


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      SwinConfig(
		    embed_dim=1_92						,				depths=(2, 2, 18, 2)						,				num_heads=(6, 12, 24, 48)						,				window_size=12						,				out_features=['''stage2''', '''stage3''', '''stage4''']						,				)

		lowercase__            =      DetaConfig(
		    backbone_config=SCREAMING_SNAKE_CASE						,				num_queries=9_00						,				encoder_ffn_dim=20_48						,				decoder_ffn_dim=20_48						,				num_feature_levels=5						,				assign_first_stage=SCREAMING_SNAKE_CASE						,				with_box_refine=SCREAMING_SNAKE_CASE						,				two_stage=SCREAMING_SNAKE_CASE						,				)

		# set labels
		lowercase__            =      '''huggingface/label-files'''
		if "o365" in model_name:
				lowercase__            =      3_66
				lowercase__            =      '''object365-id2label.json'''
		else:
				lowercase__            =      91
				lowercase__            =      '''coco-detection-id2label.json'''

		lowercase__            =      num_labels
		lowercase__            =      json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				repo_type='''dataset'''     )     )						,				'''r'''     )     )
		lowercase__            =      {int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
		lowercase__            =      idalabel
		lowercase__            =      {v: k for k, v in idalabel.items()}

		return config
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      []

		# stem
		# fmt: off
		rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')     )
		rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')     )
		rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')     )
		rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.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.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')     )

				if i < 3:
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')     )
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')     )

		rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')     )
		rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')     )
		rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')     )
		rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')     )
		rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')     )
		rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')     )

		# transformer encoder
		for i in range(config.encoder_layers     ):
				rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight')     )
				rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias')     )

		# transformer decoder
		for i in range(config.decoder_layers     ):
				rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight')     )
				rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias')     )

		# fmt: on

		return rename_keys
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      dct.pop(SCREAMING_SNAKE_CASE     )
		lowercase__            =      val
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      [int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
		for i in range(len(backbone_config.depths     )     ):
				lowercase__            =      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)
						lowercase__            =      state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'     )
						lowercase__            =      state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'     )
						# next, add query, keys and values (in that order) to the state dict
						lowercase__            =      in_proj_weight[:dim, :]
						lowercase__            =      in_proj_bias[: dim]
						lowercase__            =      in_proj_weight[
						    dim : dim * 2, :
						]
						lowercase__            =      in_proj_bias[
						    dim : dim * 2
						]
						lowercase__            =      in_proj_weight[
						    -dim :, :
						]
						lowercase__            =      in_proj_bias[-dim :]
						# fmt: on
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      config.d_model
		for i in range(config.decoder_layers     ):
				# read in weights + bias of input projection layer of self-attention
				lowercase__            =      state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'     )
				lowercase__            =      state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias'     )
				# next, add query, keys and values (in that order) to the state dict
				lowercase__            =      in_proj_weight[:hidden_size, :]
				lowercase__            =      in_proj_bias[:hidden_size]
				lowercase__            =      in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				lowercase__            =      in_proj_bias[hidden_size : hidden_size * 2]
				lowercase__            =      in_proj_weight[-hidden_size:, :]
				lowercase__            =      in_proj_bias[-hidden_size:]
def 			_a (      ):


		"""simple docstring"""




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

		return im
@torch.no_grad()
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      get_deta_config(SCREAMING_SNAKE_CASE     )

		# load original state dict
		if model_name == "deta-swin-large":
				lowercase__            =      hf_hub_download(repo_id='''nielsr/deta-checkpoints'''						,				filename='''adet_swin_ft.pth'''     )
		elif model_name == "deta-swin-large-o365":
				lowercase__            =      hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365'''						,				filename='''deta_swin_pt_o365.pth'''     )
		else:
				raise ValueError(f'Model name {model_name} not supported'     )

		lowercase__            =      torch.load(SCREAMING_SNAKE_CASE						,				map_location='''cpu'''     )['''model''']

		# original state dict
		for name, param in state_dict.items():
				print(SCREAMING_SNAKE_CASE						,				param.shape     )

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

		# fix some prefixes
		for key in state_dict.copy().keys():
				if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
						lowercase__            =      state_dict.pop(SCREAMING_SNAKE_CASE     )
						lowercase__            =      val
				if "input_proj" in key:
						lowercase__            =      state_dict.pop(SCREAMING_SNAKE_CASE     )
						lowercase__            =      val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						lowercase__            =      state_dict.pop(SCREAMING_SNAKE_CASE     )
						lowercase__            =      val

    # finally, create HuggingFace model and load state dict
		lowercase__            =      DetaForObjectDetection(SCREAMING_SNAKE_CASE     )
		model.load_state_dict(SCREAMING_SNAKE_CASE     )
		model.eval()

		lowercase__            =      '''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(SCREAMING_SNAKE_CASE     )

		# load image processor
		lowercase__            =      DetaImageProcessor(format='''coco_detection'''     )

		# verify our conversion on image
		lowercase__            =      prepare_img()
		lowercase__            =      processor(images=SCREAMING_SNAKE_CASE						,				return_tensors='''pt'''     )
		lowercase__            =      encoding['''pixel_values''']
		lowercase__            =      model(pixel_values.to(SCREAMING_SNAKE_CASE     )     )

		# verify logits
		print('''Logits:'''						,				outputs.logits[0, :3, :3]     )
		print('''Boxes:'''						,				outputs.pred_boxes[0, :3, :3]     )
		if model_name == "deta-swin-large":
				lowercase__            =      torch.tensor(
				    [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]]     )
				lowercase__            =      torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]]     )
		elif model_name == "deta-swin-large-o365":
				lowercase__            =      torch.tensor(
				    [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]]     )
				lowercase__            =      torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]]     )
		assert torch.allclose(outputs.logits[0, :3, :3]						,				expected_logits.to(SCREAMING_SNAKE_CASE     )						,				atol=1E-4     )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,				expected_boxes.to(SCREAMING_SNAKE_CASE     )						,				atol=1E-4     )
		print('''Everything ok!'''     )

		if pytorch_dump_folder_path:
				# Save model and processor
				logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'     )
				Path(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
				model.save_pretrained(SCREAMING_SNAKE_CASE     )
				processor.save_pretrained(SCREAMING_SNAKE_CASE     )

		# Push to hub
		if push_to_hub:
				print('''Pushing model and processor to hub...'''     )
				model.push_to_hub(f'jozhang97/{model_name}'     )
				processor.push_to_hub(f'jozhang97/{model_name}'     )


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()

			parser.add_argument(
			    '--model_name',
			    type=str,
			    default='deta-swin-large',
			    choices=['deta-swin-large', 'deta-swin-large-o365'],
			    help='Name of the model you\'d like to convert.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=str,
			    help='Path to the folder to output PyTorch model.',
			)
			parser.add_argument(
			    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
			)
			lowerCAmelCase       =							parser.parse_args()
			convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 						__snake_case(				_lowerCAmelCase		)		-> List[Any]:
      snake_case__  :    Dict    						=      SwinConfig()
      snake_case__  :    Optional[Any]    						=      swin_name.split("""_"""		)

      snake_case__  :    Any    						=      name_split[1]
      snake_case__  :    List[Any]    						=      int(name_split[4]		)
      snake_case__  :    int    						=      int(name_split[3][-1]		)

      if model_size == "tiny":
            snake_case__  :    List[Any]    						=      96
            snake_case__  :    int    						=      (2, 2, 6, 2)
            snake_case__  :    int    						=      (3, 6, 12, 24)
      elif model_size == "small":
            snake_case__  :    Union[str, Any]    						=      96
            snake_case__  :    Optional[Any]    						=      (2, 2, 18, 2)
            snake_case__  :    str    						=      (3, 6, 12, 24)
      elif model_size == "base":
            snake_case__  :    Dict    						=      128
            snake_case__  :    str    						=      (2, 2, 18, 2)
            snake_case__  :    Dict    						=      (4, 8, 16, 32)
      else:
            snake_case__  :    List[str]    						=      192
            snake_case__  :    str    						=      (2, 2, 18, 2)
            snake_case__  :    List[Any]    						=      (6, 12, 24, 48)

      if "in22k" in swin_name:
            snake_case__  :    str    						=      21_841
      else:
            snake_case__  :    List[str]    						=      1_000
            snake_case__  :    int    						=      """huggingface/label-files"""
            snake_case__  :    Any    						=      """imagenet-1k-id2label.json"""
            snake_case__  :    List[Any]    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
            snake_case__  :    Dict    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
            snake_case__  :    Optional[int]    						=      idalabel
            snake_case__  :    List[Any]    						=      {v: k for k, v in idalabel.items()}

