


'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def       _UpperCamelCase (						UpperCamelCase__   ) ->    str:
			UpperCAmelCase__					:   List[Any]       				=       SwinConfig(image_size=1_9_2   )

			if "base" in model_name:
						UpperCAmelCase__					:   int       				=       6
						UpperCAmelCase__					:   str       				=       1_2_8
						UpperCAmelCase__					:   int       				=       (2, 2, 1_8, 2)
						UpperCAmelCase__					:   Dict       				=       (4, 8, 1_6, 3_2)
			elif "large" in model_name:
						UpperCAmelCase__					:   List[Any]       				=       1_2
						UpperCAmelCase__					:   List[str]       				=       1_9_2
						UpperCAmelCase__					:   Tuple       				=       (2, 2, 1_8, 2)
						UpperCAmelCase__					:   Dict       				=       (6, 1_2, 2_4, 4_8)
			else:
						raise ValueError("""Model not supported, only supports base and large variants"""   )

			UpperCAmelCase__					:   Union[str, Any]       				=       window_size
			UpperCAmelCase__					:   Tuple       				=       embed_dim
			UpperCAmelCase__					:   str       				=       depths
			UpperCAmelCase__					:   Optional[Any]       				=       num_heads

			return config







def       _UpperCamelCase (						UpperCamelCase__   ) ->    Any:
			if "encoder.mask_token" in name:
						UpperCAmelCase__					:   Dict       				=       name.replace("""encoder.mask_token"""	, """embeddings.mask_token"""   )
			if "encoder.patch_embed.proj" in name:
						UpperCAmelCase__					:   List[str]       				=       name.replace("""encoder.patch_embed.proj"""	, """embeddings.patch_embeddings.projection"""   )
			if "encoder.patch_embed.norm" in name:
						UpperCAmelCase__					:   str       				=       name.replace("""encoder.patch_embed.norm"""	, """embeddings.norm"""   )
			if "attn.proj" in name:
						UpperCAmelCase__					:   List[str]       				=       name.replace("""attn.proj"""	, """attention.output.dense"""   )
			if "attn" in name:
						UpperCAmelCase__					:   str       				=       name.replace("""attn"""	, """attention.self"""   )
			if "norm1" in name:
						UpperCAmelCase__					:   Optional[int]       				=       name.replace("""norm1"""	, """layernorm_before"""   )
			if "norm2" in name:
						UpperCAmelCase__					:   Optional[int]       				=       name.replace("""norm2"""	, """layernorm_after"""   )
			if "mlp.fc1" in name:
						UpperCAmelCase__					:   Optional[Any]       				=       name.replace("""mlp.fc1"""	, """intermediate.dense"""   )
			if "mlp.fc2" in name:
						UpperCAmelCase__					:   int       				=       name.replace("""mlp.fc2"""	, """output.dense"""   )

			if name == "encoder.norm.weight":
						UpperCAmelCase__					:   List[str]       				=       """layernorm.weight"""
			if name == "encoder.norm.bias":
						UpperCAmelCase__					:   Any       				=       """layernorm.bias"""

			if "decoder" in name:
						pass
			else:
						UpperCAmelCase__					:   Optional[Any]       				=       """swin.""" + name

			return name







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ) ->    str:
			for key in orig_state_dict.copy().keys():
						UpperCAmelCase__					:   int       				=       orig_state_dict.pop(UpperCamelCase__   )

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

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

			return orig_state_dict







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ) ->    int:
			UpperCAmelCase__					:   Dict       				=       torch.load(UpperCamelCase__	, map_location="""cpu"""   )["""model"""]

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

			UpperCAmelCase__					:   List[str]       				=       convert_state_dict(UpperCamelCase__	, UpperCamelCase__   )
			model.load_state_dict(UpperCamelCase__   )

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

			UpperCAmelCase__					:   Any       				=       ViTImageProcessor(size={"""height""": 1_9_2, """width""": 1_9_2}   )
			UpperCAmelCase__					:   Tuple       				=       Image.open(requests.get(UpperCamelCase__	, stream=UpperCamelCase__   ).raw   )
			UpperCAmelCase__					:   str       				=       image_processor(images=UpperCamelCase__	, return_tensors="""pt"""   )

			with torch.no_grad():
						UpperCAmelCase__					:   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__":
		__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 List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 						_snake_case   (       a__		):
					lowerCAmelCase :Optional[int]						=   ['''image_processor''', '''tokenizer''']
					lowerCAmelCase :Optional[int]						=   '''BridgeTowerImageProcessor'''
					lowerCAmelCase :List[str]						=   ('''RobertaTokenizer''', '''RobertaTokenizerFast''')




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								super().__init__(_lowerCamelCase							,							_lowerCamelCase)




					def __call__( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = 0							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   List[str]       				=       self.tokenizer(
								    text=_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=_lowerCamelCase							,							stride=_lowerCamelCase							,							pad_to_multiple_of=_lowerCamelCase							,							return_token_type_ids=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							return_overflowing_tokens=_lowerCamelCase							,							return_special_tokens_mask=_lowerCamelCase							,							return_offsets_mapping=_lowerCamelCase							,							return_length=_lowerCamelCase							,							verbose=_lowerCamelCase							,							return_tensors=_lowerCamelCase							,							**_lowerCamelCase							,							)
								# add pixel_values + pixel_mask
								UpperCAmelCase__					:   Optional[Any]       				=       self.image_processor(
								    _lowerCamelCase							,							return_tensors=_lowerCamelCase							,							do_normalize=_lowerCamelCase							,							do_center_crop=_lowerCamelCase							,							**_lowerCamelCase)
								encoding.update(_lowerCamelCase)

								return encoding




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.batch_decode(*_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.decode(*_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.tokenizer.model_input_names
								UpperCAmelCase__					:   str       				=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))