
"""simple docstring"""







import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   SwinConfig()
	__lowerCAmelCase    :		Union[str, Any]		    =   swin_name.split('_'	)

	__lowerCAmelCase    :		Dict		    =   name_split[1]
	__lowerCAmelCase    :		Any		    =   int(name_split[4]	)
	__lowerCAmelCase    :		Optional[Any]		    =   int(name_split[3][-1]	)

	if model_size == "tiny":
		__lowerCAmelCase    :		int		    =   96
		__lowerCAmelCase    :		Union[str, Any]		    =   (2, 2, 6, 2)
		__lowerCAmelCase    :		List[str]		    =   (3, 6, 12, 24)
	elif model_size == "small":
		__lowerCAmelCase    :		List[Any]		    =   96
		__lowerCAmelCase    :		Optional[Any]		    =   (2, 2, 18, 2)
		__lowerCAmelCase    :		Any		    =   (3, 6, 12, 24)
	elif model_size == "base":
		__lowerCAmelCase    :		int		    =   128
		__lowerCAmelCase    :		Optional[int]		    =   (2, 2, 18, 2)
		__lowerCAmelCase    :		Tuple		    =   (4, 8, 16, 32)
	else:
		__lowerCAmelCase    :		Tuple		    =   192
		__lowerCAmelCase    :		List[str]		    =   (2, 2, 18, 2)
		__lowerCAmelCase    :		List[Any]		    =   (6, 12, 24, 48)

	if "in22k" in swin_name:
		__lowerCAmelCase    :		Union[str, Any]		    =   2_1841
	else:
		__lowerCAmelCase    :		Dict		    =   1000
		__lowerCAmelCase    :		List[Any]		    =   'huggingface/label-files'
		__lowerCAmelCase    :		Optional[Any]		    =   'imagenet-1k-id2label.json'
		__lowerCAmelCase    :		Dict		    =   json.load(open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type='dataset'	) ,						'r'	)	)
		__lowerCAmelCase    :		Tuple		    =   {int(_UpperCamelCase	): v for k, v in idalabel.items()}
		__lowerCAmelCase    :		Union[str, Any]		    =   idalabel
		__lowerCAmelCase    :		List[str]		    =   {v: k for k, v in idalabel.items()}

	__lowerCAmelCase    :		List[str]		    =   img_size
	__lowerCAmelCase    :		Dict		    =   num_classes
	__lowerCAmelCase    :		Dict		    =   embed_dim
	__lowerCAmelCase    :		str		    =   depths
	__lowerCAmelCase    :		int		    =   num_heads
	__lowerCAmelCase    :		Optional[Any]		    =   window_size

	return config
def 					__lowerCAmelCase       (_UpperCamelCase	):
	if "patch_embed.proj" in name:
		__lowerCAmelCase    :		Union[str, Any]		    =   name.replace('patch_embed.proj' ,						'embeddings.patch_embeddings.projection'	)
	if "patch_embed.norm" in name:
		__lowerCAmelCase    :		Optional[Any]		    =   name.replace('patch_embed.norm' ,						'embeddings.norm'	)
	if "layers" in name:
		__lowerCAmelCase    :		Optional[int]		    =   'encoder.' + name
	if "attn.proj" in name:
		__lowerCAmelCase    :		Optional[Any]		    =   name.replace('attn.proj' ,						'attention.output.dense'	)
	if "attn" in name:
		__lowerCAmelCase    :		str		    =   name.replace('attn' ,						'attention.self'	)
	if "norm1" in name:
		__lowerCAmelCase    :		Optional[Any]		    =   name.replace('norm1' ,						'layernorm_before'	)
	if "norm2" in name:
		__lowerCAmelCase    :		int		    =   name.replace('norm2' ,						'layernorm_after'	)
	if "mlp.fc1" in name:
		__lowerCAmelCase    :		List[str]		    =   name.replace('mlp.fc1' ,						'intermediate.dense'	)
	if "mlp.fc2" in name:
		__lowerCAmelCase    :		Any		    =   name.replace('mlp.fc2' ,						'output.dense'	)

	if name == "norm.weight":
		__lowerCAmelCase    :		Optional[Any]		    =   'layernorm.weight'
	if name == "norm.bias":
		__lowerCAmelCase    :		Dict		    =   'layernorm.bias'

	if "head" in name:
		__lowerCAmelCase    :		Any		    =   name.replace('head' ,						'classifier'	)
	else:
		__lowerCAmelCase    :		Optional[int]		    =   'swin.' + name

	return name
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	for key in orig_state_dict.copy().keys():
		__lowerCAmelCase    :		List[str]		    =   orig_state_dict.pop(_UpperCamelCase	)

		if "mask" in key:
			continue
		elif "qkv" in key:
			__lowerCAmelCase    :		List[str]		    =   key.split('.'	)
			__lowerCAmelCase    :		int		    =   int(key_split[1]	)
			__lowerCAmelCase    :		Optional[int]		    =   int(key_split[3]	)
			__lowerCAmelCase    :		List[str]		    =   model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

	return orig_state_dict
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   timm.create_model(_UpperCamelCase ,						pretrained=_UpperCamelCase	)
	timm_model.eval()

	__lowerCAmelCase    :		List[str]		    =   get_swin_config(_UpperCamelCase	)
	__lowerCAmelCase    :		int		    =   SwinForImageClassification(_UpperCamelCase	)
	model.eval()

	__lowerCAmelCase    :		str		    =   convert_state_dict(timm_model.state_dict() ,						_UpperCamelCase	)
	model.load_state_dict(_UpperCamelCase	)

	__lowerCAmelCase    :		Dict		    =   'http://images.cocodataset.org/val2017/000000039769.jpg'

	__lowerCAmelCase    :		Optional[Any]		    =   AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' ,						'-'	)	)	)
	__lowerCAmelCase    :		Tuple		    =   Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase	).raw	)
	__lowerCAmelCase    :		str		    =   image_processor(images=_UpperCamelCase ,						return_tensors='pt'	)

	__lowerCAmelCase    :		Optional[int]		    =   timm_model(inputs['pixel_values']	)
	__lowerCAmelCase    :		Tuple		    =   model(**_UpperCamelCase	).logits

	assert torch.allclose(_UpperCamelCase ,						_UpperCamelCase ,						atol=1e-3	)

	print(F"Saving model {swin_name} to {pytorch_dump_folder_path}"	)
	model.save_pretrained(_UpperCamelCase	)

	print(F"Saving image processor to {pytorch_dump_folder_path}"	)
	image_processor.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
							lowerCamelCase__           =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--swin_name""",
							    default="""swin_tiny_patch4_window7_224""",
							    type=str,
							    help="""Name of the Swin timm model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
							)

							lowerCamelCase__           =	parser.parse_args()
							convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)

"""simple docstring"""







from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip