





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Dict:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   SwinConfig(image_size=192					)

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

						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   window_size
						__SCREAMING_SNAKE_CASE:				int           =   embed_dim
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   depths
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   num_heads

						return config






def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict					)      ->    List[Any]:

						"""simple docstring"""





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

						if name == "encoder.norm.weight":
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   '''layernorm.weight'''
						if name == "encoder.norm.bias":
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   '''layernorm.bias'''

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

						return name






def 		lowerCAmelCase  (	UpperCamelCase__   :   Any       ,			UpperCamelCase__   :   Tuple					)      ->    Any:

						"""simple docstring"""





						for key in orig_state_dict.copy().keys():
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   orig_state_dict.pop(UpperCamelCase__					)

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

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

						return orig_state_dict






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]       ,			UpperCamelCase__   :   Any       ,			UpperCamelCase__   :   int       ,			UpperCamelCase__   :   Union[str, Any]					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.load(UpperCamelCase__       ,			map_location='''cpu'''					)['''model''']

						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   get_swin_config(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				int           =   SwinForMaskedImageModeling(UpperCamelCase__					)
						model.eval()

						__SCREAMING_SNAKE_CASE:				str           =   convert_state_dict(UpperCamelCase__       ,			UpperCamelCase__					)
						model.load_state_dict(UpperCamelCase__					)

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

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

						with torch.no_grad():
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   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__":
							lowerCAmelCase							:       int					 =	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."""
							)

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










from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = False			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(
										    features=_lowerCAmelCase			, cache_dir=_lowerCAmelCase			, keep_in_memory=_lowerCAmelCase			, streaming=_lowerCAmelCase			, num_proc=_lowerCAmelCase			, **_lowerCAmelCase			, )
										__SCREAMING_SNAKE_CASE:				Any           =   Generator(
										    cache_dir=_lowerCAmelCase			, features=_lowerCAmelCase			, generator=_lowerCAmelCase			, gen_kwargs=_lowerCAmelCase			, **_lowerCAmelCase			, )






				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.streaming:
																__SCREAMING_SNAKE_CASE:				List[str]           =   self.builder.as_streaming_dataset(split='''train'''					)
										# Build regular (map-style) dataset
										else:
																__SCREAMING_SNAKE_CASE:				str           =   None
																__SCREAMING_SNAKE_CASE:				List[Any]           =   None
																__SCREAMING_SNAKE_CASE:				Tuple           =   None
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   None

																self.builder.download_and_prepare(
																    download_config=_lowerCAmelCase			, download_mode=_lowerCAmelCase			, verification_mode=_lowerCAmelCase			, base_path=_lowerCAmelCase			, num_proc=self.num_proc			, )
																__SCREAMING_SNAKE_CASE:				List[str]           =   self.builder.as_dataset(
																    split='''train'''			, verification_mode=_lowerCAmelCase			, in_memory=self.keep_in_memory					)
										return dataset



