

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 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= SwinConfig()
	UpperCAmelCase__:	Optional[int]									= swin_name.split("_"      )

	UpperCAmelCase__:	Dict									= name_split[1]
	UpperCAmelCase__:	int									= int(name_split[4]      )
	UpperCAmelCase__:	Dict									= int(name_split[3][-1]      )

	if model_size == "tiny":
		UpperCAmelCase__:	Union[str, Any]									= 9_6
		UpperCAmelCase__:	List[str]									= (2, 2, 6, 2)
		UpperCAmelCase__:	Optional[int]									= (3, 6, 1_2, 2_4)
	elif model_size == "small":
		UpperCAmelCase__:	Dict									= 9_6
		UpperCAmelCase__:	str									= (2, 2, 1_8, 2)
		UpperCAmelCase__:	int									= (3, 6, 1_2, 2_4)
	elif model_size == "base":
		UpperCAmelCase__:	Optional[int]									= 1_2_8
		UpperCAmelCase__:	int									= (2, 2, 1_8, 2)
		UpperCAmelCase__:	List[Any]									= (4, 8, 1_6, 3_2)
	else:
		UpperCAmelCase__:	List[Any]									= 1_9_2
		UpperCAmelCase__:	str									= (2, 2, 1_8, 2)
		UpperCAmelCase__:	Tuple									= (6, 1_2, 2_4, 4_8)

	if "in22k" in swin_name:
		UpperCAmelCase__:	List[Any]									= 2_1_8_4_1
	else:
		UpperCAmelCase__:	Optional[Any]									= 1_0_0_0
		UpperCAmelCase__:	List[str]									= "huggingface/label-files"
		UpperCAmelCase__:	Any									= "imagenet-1k-id2label.json"
		UpperCAmelCase__:	Tuple									= json.load(open(hf_hub_download(__a	,__a	,repo_type="dataset"      )	,"r"      )      )
		UpperCAmelCase__:	Optional[Any]									= {int(__a      ): v for k, v in idalabel.items()}
		UpperCAmelCase__:	Dict									= idalabel
		UpperCAmelCase__:	Tuple									= {v: k for k, v in idalabel.items()}

	UpperCAmelCase__:	List[str]									= img_size
	UpperCAmelCase__:	Tuple									= num_classes
	UpperCAmelCase__:	Union[str, Any]									= embed_dim
	UpperCAmelCase__:	List[str]									= depths
	UpperCAmelCase__:	Any									= num_heads
	UpperCAmelCase__:	Optional[int]									= window_size

	return config



def 			_A	(      SCREAMING_SNAKE_CASE      ):

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

	if name == "norm.weight":
		UpperCAmelCase__:	Tuple									= "layernorm.weight"
	if name == "norm.bias":
		UpperCAmelCase__:	Tuple									= "layernorm.bias"

	if "head" in name:
		UpperCAmelCase__:	Any									= name.replace("head"	,"classifier"      )
	else:
		UpperCAmelCase__:	Union[str, Any]									= "swin." + name

	return name



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):

	for key in orig_state_dict.copy().keys():
		UpperCAmelCase__:	str									= orig_state_dict.pop(__a      )

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

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

	return orig_state_dict



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[int]									= timm.create_model(__a	,pretrained=__a      )
	timm_model.eval()

	UpperCAmelCase__:	List[str]									= get_swin_config(__a      )
	UpperCAmelCase__:	Optional[int]									= SwinForImageClassification(__a      )
	model.eval()

	UpperCAmelCase__:	List[str]									= convert_state_dict(timm_model.state_dict()	,__a      )
	model.load_state_dict(__a      )

	UpperCAmelCase__:	Union[str, Any]									= "http://images.cocodataset.org/val2017/000000039769.jpg"

	UpperCAmelCase__:	Union[str, Any]									= AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"	,"-"      )      )      )
	UpperCAmelCase__:	List[Any]									= Image.open(requests.get(__a	,stream=__a      ).raw      )
	UpperCAmelCase__:	Dict									= image_processor(images=__a	,return_tensors="pt"      )

	UpperCAmelCase__:	List[str]									= timm_model(inputs["pixel_values"]      )
	UpperCAmelCase__:	List[str]									= model(**__a      ).logits

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

	print(f"Saving model {swin_name} to {pytorch_dump_folder_path}"      )
	model.save_pretrained(__a      )

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


if __name__ == "__main__":
					_lowerCAmelCase  :      Optional[Any]			 =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  :      List[str]			 =parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A       :   Optional[int]					= {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Optional[int]					= ['''Pix2StructImageProcessor''']


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= [
					    '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Pix2StructPreTrainedModel''',
					    '''Pix2StructForConditionalGeneration''',
					    '''Pix2StructVisionModel''',
					    '''Pix2StructTextModel''',
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					A       :   Any					= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