      snake_case__  :    List[Any]    						=      img_size
      snake_case__  :    Dict    						=      num_classes
      snake_case__  :    Dict    						=      embed_dim
      snake_case__  :    Optional[int]    						=      depths
      snake_case__  :    int    						=      num_heads
      snake_case__  :    Optional[int]    						=      window_size

      return config





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      if "patch_embed.proj" in name:
            snake_case__  :    List[str]    						=      name.replace("""patch_embed.proj"""     ,   """embeddings.patch_embeddings.projection"""		)
      if "patch_embed.norm" in name:
            snake_case__  :    int    						=      name.replace("""patch_embed.norm"""     ,   """embeddings.norm"""		)
      if "layers" in name:
            snake_case__  :    str    						=      """encoder.""" + name
      if "attn.proj" in name:
            snake_case__  :    List[str]    						=      name.replace("""attn.proj"""     ,   """attention.output.dense"""		)
      if "attn" in name:
            snake_case__  :    Tuple    						=      name.replace("""attn"""     ,   """attention.self"""		)
      if "norm1" in name:
            snake_case__  :    List[str]    						=      name.replace("""norm1"""     ,   """layernorm_before"""		)
      if "norm2" in name:
            snake_case__  :    Optional[Any]    						=      name.replace("""norm2"""     ,   """layernorm_after"""		)
      if "mlp.fc1" in name:
            snake_case__  :    Union[str, Any]    						=      name.replace("""mlp.fc1"""     ,   """intermediate.dense"""		)
      if "mlp.fc2" in name:
            snake_case__  :    Dict    						=      name.replace("""mlp.fc2"""     ,   """output.dense"""		)

      if name == "norm.weight":
            snake_case__  :    Tuple    						=      """layernorm.weight"""
      if name == "norm.bias":
            snake_case__  :    Union[str, Any]    						=      """layernorm.bias"""

      if "head" in name:
            snake_case__  :    Optional[int]    						=      name.replace("""head"""     ,   """classifier"""		)
      else:
            snake_case__  :    List[str]    						=      """swin.""" + name

      return name





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      for key in orig_state_dict.copy().keys():
            snake_case__  :    Optional[int]    						=      orig_state_dict.pop(_lowerCAmelCase		)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  snake_case__  :    Dict    						=      key.split("""."""		)
                  snake_case__  :    Optional[int]    						=      int(key_split[1]		)
                  snake_case__  :    Union[str, Any]    						=      int(key_split[3]		)
                  snake_case__  :    List[Any]    						=      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        snake_case__  :    Optional[Any]    						=      val[:dim, :]
                        snake_case__  :    Tuple    						=      val[
                            dim : dim * 2, :
                        ]
                        snake_case__  :    Dict    						=      val[-dim:, :]
                  else:
                        snake_case__  :    Tuple    						=      val[
                            :dim
                        ]
                        snake_case__  :    int    						=      val[
                            dim : dim * 2
                        ]
                        snake_case__  :    int    						=      val[
                            -dim:
                        ]
            else:
                  snake_case__  :    Union[str, Any]    						=      val

      return orig_state_dict





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    Optional[int]    						=      timm.create_model(_lowerCAmelCase     ,   pretrained=_lowerCAmelCase		)
      timm_model.eval()

      snake_case__  :    Optional[int]    						=      get_swin_config(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      SwinForImageClassification(_lowerCAmelCase		)
      model.eval()

      snake_case__  :    str    						=      convert_state_dict(timm_model.state_dict()     ,   _lowerCAmelCase		)
      model.load_state_dict(_lowerCAmelCase		)

      snake_case__  :    int    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""

      snake_case__  :    Dict    						=      AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""     ,   """-"""		)		)		)
      snake_case__  :    Dict    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)
      snake_case__  :    Optional[int]    						=      image_processor(images=_lowerCAmelCase     ,   return_tensors="""pt"""		)

      snake_case__  :    Optional[Any]    						=      timm_model(inputs["""pixel_values"""]		)
      snake_case__  :    str    						=      model(**_lowerCAmelCase		).logits

      assert torch.allclose(_lowerCAmelCase     ,   _lowerCAmelCase     ,   atol=1e-3		)

      print(f"Saving model {swin_name} to {pytorch_dump_folder_path}"		)
      model.save_pretrained(_lowerCAmelCase		)

      print(f"Saving image processor to {pytorch_dump_folder_path}"		)
      image_processor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--swin_name",
           default="swin_tiny_patch4_window7_224",
           type=str,
           help="Name of the Swin timm model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )

       __a          =		parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
