



"""simple docstring"""







import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case						:					List[str]	       =      logging.get_logger(__name__)



def 	a_						(      __a   ):
     A__      						= SwinConfig.from_pretrained(
         '''microsoft/swin-tiny-patch4-window7-224'''		,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']   )
     A__      						= MaskFormerConfig(backbone_config=lowerCAmelCase_   )

     A__      						= '''huggingface/label-files'''
     if "ade20k-full" in model_name:
          # this should be ok
          A__      						= 847
          A__      						= '''maskformer-ade20k-full-id2label.json'''
     elif "ade" in model_name:
          # this should be ok
          A__      						= 150
          A__      						= '''ade20k-id2label.json'''
     elif "coco-stuff" in model_name:
          # this should be ok
          A__      						= 171
          A__      						= '''maskformer-coco-stuff-id2label.json'''
     elif "coco" in model_name:
          # TODO
          A__      						= 133
          A__      						= '''coco-panoptic-id2label.json'''
     elif "cityscapes" in model_name:
          # this should be ok
          A__      						= 19
          A__      						= '''cityscapes-id2label.json'''
     elif "vistas" in model_name:
          # this should be ok
          A__      						= 65
          A__      						= '''mapillary-vistas-id2label.json'''

     A__      						= json.load(open(hf_hub_download(lowerCAmelCase_		,				lowerCAmelCase_		,				repo_type='''dataset'''   )		,				'''r'''   )   )
     A__      						= {int(lowerCAmelCase_   ): v for k, v in idalabel.items()}

     return config



def 	a_						(      __a   ):
     A__      						= []
     # stem
     # fmt: off
     rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')   )
     rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')   )
     rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')   )
     rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')   )
     # stages
     for i in range(len(config.backbone_config.depths   )   ):
          for j in range(config.backbone_config.depths[i]   ):
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')   )
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')   )
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')   )
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')   )
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')   )
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')   )
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')   )
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')   )
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')   )
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')   )
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')   )
               rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')   )

          if i < 3:
               rename_keys.append((f'''backbone.layers.{i}.downsample.reduction.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')   )
               rename_keys.append((f'''backbone.layers.{i}.downsample.norm.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')   )
               rename_keys.append((f'''backbone.layers.{i}.downsample.norm.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')   )
          rename_keys.append((f'''backbone.norm{i}.weight''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')   )
          rename_keys.append((f'''backbone.norm{i}.bias''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')   )

     # FPN
     rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')   )
     rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')   )
     rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')   )
     for source_index, target_index in zip(range(3		,				0		,				-1   )		,				range(0		,				3   )   ):
          rename_keys.append((f'''sem_seg_head.adapter_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')   )
          rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')   )
          rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')   )
          rename_keys.append((f'''sem_seg_head.layer_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')   )
          rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')   )
          rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')   )
     rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')   )
     rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')   )

     # Transformer decoder
     for idx in range(config.decoder_config.decoder_layers   ):
          # self-attention out projection
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')   )
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')   )
          # cross-attention out projection
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')   )
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')   )
          # MLP 1
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')   )
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')   )
          # MLP 2
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')   )
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')   )
          # layernorm 1 (self-attention layernorm)
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')   )
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')   )
          # layernorm 2 (cross-attention layernorm)
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')   )
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')   )
          # layernorm 3 (final layernorm)
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')   )
          rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')   )

     rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')   )
     rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')   )

     # heads on top
     rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')   )

     rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')   )
     rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')   )

     rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')   )
     rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')   )

     for i in range(3   ):
          rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', f'''mask_embedder.{i}.0.weight''')   )
          rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', f'''mask_embedder.{i}.0.bias''')   )
     # fmt: on

     return rename_keys



def 	a_						(      __a		,				__a		,				__a   ):
     A__      						= dct.pop(lowerCAmelCase_   )
     A__      						= val



def 	a_						(      __a		,				__a   ):
     A__      						= [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
     for i in range(len(backbone_config.depths   )   ):
          A__      						= num_features[i]
          for j in range(backbone_config.depths[i]   ):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               A__      						= state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''   )
               A__      						= state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''   )
               # next, add query, keys and values (in that order) to the state dict
               A__      						= in_proj_weight[:dim, :]
               A__      						= in_proj_bias[: dim]
               A__      						= in_proj_weight[
                   dim : dim * 2, :
               ]
               A__      						= in_proj_bias[
                   dim : dim * 2
               ]
               A__      						= in_proj_weight[
                   -dim :, :
               ]
               A__      						= in_proj_bias[-dim :]
               # fmt: on



