



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					SwinConfig(image_size=192	)

			if "base" in model_name:
						lowercase__       	=					6
						lowercase__       	=					128
						lowercase__       	=					(2, 2, 18, 2)
						lowercase__       	=					(4, 8, 16, 32)
			elif "large" in model_name:
						lowercase__       	=					12
						lowercase__       	=					192
						lowercase__       	=					(2, 2, 18, 2)
						lowercase__       	=					(6, 12, 24, 48)
			else:
						raise ValueError("Model not supported, only supports base and large variants"	)

			lowercase__       	=					window_size
			lowercase__       	=					embed_dim
			lowercase__       	=					depths
			lowercase__       	=					num_heads

			return config



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			if "encoder.mask_token" in name:
						lowercase__       	=					name.replace("encoder.mask_token"    ,	"embeddings.mask_token"	)
			if "encoder.patch_embed.proj" in name:
						lowercase__       	=					name.replace("encoder.patch_embed.proj"    ,	"embeddings.patch_embeddings.projection"	)
			if "encoder.patch_embed.norm" in name:
						lowercase__       	=					name.replace("encoder.patch_embed.norm"    ,	"embeddings.norm"	)
			if "attn.proj" in name:
						lowercase__       	=					name.replace("attn.proj"    ,	"attention.output.dense"	)
			if "attn" in name:
						lowercase__       	=					name.replace("attn"    ,	"attention.self"	)
			if "norm1" in name:
						lowercase__       	=					name.replace("norm1"    ,	"layernorm_before"	)
			if "norm2" in name:
						lowercase__       	=					name.replace("norm2"    ,	"layernorm_after"	)
			if "mlp.fc1" in name:
						lowercase__       	=					name.replace("mlp.fc1"    ,	"intermediate.dense"	)
			if "mlp.fc2" in name:
						lowercase__       	=					name.replace("mlp.fc2"    ,	"output.dense"	)

			if name == "encoder.norm.weight":
						lowercase__       	=					"layernorm.weight"
			if name == "encoder.norm.bias":
						lowercase__       	=					"layernorm.bias"

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

			return name



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):
			for key in orig_state_dict.copy().keys():
						lowercase__       	=					orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

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

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

			return orig_state_dict



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					torch.load(SCREAMING_SNAKE_CASE_    ,	map_location="cpu"	)["model"]

			lowercase__       	=					get_swin_config(SCREAMING_SNAKE_CASE_	)
			lowercase__       	=					SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE_	)
			model.eval()

			lowercase__       	=					convert_state_dict(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	)
			model.load_state_dict(SCREAMING_SNAKE_CASE_	)

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

			lowercase__       	=					ViTImageProcessor(size={"height": 192, "width": 192}	)
			lowercase__       	=					Image.open(requests.get(SCREAMING_SNAKE_CASE_    ,	stream=SCREAMING_SNAKE_CASE_	).raw	)
			lowercase__       	=					image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="pt"	)

			with torch.no_grad():
						lowercase__       	=					model(**SCREAMING_SNAKE_CASE_	).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(SCREAMING_SNAKE_CASE_	)

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

			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_										=  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_										=  parser.parse_args()
							convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_   :      Dict			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		'''rwkv'''
	__UpperCamelCase   =		{'''max_position_embeddings''': '''context_length'''}




	def __init__(						self							:     List[str]					,      __lowerCamelCase							:     Tuple=50_277					,      __lowerCamelCase							:     List[str]=1_024					,      __lowerCamelCase							:     List[Any]=4_096					,      __lowerCamelCase							:     Tuple=32					,      __lowerCamelCase							:     List[str]=None					,      __lowerCamelCase							:     Optional[int]=None					,      __lowerCamelCase							:     Optional[Any]=1E-5					,      __lowerCamelCase							:     Tuple=0					,      __lowerCamelCase							:     int=0					,      __lowerCamelCase							:     Dict=6					,      __lowerCamelCase							:     List[Any]=False					,      __lowerCamelCase							:     Tuple=True					,      **__lowerCamelCase							:     Optional[int]					,      )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     vocab_size
			__lowercase											=     context_length
			__lowercase											=     hidden_size
			__lowercase											=     num_hidden_layers
			__lowercase											=     attention_hidden_size if attention_hidden_size is not None else hidden_size
			__lowercase											=     intermediate_size if intermediate_size is not None else 4 * hidden_size
			__lowercase											=     layer_norm_epsilon
			__lowercase											=     rescale_every
			__lowercase											=     use_cache

			__lowercase											=     bos_token_id
			__lowercase											=     eos_token_id

			super().__init__(
			    tie_word_embeddings=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase   )




