





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def      _lowercase					(						_UpperCAmelCase				)						->  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      _lowercase					(						_UpperCAmelCase				)						->  Any:
				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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  List[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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Union[str, 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__":
							UpperCAmelCase__						:			List[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.'''
							)

							UpperCAmelCase__						:			Optional[Any]									=parser.parse_args()
							convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)












from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
							from .pipeline_kandinsky import KandinskyPipeline
							from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
							from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
							from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
							from .text_encoder import MultilingualCLIP





