



"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def   snake_case__     (	__lowerCamelCase       :					Union[str, Any]			):
   """simple docstring"""
   lowerCamelCase__	:	List[Any]				=SwinConfig(image_size=192			)

   if "base" in model_name:
      lowerCamelCase__	:	Optional[Any]				=6
      lowerCamelCase__	:	str				=128
      lowerCamelCase__	:	Optional[int]				=(2, 2, 18, 2)
      lowerCamelCase__	:	Tuple				=(4, 8, 16, 32)
   elif "large" in model_name:
      lowerCamelCase__	:	int				=12
      lowerCamelCase__	:	Union[str, Any]				=192
      lowerCamelCase__	:	str				=(2, 2, 18, 2)
      lowerCamelCase__	:	str				=(6, 12, 24, 48)
   else:
      raise ValueError('''Model not supported, only supports base and large variants'''			)

   lowerCamelCase__	:	Any				=window_size
   lowerCamelCase__	:	List[Any]				=embed_dim
   lowerCamelCase__	:	Any				=depths
   lowerCamelCase__	:	Optional[int]				=num_heads

   return config






def   snake_case__     (	__lowerCamelCase       :					int			):
   """simple docstring"""
   if "encoder.mask_token" in name:
      lowerCamelCase__	:	Union[str, Any]				=name.replace('''encoder.mask_token'''							,	'''embeddings.mask_token'''			)
   if "encoder.patch_embed.proj" in name:
      lowerCamelCase__	:	Optional[int]				=name.replace('''encoder.patch_embed.proj'''							,	'''embeddings.patch_embeddings.projection'''			)
   if "encoder.patch_embed.norm" in name:
      lowerCamelCase__	:	int				=name.replace('''encoder.patch_embed.norm'''							,	'''embeddings.norm'''			)
   if "attn.proj" in name:
      lowerCamelCase__	:	Optional[Any]				=name.replace('''attn.proj'''							,	'''attention.output.dense'''			)
   if "attn" in name:
      lowerCamelCase__	:	List[str]				=name.replace('''attn'''							,	'''attention.self'''			)
   if "norm1" in name:
      lowerCamelCase__	:	Optional[int]				=name.replace('''norm1'''							,	'''layernorm_before'''			)
   if "norm2" in name:
      lowerCamelCase__	:	List[Any]				=name.replace('''norm2'''							,	'''layernorm_after'''			)
   if "mlp.fc1" in name:
      lowerCamelCase__	:	Optional[Any]				=name.replace('''mlp.fc1'''							,	'''intermediate.dense'''			)
   if "mlp.fc2" in name:
      lowerCamelCase__	:	Tuple				=name.replace('''mlp.fc2'''							,	'''output.dense'''			)

   if name == "encoder.norm.weight":
      lowerCamelCase__	:	int				='''layernorm.weight'''
   if name == "encoder.norm.bias":
      lowerCamelCase__	:	int				='''layernorm.bias'''

   if "decoder" in name:
      pass
   else:
      lowerCamelCase__	:	Union[str, Any]				='''swin.''' + name

   return name






def   snake_case__     (	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					str			):
   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      lowerCamelCase__	:	Optional[Any]				=orig_state_dict.pop(__lowerCamelCase			)

      if "attn_mask" in key:
         pass
      elif "qkv" in key:
         lowerCamelCase__	:	List[Any]				=key.split('''.'''			)
         lowerCamelCase__	:	Any				=int(key_split[2]			)
         lowerCamelCase__	:	Optional[Any]				=int(key_split[4]			)
         lowerCamelCase__	:	Dict				=model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            lowerCamelCase__	:	List[Any]				=val[:dim, :]
            lowerCamelCase__	:	List[Any]				=val[
                dim : dim * 2, :
            ]
            lowerCamelCase__	:	List[str]				=val[-dim:, :]
         else:
            lowerCamelCase__	:	Optional[Any]				=val[
                :dim
            ]
            lowerCamelCase__	:	Tuple				=val[
                dim : dim * 2
            ]
            lowerCamelCase__	:	Tuple				=val[
                -dim:
            ]
      else:
         lowerCamelCase__	:	Optional[Any]				=val

   return orig_state_dict






def   snake_case__     (	__lowerCamelCase       :					int							,	__lowerCamelCase       :					Optional[int]							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Optional[int]			):
   """simple docstring"""
   lowerCamelCase__	:	Union[str, Any]				=torch.load(__lowerCamelCase							,	map_location='''cpu'''			)['''model''']

   lowerCamelCase__	:	int				=get_swin_config(__lowerCamelCase			)
   lowerCamelCase__	:	Tuple				=SwinForMaskedImageModeling(__lowerCamelCase			)
   model.eval()

   lowerCamelCase__	:	List[Any]				=convert_state_dict(__lowerCamelCase							,	__lowerCamelCase			)
   model.load_state_dict(__lowerCamelCase			)

   lowerCamelCase__	:	Optional[Any]				='''http://images.cocodataset.org/val2017/000000039769.jpg'''

   lowerCamelCase__	:	str				=ViTImageProcessor(size={'''height''': 192, '''width''': 192}			)
   lowerCamelCase__	:	List[Any]				=Image.open(requests.get(__lowerCamelCase							,	stream=__lowerCamelCase			).raw			)
   lowerCamelCase__	:	Optional[Any]				=image_processor(images=__lowerCamelCase							,	return_tensors='''pt'''			)

   with torch.no_grad():
      lowerCamelCase__	:	str				=model(**__lowerCamelCase			).logits

   print(outputs.keys()			)
   print('''Looks ok!'''			)

   if pytorch_dump_folder_path is not None:
      print(f'''Saving model {model_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 push_to_hub:
      print(f'''Pushing model and image processor for {model_name} to hub'''			)
      model.push_to_hub(f'''microsoft/{model_name}'''			)
      image_processor.push_to_hub(f'''microsoft/{model_name}'''			)


if __name__ == "__main__":
  _lowercase				:				Union[str, Any]   		=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="swin-base-simmim-window6-192",
      type=str,
      choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
      help="Name of the Swin SimMIM model you'd like to convert.",
  )
  parser.add_argument(
      "--checkpoint_path",
      default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
      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 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."
  )

  _lowercase				:				Union[str, Any]   		=		parser.parse_args()
  convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase				:				Optional[Any]   		=		{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				str   		=		["CLIPTokenizerFast"]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Any   		=		["CLIPFeatureExtractor"]
		_lowercase				:				int   		=		["CLIPImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Optional[Any]   		=		[
		    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CLIPModel",
		    "CLIPPreTrainedModel",
		    "CLIPTextModel",
		    "CLIPTextModelWithProjection",
		    "CLIPVisionModel",
		    "CLIPVisionModelWithProjection",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Dict   		=		[
		    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFCLIPModel",
		    "TFCLIPPreTrainedModel",
		    "TFCLIPTextModel",
		    "TFCLIPVisionModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Union[str, Any]   		=		[
		    "FlaxCLIPModel",
		    "FlaxCLIPPreTrainedModel",
		    "FlaxCLIPTextModel",
		    "FlaxCLIPTextPreTrainedModel",
		    "FlaxCLIPVisionModel",
		    "FlaxCLIPVisionPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

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




