






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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[int]       ):
 snake_case__			:				Dict       =  SwinConfig()
 snake_case__			:				List[str]       =  swin_name.split("_"       )

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

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

 if "in22k" in swin_name:
  snake_case__			:				List[str]       =  21841
 else:
  snake_case__			:				Dict       =  1000
  snake_case__			:				Optional[Any]       =  '''huggingface/label-files'''
  snake_case__			:				Any       =  '''imagenet-1k-id2label.json'''
  snake_case__			:				Optional[Any]       =  json.load(open(hf_hub_download(A_					,  A_					,  repo_type="dataset"       )					,  "r"       )       )
  snake_case__			:				Any       =  {int(A_       ): v for k, v in idalabel.items()}
  snake_case__			:				str       =  idalabel
  snake_case__			:				List[str]       =  {v: k for k, v in idalabel.items()}

 snake_case__			:				Dict       =  img_size
 snake_case__			:				str       =  num_classes
 snake_case__			:				Union[str, Any]       =  embed_dim
 snake_case__			:				Any       =  depths
 snake_case__			:				Optional[Any]       =  num_heads
 snake_case__			:				int       =  window_size

 return config







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Union[str, Any]       ):
 if "patch_embed.proj" in name:
  snake_case__			:				str       =  name.replace("patch_embed.proj"					,  "embeddings.patch_embeddings.projection"       )
 if "patch_embed.norm" in name:
  snake_case__			:				str       =  name.replace("patch_embed.norm"					,  "embeddings.norm"       )
 if "layers" in name:
  snake_case__			:				Any       =  '''encoder.''' + name
 if "attn.proj" in name:
  snake_case__			:				List[str]       =  name.replace("attn.proj"					,  "attention.output.dense"       )
 if "attn" in name:
  snake_case__			:				str       =  name.replace("attn"					,  "attention.self"       )
 if "norm1" in name:
  snake_case__			:				Tuple       =  name.replace("norm1"					,  "layernorm_before"       )
 if "norm2" in name:
  snake_case__			:				Tuple       =  name.replace("norm2"					,  "layernorm_after"       )
 if "mlp.fc1" in name:
  snake_case__			:				int       =  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__			:				Optional[Any]       =  '''layernorm.weight'''
 if name == "norm.bias":
  snake_case__			:				str       =  '''layernorm.bias'''

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

 return name







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							int       ):
 for key in orig_state_dict.copy().keys():
  snake_case__			:				Any       =  orig_state_dict.pop(A_       )

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

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

 return orig_state_dict







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							List[str]       ):
 snake_case__			:				int       =  timm.create_model(A_					,  pretrained=A_       )
 timm_model.eval()

 snake_case__			:				Tuple       =  get_swin_config(A_       )
 snake_case__			:				str       =  SwinForImageClassification(A_       )
 model.eval()

 snake_case__			:				List[Any]       =  convert_state_dict(timm_model.state_dict()					,  A_       )
 model.load_state_dict(A_       )

 snake_case__			:				Tuple       =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''

 snake_case__			:				Any       =  AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"					,  "-"       )       )       )
 snake_case__			:				str       =  Image.open(requests.get(A_					,  stream=A_       ).raw       )
 snake_case__			:				Any       =  image_processor(images=A_					,  return_tensors="pt"       )

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

 assert torch.allclose(A_					,  A_					,  atol=1E-3       )

 print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''       )
 model.save_pretrained(A_       )

 print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
 image_processor.save_pretrained(A_       )


if __name__ == "__main__":
 __lowerCamelCase :      Union[str, Any]										=				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."""
 )

 __lowerCamelCase :      List[Any]										=				parser.parse_args()
 convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Any										=				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      List[Any]										=				[
	    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """InstructBlipQFormerModel""",
	    """InstructBlipPreTrainedModel""",
	    """InstructBlipForConditionalGeneration""",
	    """InstructBlipVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_instructblip import (
	    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    InstructBlipConfig,
	    InstructBlipQFormerConfig,
	    InstructBlipVisionConfig,
	)
	from .processing_instructblip import InstructBlipProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    InstructBlipForConditionalGeneration,
		    InstructBlipPreTrainedModel,
		    InstructBlipQFormerModel,
		    InstructBlipVisionModel,
		)

else:
	import sys

	__lowerCamelCase :      Any										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



