






'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Tuple:
							lowerCamelCase__					:	Tuple         =       SwinConfig(image_size=192      )

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

							lowerCamelCase__					:	str         =       window_size
							lowerCamelCase__					:	Dict         =       embed_dim
							lowerCamelCase__					:	Tuple         =       depths
							lowerCamelCase__					:	str         =       num_heads

							return config





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       int:
							if "encoder.mask_token" in name:
														lowerCamelCase__					:	Any         =       name.replace("""encoder.mask_token"""				,			"""embeddings.mask_token"""      )
							if "encoder.patch_embed.proj" in name:
														lowerCamelCase__					:	List[Any]         =       name.replace("""encoder.patch_embed.proj"""				,			"""embeddings.patch_embeddings.projection"""      )
							if "encoder.patch_embed.norm" in name:
														lowerCamelCase__					:	str         =       name.replace("""encoder.patch_embed.norm"""				,			"""embeddings.norm"""      )
							if "attn.proj" in name:
														lowerCamelCase__					:	List[Any]         =       name.replace("""attn.proj"""				,			"""attention.output.dense"""      )
							if "attn" in name:
														lowerCamelCase__					:	List[Any]         =       name.replace("""attn"""				,			"""attention.self"""      )
							if "norm1" in name:
														lowerCamelCase__					:	Tuple         =       name.replace("""norm1"""				,			"""layernorm_before"""      )
							if "norm2" in name:
														lowerCamelCase__					:	int         =       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__					:	Optional[Any]         =       name.replace("""mlp.fc2"""				,			"""output.dense"""      )

							if name == "encoder.norm.weight":
														lowerCamelCase__					:	Optional[int]         =       """layernorm.weight"""
							if name == "encoder.norm.bias":
														lowerCamelCase__					:	str         =       """layernorm.bias"""

							if "decoder" in name:
														pass
							else:
														lowerCamelCase__					:	Tuple         =       """swin.""" + name

							return name





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Optional[int]:
							for key in orig_state_dict.copy().keys():
														lowerCamelCase__					:	Tuple         =       orig_state_dict.pop(UpperCamelCase      )

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

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

							return orig_state_dict





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Optional[Any]:
							lowerCamelCase__					:	Optional[int]         =       torch.load(UpperCamelCase				,			map_location="""cpu"""      )["""model"""]

							lowerCamelCase__					:	List[str]         =       get_swin_config(UpperCamelCase      )
							lowerCamelCase__					:	str         =       SwinForMaskedImageModeling(UpperCamelCase      )
							model.eval()

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

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

							lowerCamelCase__					:	Dict         =       ViTImageProcessor(size={"""height""": 192, """width""": 192}      )
							lowerCamelCase__					:	Dict         =       Image.open(requests.get(UpperCamelCase				,			stream=UpperCamelCase      ).raw      )
							lowerCamelCase__					:	Union[str, Any]         =       image_processor(images=UpperCamelCase				,			return_tensors="""pt"""      )

							with torch.no_grad():
														lowerCamelCase__					:	List[Any]         =       model(**UpperCamelCase      ).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(UpperCamelCase      )

														print(f'''Saving image processor to {pytorch_dump_folder_path}'''      )
														image_processor.save_pretrained(UpperCamelCase      )

							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__":
			_A	:			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.'''
			)

			_A	:			int         =parser.parse_args()
			convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ResNetForImageClassification",
					    "ResNetModel",
					    "ResNetPreTrainedModel",
					    "ResNetBackbone",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFResNetForImageClassification",
					    "TFResNetModel",
					    "TFResNetPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    [
					    "FlaxResNetForImageClassification",
					    "FlaxResNetModel",
					    "FlaxResNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_resnet import (
										    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ResNetBackbone,
										    ResNetForImageClassification,
										    ResNetModel,
										    ResNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_resnet import (
										    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFResNetForImageClassification,
										    TFResNetModel,
										    TFResNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Any												=    _LazyModule(__name__, globals()["__file__"], _import_structure)



