __lowerCamelCase			,     image=__lowerCamelCase			,     image_embeds=__lowerCamelCase			,     negative_image_embeds=__lowerCamelCase			,     generator=__lowerCamelCase			,     num_inference_steps=100			,     height=768			,     width=768			,     strength=0.2			,     output_type='''np'''			,     )

       SCREAMING_SNAKE_CASE__					=					output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(__lowerCamelCase			,     __lowerCamelCase      )


