





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def lowerCAmelCase__(__snake_case				)							-> str:







	'''simple docstring'''







	lowerCamelCase__														=						SwinConfig(image_size=192				)

	if "base" in model_name:
		lowerCamelCase__														=						6
		lowerCamelCase__														=						128
		lowerCamelCase__														=						(2, 2, 18, 2)
		lowerCamelCase__														=						(4, 8, 16, 32)
	elif "large" in model_name:
		lowerCamelCase__														=						12
		lowerCamelCase__														=						192
		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 lowerCAmelCase__(__snake_case				)							-> Dict:







	'''simple docstring'''







	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 lowerCAmelCase__(__snake_case			,__snake_case				)							-> Union[str, Any]:







	'''simple docstring'''







	for key in orig_state_dict.copy().keys():
		lowerCamelCase__														=						orig_state_dict.pop(A__				)

		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 lowerCAmelCase__(__snake_case			,__snake_case			,__snake_case			,__snake_case				)							-> Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__														=						torch.load(A__			,map_location='''cpu'''				)["model"]

	lowerCamelCase__														=						get_swin_config(A__				)
	lowerCamelCase__														=						SwinForMaskedImageModeling(A__				)
	model.eval()

	lowerCamelCase__														=						convert_state_dict(A__			,A__				)
	model.load_state_dict(A__				)

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

	lowerCamelCase__														=						ViTImageProcessor(size={'''height''': 192, '''width''': 192}				)
	lowerCamelCase__														=						Image.open(requests.get(A__			,stream=A__				).raw				)
	lowerCamelCase__														=						image_processor(images=A__			,return_tensors='''pt'''				)

	with torch.no_grad():
		lowerCamelCase__														=						model(**A__				).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(A__				)

		print(F'Saving image processor to {pytorch_dump_folder_path}'				)
		image_processor.save_pretrained(A__				)

	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"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			lowerCamelCase_							      = None


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_							      = {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_							      = {
    '''camembert-base''': 512,
}

lowerCamelCase_							      = '''▁'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']
	__magic_name__       =							CamembertTokenizer





	def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					Optional[int]=None      ,      lowerCAmelCase_   :					Optional[int]="<s>"      ,      lowerCAmelCase_   :					Dict="</s>"      ,      lowerCAmelCase_   :					Any="</s>"      ,      lowerCAmelCase_   :					Union[str, Any]="<s>"      ,      lowerCAmelCase_   :					int="<unk>"      ,      lowerCAmelCase_   :					Union[str, Any]="<pad>"      ,      lowerCAmelCase_   :					Union[str, Any]="<mask>"      ,      lowerCAmelCase_   :					Any=["<s>NOTUSED", "</s>NOTUSED"]      ,      **lowerCAmelCase_   :					Any      ,      )		->     Union[str, Any]:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_	:					Dict     	=   AddedToken(lowerCAmelCase_      ,      lstrip=lowerCAmelCase_      ,      rstrip=lowerCAmelCase_		) if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else mask_token

				super().__init__(
				    lowerCAmelCase_      ,      tokenizer_file=lowerCAmelCase_      ,      bos_token=lowerCAmelCase_      ,      eos_token=lowerCAmelCase_      ,      sep_token=lowerCAmelCase_      ,      cls_token=lowerCAmelCase_      ,      unk_token=lowerCAmelCase_      ,      pad_token=lowerCAmelCase_      ,      mask_token=lowerCAmelCase_      ,      additional_special_tokens=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				UpperCAmelCase_	:					str     	=   vocab_file
				UpperCAmelCase_	:					Optional[Any]     	=   False if not self.vocab_file else True





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:

				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase_	:					Tuple     	=   [self.cls_token_id]
				UpperCAmelCase_	:					Any     	=   [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:
				UpperCAmelCase_	:					List[Any]     	=   [self.sep_token_id]
				UpperCAmelCase_	:					Optional[int]     	=   [self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."		)

				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_	:					Dict     	=   os.path.join(
				    lowerCAmelCase_      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCAmelCase_		):
							copyfile(self.vocab_file      ,      lowerCAmelCase_		)

				return (out_vocab_file,)



