

'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			Optional[int]:
					lowerCamelCase_  				=      SwinConfig(image_size=1_92	)

					if "base" in model_name:
										lowerCamelCase_  				=      6
										lowerCamelCase_  				=      1_28
										lowerCamelCase_  				=      (2, 2, 18, 2)
										lowerCamelCase_  				=      (4, 8, 16, 32)
					elif "large" in model_name:
										lowerCamelCase_  				=      12
										lowerCamelCase_  				=      1_92
										lowerCamelCase_  				=      (2, 2, 18, 2)
										lowerCamelCase_  				=      (6, 12, 24, 48)
					else:
										raise ValueError('Model not supported, only supports base and large variants'	)

					lowerCamelCase_  				=      window_size
					lowerCamelCase_  				=      embed_dim
					lowerCamelCase_  				=      depths
					lowerCamelCase_  				=      num_heads

					return config







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			Tuple:
					if "encoder.mask_token" in name:
										lowerCamelCase_  				=      name.replace('encoder.mask_token'    ,'embeddings.mask_token'	)
					if "encoder.patch_embed.proj" in name:
										lowerCamelCase_  				=      name.replace('encoder.patch_embed.proj'    ,'embeddings.patch_embeddings.projection'	)
					if "encoder.patch_embed.norm" in name:
										lowerCamelCase_  				=      name.replace('encoder.patch_embed.norm'    ,'embeddings.norm'	)
					if "attn.proj" in name:
										lowerCamelCase_  				=      name.replace('attn.proj'    ,'attention.output.dense'	)
					if "attn" in name:
										lowerCamelCase_  				=      name.replace('attn'    ,'attention.self'	)
					if "norm1" in name:
										lowerCamelCase_  				=      name.replace('norm1'    ,'layernorm_before'	)
					if "norm2" in name:
										lowerCamelCase_  				=      name.replace('norm2'    ,'layernorm_after'	)
					if "mlp.fc1" in name:
										lowerCamelCase_  				=      name.replace('mlp.fc1'    ,'intermediate.dense'	)
					if "mlp.fc2" in name:
										lowerCamelCase_  				=      name.replace('mlp.fc2'    ,'output.dense'	)

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

					if "decoder" in name:
										pass
					else:
										lowerCamelCase_  				=      'swin.' + name

					return name







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase	)       ->			Union[str, Any]:
					for key in orig_state_dict.copy().keys():
										lowerCamelCase_  				=      orig_state_dict.pop(__UpperCamelCase	)

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

															if "weight" in key:
																				lowerCamelCase_  				=      val[:dim, :]
																				lowerCamelCase_  				=      val[
																				    dim : dim * 2, :
																				]
																				lowerCamelCase_  				=      val[-dim:, :]
															else:
																				lowerCamelCase_  				=      val[
																				    :dim
																				]
																				lowerCamelCase_  				=      val[
																				    dim : dim * 2
																				]
																				lowerCamelCase_  				=      val[
																				    -dim:
																				]
										else:
															lowerCamelCase_  				=      val

					return orig_state_dict







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			Any:
					lowerCamelCase_  				=      torch.load(__UpperCamelCase    ,map_location='cpu'	)['model']

					lowerCamelCase_  				=      get_swin_config(__UpperCamelCase	)
					lowerCamelCase_  				=      SwinForMaskedImageModeling(__UpperCamelCase	)
					model.eval()

					lowerCamelCase_  				=      convert_state_dict(__UpperCamelCase    ,__UpperCamelCase	)
					model.load_state_dict(__UpperCamelCase	)

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

					lowerCamelCase_  				=      ViTImageProcessor(size={'height': 1_92, 'width': 1_92}	)
					lowerCamelCase_  				=      Image.open(requests.get(__UpperCamelCase    ,stream=__UpperCamelCase	).raw	)
					lowerCamelCase_  				=      image_processor(images=__UpperCamelCase    ,return_tensors='pt'	)

					with torch.no_grad():
										lowerCamelCase_  				=      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_	= 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_	= 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_torch_available


_lowercase  =			{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """PegasusXForConditionalGeneration""",
	    """PegasusXModel""",
	    """PegasusXPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_pegasus_x import (
		    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PegasusXForConditionalGeneration,
		    PegasusXModel,
		    PegasusXPreTrainedModel,
		)


else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

