
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 							__A(lowerCAmelCase )		->				Dict:

		"""simple docstring"""






		_UpperCamelCase						    =	SwinConfig(image_size=1_9_2 )

		if "base" in model_name:
				_UpperCamelCase						    =	6
				_UpperCamelCase						    =	1_2_8
				_UpperCamelCase						    =	(2, 2, 1_8, 2)
				_UpperCamelCase						    =	(4, 8, 1_6, 3_2)
		elif "large" in model_name:
				_UpperCamelCase						    =	1_2
				_UpperCamelCase						    =	1_9_2
				_UpperCamelCase						    =	(2, 2, 1_8, 2)
				_UpperCamelCase						    =	(6, 1_2, 2_4, 4_8)
		else:
				raise ValueError("""Model not supported, only supports base and large variants""" )

		_UpperCamelCase						    =	window_size
		_UpperCamelCase						    =	embed_dim
		_UpperCamelCase						    =	depths
		_UpperCamelCase						    =	num_heads

		return config


def 							__A(lowerCAmelCase )		->				List[Any]:

		"""simple docstring"""






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

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

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

		return name


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				Union[str, Any]:

		"""simple docstring"""






		for key in orig_state_dict.copy().keys():
				_UpperCamelCase						    =	orig_state_dict.pop(lowerCAmelCase )

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

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

		return orig_state_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )["""model"""]

		_UpperCamelCase						    =	get_swin_config(lowerCAmelCase )
		_UpperCamelCase						    =	SwinForMaskedImageModeling(lowerCAmelCase )
		model.eval()

		_UpperCamelCase						    =	convert_state_dict(lowerCAmelCase						,  lowerCAmelCase )
		model.load_state_dict(lowerCAmelCase )

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

		_UpperCamelCase						    =	ViTImageProcessor(size={"""height""": 1_9_2, """width""": 1_9_2} )
		_UpperCamelCase						    =	Image.open(requests.get(lowerCAmelCase						,  stream=lowerCAmelCase ).raw )
		_UpperCamelCase						    =	image_processor(images=lowerCAmelCase						,  return_tensors="""pt""" )

		with torch.no_grad():
				_UpperCamelCase						    =	model(**lowerCAmelCase ).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(lowerCAmelCase )

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

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





import warnings
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        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Dict	=		["image_processor", "tokenizer"]
						UpperCamelCase_							: Union[str, Any]	=		"ViltImageProcessor"
						UpperCamelCase_							: List[str]	=		("BertTokenizer", "BertTokenizerFast")

						def __init__(			self       ,							a=None       ,							a=None       ,							**a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
										    """ instead."""       ,							a       ,							)
										_UpperCamelCase						    =	kwargs.pop("""feature_extractor"""				)

								_UpperCamelCase						    =	image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
										raise ValueError("""You need to specify an `image_processor`."""				)
								if tokenizer is None:
										raise ValueError("""You need to specify a `tokenizer`."""				)

								super().__init__(a       ,							a				)
								_UpperCamelCase						    =	self.image_processor

						def __call__(			self       ,							a       ,							a = None       ,							a = True       ,							a = False       ,							a = None       ,							a = None       ,							a = 0       ,							a = None       ,							a = None       ,							a = None       ,							a = False       ,							a = False       ,							a = False       ,							a = False       ,							a = True       ,							a = None       ,							**a       ,							)					->      BatchEncoding:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer(
								    text=a       ,							add_special_tokens=a       ,							padding=a       ,							truncation=a       ,							max_length=a       ,							stride=a       ,							pad_to_multiple_of=a       ,							return_token_type_ids=a       ,							return_attention_mask=a       ,							return_overflowing_tokens=a       ,							return_special_tokens_mask=a       ,							return_offsets_mapping=a       ,							return_length=a       ,							verbose=a       ,							return_tensors=a       ,							**a       ,							)
								# add pixel_values + pixel_mask
								_UpperCamelCase						    =	self.image_processor(a       ,							return_tensors=a				)
								encoding.update(a				)

								return encoding

						def  A_				(			self       ,							*a       ,							**a				)					->      Optional[Any]:


								'''simple docstring'''

								return self.tokenizer.batch_decode(*a       ,							**a				)

						def  A_				(			self       ,							*a       ,							**a				)					->      int:


								'''simple docstring'''

								return self.tokenizer.decode(*a       ,							**a				)

						@property
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer.model_input_names
								_UpperCamelCase						    =	self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,							a       ,							)
								return self.image_processor_class







						@property
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,							a       ,							)
								return self.image_processor