def 	a_						(      __a		,				__a   ):
     # fmt: off
     A__      						= config.decoder_config.hidden_size
     for idx in range(config.decoder_config.decoder_layers   ):
          # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
          A__      						= state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''   )
          A__      						= state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''   )
          # next, add query, keys and values (in that order) to the state dict
          A__      						= in_proj_weight[: hidden_size, :]
          A__      						= in_proj_bias[:config.hidden_size]
          A__      						= in_proj_weight[hidden_size : hidden_size * 2, :]
          A__      						= in_proj_bias[hidden_size : hidden_size * 2]
          A__      						= in_proj_weight[-hidden_size :, :]
          A__      						= in_proj_bias[-hidden_size :]
          # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
          A__      						= state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''   )
          A__      						= state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''   )
          # next, add query, keys and values (in that order) to the state dict
          A__      						= in_proj_weight[: hidden_size, :]
          A__      						= in_proj_bias[:config.hidden_size]
          A__      						= in_proj_weight[hidden_size : hidden_size * 2, :]
          A__      						= in_proj_bias[hidden_size : hidden_size * 2]
          A__      						= in_proj_weight[-hidden_size :, :]
          A__      						= in_proj_bias[-hidden_size :]
     # fmt: on



def 	a_						(      ):
     A__      						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     A__      						= Image.open(requests.get(lowerCAmelCase_		,				stream=lowerCAmelCase_   ).raw   )
     return im



@torch.no_grad()
def 	a_						(      __a		,				__a		,				__a		,				__a = False   ):
     A__      						= get_maskformer_config(lowerCAmelCase_   )

     # load original state_dict
     with open(lowerCAmelCase_		,				'''rb'''   ) as f:
          A__      						= pickle.load(lowerCAmelCase_   )
     A__      						= data['''model''']

     # for name, param in state_dict.items():
     #     print(name, param.shape)

     # rename keys
     A__      						= create_rename_keys(lowerCAmelCase_   )
     for src, dest in rename_keys:
          rename_key(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_   )
     read_in_swin_q_k_v(lowerCAmelCase_		,				config.backbone_config   )
     read_in_decoder_q_k_v(lowerCAmelCase_		,				lowerCAmelCase_   )

     # update to torch tensors
     for key, value in state_dict.items():
          A__      						= torch.from_numpy(lowerCAmelCase_   )

     # load 🤗 model
     A__      						= MaskFormerForInstanceSegmentation(lowerCAmelCase_   )
     model.eval()

     for name, param in model.named_parameters():
          print(lowerCAmelCase_		,				param.shape   )

     A__						,  A__      						= model.load_state_dict(lowerCAmelCase_		,				strict=lowerCAmelCase_   )
     assert missing_keys == [
         "model.pixel_level_module.encoder.model.layernorm.weight",
         "model.pixel_level_module.encoder.model.layernorm.bias",
     ]
     assert len(lowerCAmelCase_   ) == 0, f'''Unexpected keys: {unexpected_keys}'''

     # verify results
     A__      						= prepare_img()
     if "vistas" in model_name:
          A__      						= 65
     elif "cityscapes" in model_name:
          A__      						= 6_5535
     else:
          A__      						= 255
     A__      						= True if '''ade''' in model_name else False
     A__      						= MaskFormerImageProcessor(ignore_index=lowerCAmelCase_		,				reduce_labels=lowerCAmelCase_   )

     A__      						= image_processor(lowerCAmelCase_		,				return_tensors='''pt'''   )

     A__      						= model(**lowerCAmelCase_   )

     print('''Logits:'''		,				outputs.class_queries_logits[0, :3, :3]   )

     if model_name == "maskformer-swin-tiny-ade":
          A__      						= torch.tensor(
              [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]   )
     assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,				lowerCAmelCase_		,				atol=1e-4   )
     print('''Looks ok!'''   )

     if pytorch_dump_folder_path is not None:
          print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''   )
          Path(lowerCAmelCase_   ).mkdir(exist_ok=lowerCAmelCase_   )
          model.save_pretrained(lowerCAmelCase_   )
          image_processor.save_pretrained(lowerCAmelCase_   )

     if push_to_hub:
          print('''Pushing model and image processor to the hub...'''   )
          model.push_to_hub(f'''nielsr/{model_name}'''   )
          image_processor.push_to_hub(f'''nielsr/{model_name}'''   )


if __name__ == "__main__":
 __snake_case						:					Optional[Any]	       =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='maskformer-swin-tiny-ade',
     type=str,
     help=('Name of the MaskFormer model you\'d like to convert',),
 )
 parser.add_argument(
     '--checkpoint_path',
     default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
     type=str,
     help='Path to the original state dict (.pth file).',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
 )

 __snake_case						:					str	       =      parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )










import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline


@pytest.mark.integration
def 							__snake_case							(    )				->						Any:
    with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT	):
        with pytest.raises(lowerCAmelCase_	):
            requests.request('''GET''' ,							'''https://huggingface.co'''	)
        with pytest.raises(requests.exceptions.ConnectTimeout	):
            requests.request('''GET''' ,							'''https://huggingface.co''' ,							timeout=1.0	)


@pytest.mark.integration
def 							__snake_case							(    )				->						int:
    with offline(OfflineSimulationMode.CONNECTION_FAILS	):
        with pytest.raises(requests.exceptions.ConnectionError	):
            requests.request('''GET''' ,							'''https://huggingface.co'''	)



def 							__snake_case							(    )				->						List[Any]:
    with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1	):
        with pytest.raises(lowerCAmelCase_	):
            http_head('''https://huggingface.co'''	)
