






'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def      __lowercase	(_lowercase  ) ->  Dict:


     """simple docstring"""
     __lowerCamelCase    :							int					 =					SwinConfig(image_size=192  )

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

     __lowerCamelCase    :							str					 =					window_size
     __lowerCamelCase    :							List[Any]					 =					embed_dim
     __lowerCamelCase    :							Tuple					 =					depths
     __lowerCamelCase    :							int					 =					num_heads

     return config
def      __lowercase	(_lowercase  ) ->  List[Any]:


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

     if name == "encoder.norm.weight":
          __lowerCamelCase    :							Union[str, Any]					 =					'''layernorm.weight'''
     if name == "encoder.norm.bias":
          __lowerCamelCase    :							Optional[Any]					 =					'''layernorm.bias'''

     if "decoder" in name:
          pass
     else:
          __lowerCamelCase    :							Dict					 =					'''swin.''' + name

     return name
def      __lowercase	(_lowercase,    _lowercase  ) ->  int:


     """simple docstring"""
     for key in orig_state_dict.copy().keys():
          __lowerCamelCase    :							List[str]					 =					orig_state_dict.pop(__lowercase  )

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

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

     return orig_state_dict
def      __lowercase	(_lowercase,    _lowercase,    _lowercase,    _lowercase  ) ->  Union[str, Any]:


     """simple docstring"""
     __lowerCamelCase    :							Optional[Any]					 =					torch.load(__lowercase,    map_location="""cpu"""  )['''model''']

     __lowerCamelCase    :							List[Any]					 =					get_swin_config(__lowercase  )
     __lowerCamelCase    :							Optional[int]					 =					SwinForMaskedImageModeling(__lowercase  )
     model.eval()

     __lowerCamelCase    :							List[Any]					 =					convert_state_dict(__lowercase,    __lowercase  )
     model.load_state_dict(__lowercase  )

     __lowerCamelCase    :							List[Any]					 =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''

     __lowerCamelCase    :							Any					 =					ViTImageProcessor(size={"""height""": 192, """width""": 192}  )
     __lowerCamelCase    :							Optional[int]					 =					Image.open(requests.get(__lowercase,    stream=__lowercase  ).raw  )
     __lowerCamelCase    :							Union[str, Any]					 =					image_processor(images=__lowercase,    return_tensors="""pt"""  )

     with torch.no_grad():
          __lowerCamelCase    :							int					 =					model(**__lowercase  ).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(__lowercase  )

          print(f"Saving image processor to {pytorch_dump_folder_path}"  )
          image_processor.save_pretrained(__lowercase  )

     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__":
   UpperCAmelCase__    :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."""
   )

   UpperCAmelCase__    :Optional[int] 							=      parser.parse_args()
   convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase			:	Dict								={
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Optional[int]								=[
	    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AlbertForMaskedLM',
	    'AlbertForMultipleChoice',
	    'AlbertForPreTraining',
	    'AlbertForQuestionAnswering',
	    'AlbertForSequenceClassification',
	    'AlbertForTokenClassification',
	    'AlbertModel',
	    'AlbertPreTrainedModel',
	    'load_tf_weights_in_albert',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=[
	    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFAlbertForMaskedLM',
	    'TFAlbertForMultipleChoice',
	    'TFAlbertForPreTraining',
	    'TFAlbertForQuestionAnswering',
	    'TFAlbertForSequenceClassification',
	    'TFAlbertForTokenClassification',
	    'TFAlbertMainLayer',
	    'TFAlbertModel',
	    'TFAlbertPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Any								=[
	    'FlaxAlbertForMaskedLM',
	    'FlaxAlbertForMultipleChoice',
	    'FlaxAlbertForPreTraining',
	    'FlaxAlbertForQuestionAnswering',
	    'FlaxAlbertForSequenceClassification',
	    'FlaxAlbertForTokenClassification',
	    'FlaxAlbertModel',
	    'FlaxAlbertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert import AlbertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert_fast import AlbertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_albert import (
		    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AlbertForMaskedLM,
		    AlbertForMultipleChoice,
		    AlbertForPreTraining,
		    AlbertForQuestionAnswering,
		    AlbertForSequenceClassification,
		    AlbertForTokenClassification,
		    AlbertModel,
		    AlbertPreTrainedModel,
		    load_tf_weights_in_albert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_albert import (
		    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAlbertForMaskedLM,
		    TFAlbertForMultipleChoice,
		    TFAlbertForPreTraining,
		    TFAlbertForQuestionAnswering,
		    TFAlbertForSequenceClassification,
		    TFAlbertForTokenClassification,
		    TFAlbertMainLayer,
		    TFAlbertModel,
		    TFAlbertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_albert import (
		    FlaxAlbertForMaskedLM,
		    FlaxAlbertForMultipleChoice,
		    FlaxAlbertForPreTraining,
		    FlaxAlbertForQuestionAnswering,
		    FlaxAlbertForSequenceClassification,
		    FlaxAlbertForTokenClassification,
		    FlaxAlbertModel,
		    FlaxAlbertPreTrainedModel,
		)
else:
	import sys

	_UpperCamelCase			:	str								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



