




"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Optional[int]    =    SwinConfig(image_size=192		)

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

				_lowerCamelCase     :			List[str]    =    window_size
				_lowerCamelCase     :			int    =    embed_dim
				_lowerCamelCase     :			Optional[Any]    =    depths
				_lowerCamelCase     :			Any    =    num_heads

				return config







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

				if name == "encoder.norm.weight":
								_lowerCamelCase     :			int    =    'layernorm.weight'
				if name == "encoder.norm.bias":
								_lowerCamelCase     :			List[str]    =    'layernorm.bias'

				if "decoder" in name:
								pass
				else:
								_lowerCamelCase     :			Union[str, Any]    =    'swin.' + name

				return name







def      _snake_case			(				lowercase__							,    lowercase__		):
				for key in orig_state_dict.copy().keys():
								_lowerCamelCase     :			Any    =    orig_state_dict.pop(lowercase__		)

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

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

				return orig_state_dict







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Optional[Any]    =    torch.load(lowercase__							,    map_location='cpu'		)['model']

				_lowerCamelCase     :			Optional[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     :			int    =    'http://images.cocodataset.org/val2017/000000039769.jpg'

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

				with torch.no_grad():
								_lowerCamelCase     :			List[Any]    =    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__":
			lowercase__     						=						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."""
			)

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



import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase       =							'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase       =							direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCAmelCase       =							transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCAmelCase       =							re.compile(R'\[(.+?)\]\((https://huggingface\.co/.+?)\)')


lowerCAmelCase       =							{
    'DecisionTransformerConfig',
    'EncoderDecoderConfig',
    'MusicgenConfig',
    'RagConfig',
    'SpeechEncoderDecoderConfig',
    'TimmBackboneConfig',
    'VisionEncoderDecoderConfig',
    'VisionTextDualEncoderConfig',
    'LlamaConfig',
}
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      None

		# source code of `config_class`
		lowercase__            =      inspect.getsource(SCREAMING_SNAKE_CASE     )
		lowercase__            =      _re_checkpoint.findall(SCREAMING_SNAKE_CASE     )

		# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
		# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
		for ckpt_name, ckpt_link in checkpoints:
				# allow the link to end with `/`
				if ckpt_link.endswith('''/'''     ):
						lowercase__            =      ckpt_link[:-1]

				# verify the checkpoint name corresponds to the checkpoint link
				lowercase__            =      f'https://huggingface.co/{ckpt_name}'
				if ckpt_link == ckpt_link_from_name:
						lowercase__            =      ckpt_name
						break

		return checkpoint
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      []

		for config_class in list(CONFIG_MAPPING.values()     ):
				# Skip deprecated models
				if "models.deprecated" in config_class.__module__:
						continue
				lowercase__            =      get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE     )

				lowercase__            =      config_class.__name__
				if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
						configs_without_checkpoint.append(SCREAMING_SNAKE_CASE     )

		if len(SCREAMING_SNAKE_CASE     ) > 0:
				lowercase__            =      '''\n'''.join(sorted(SCREAMING_SNAKE_CASE     )     )
				raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}'     )


if __name__ == "__main__":
			check_config_docstrings_have_checkpoints()


