
'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  SwinConfig(image_size=192				)

							if "base" in model_name:
														_snake_case       =  6
														_snake_case       =  128
														_snake_case       =  (2, 2, 18, 2)
														_snake_case       =  (4, 8, 16, 32)
							elif "large" in model_name:
														_snake_case       =  12
														_snake_case       =  192
														_snake_case       =  (2, 2, 18, 2)
														_snake_case       =  (6, 12, 24, 48)
							else:
														raise ValueError("""Model not supported, only supports base and large variants"""				)

							_snake_case       =  window_size
							_snake_case       =  embed_dim
							_snake_case       =  depths
							_snake_case       =  num_heads

							return config






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

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

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

							return name






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							for key in orig_state_dict.copy().keys():
														_snake_case       =  orig_state_dict.pop(_SCREAMING_SNAKE_CASE				)

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

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

							return orig_state_dict






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  torch.load(_SCREAMING_SNAKE_CASE							, map_location="""cpu"""				)["""model"""]

							_snake_case       =  get_swin_config(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  SwinForMaskedImageModeling(_SCREAMING_SNAKE_CASE				)
							model.eval()

							_snake_case       =  convert_state_dict(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
							model.load_state_dict(_SCREAMING_SNAKE_CASE				)

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

							_snake_case       =  ViTImageProcessor(size={"""height""": 192, """width""": 192}				)
							_snake_case       =  Image.open(requests.get(_SCREAMING_SNAKE_CASE							, stream=_SCREAMING_SNAKE_CASE				).raw				)
							_snake_case       =  image_processor(images=_SCREAMING_SNAKE_CASE							, return_tensors="""pt"""				)

							with torch.no_grad():
														_snake_case       =  model(**_SCREAMING_SNAKE_CASE				).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(_SCREAMING_SNAKE_CASE				)

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

							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)

'''simple docstring'''
from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url






@pytest.mark.parametrize("""repo_id"""							, ["""canonical_dataset_name""", """org-name/dataset-name"""]				)
@pytest.mark.parametrize("""path"""							, ["""filename.csv""", """filename with blanks.csv"""]				)
@pytest.mark.parametrize("""revision"""							, [None, """v2"""]				)
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  hf_hub_url(repo_id=_SCREAMING_SNAKE_CASE							, path=_SCREAMING_SNAKE_CASE							, revision=_SCREAMING_SNAKE_CASE				)
							assert url == f"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(_SCREAMING_SNAKE_CASE				)}"""