






'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase						:						Any  =					logging.get_logger(__name__)
def __UpperCAmelCase   (					A					:			Union[str, Any]		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Optional[int]          =       "huggingface/label-files"
			UpperCAmelCase_      :       Union[str, Any]          =       "imagenet-1k-id2label.json"
			UpperCAmelCase_      :       Optional[Any]          =       json.load(open(hf_hub_download(A			,       A			,       repo_type='''dataset'''		)			,       '''r'''		)		)
			UpperCAmelCase_      :       List[Any]          =       {int(A		): v for k, v in idalabel.items()}
			UpperCAmelCase_      :       Any          =       {v: k for k, v in idalabel.items()}

			UpperCAmelCase_      :       Dict          =       "std_conv" if "bit" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			UpperCAmelCase_      :       Optional[int]          =       BitConfig(
			    conv_layer=A			,       num_labels=1_0_0_0			,       idalabel=A			,       labelaid=A			,       )

			return config
def __UpperCAmelCase   (					A					:			Any		)    ->   Optional[int]:





			if "stem.conv" in name:
						UpperCAmelCase_      :       Dict          =       name.replace('''stem.conv'''			,       '''bit.embedder.convolution'''		)
			if "blocks" in name:
						UpperCAmelCase_      :       str          =       name.replace('''blocks'''			,       '''layers'''		)
			if "head.fc" in name:
						UpperCAmelCase_      :       List[str]          =       name.replace('''head.fc'''			,       '''classifier.1'''		)
			if name.startswith('''norm'''		):
						UpperCAmelCase_      :       Optional[int]          =       "bit." + name
			if "bit" not in name and "classifier" not in name:
						UpperCAmelCase_      :       Dict          =       "bit.encoder." + name

			return name
def __UpperCAmelCase   (					)    ->   Dict:
			UpperCAmelCase_      :       Any          =       "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCAmelCase_      :       List[str]          =       Image.open(requests.get(A			,       stream=A		).raw		)
			return im
@torch.no_grad()
def __UpperCAmelCase   (					A					:			Dict			,       A					:			str			,       A					:			Tuple=False		)    ->   Optional[int]:
			UpperCAmelCase_      :       str          =       get_config(A		)

			# load original model from timm
			UpperCAmelCase_      :       Any          =       create_model(A			,       pretrained=A		)
			timm_model.eval()

			# load state_dict of original model
			UpperCAmelCase_      :       Union[str, Any]          =       timm_model.state_dict()
			for key in state_dict.copy().keys():
						UpperCAmelCase_      :       Optional[int]          =       state_dict.pop(A		)
						UpperCAmelCase_      :       Optional[int]          =       val.squeeze() if "head" in key else val

			# load HuggingFace model
			UpperCAmelCase_      :       Any          =       BitForImageClassification(A		)
			model.eval()
			model.load_state_dict(A		)

			# create image processor
			UpperCAmelCase_      :       Tuple          =       create_transform(**resolve_data_config({}			,       model=A		)		)
			UpperCAmelCase_      :       Any          =       transform.transforms

			UpperCAmelCase_      :       Union[str, Any]          =       {
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

			UpperCAmelCase_      :       Optional[int]          =       BitImageProcessor(
			    do_resize=A			,       size={'''shortest_edge''': timm_transforms[0].size}			,       resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,       do_center_crop=A			,       crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}			,       do_normalize=A			,       image_mean=timm_transforms[-1].mean.tolist()			,       image_std=timm_transforms[-1].std.tolist()			,       )

			UpperCAmelCase_      :       Optional[Any]          =       prepare_img()
			UpperCAmelCase_      :       List[str]          =       transform(A		).unsqueeze(0		)
			UpperCAmelCase_      :       List[str]          =       processor(A			,       return_tensors='''pt'''		).pixel_values

			# verify pixel values
			assert torch.allclose(A			,       A		)

			# verify logits
			with torch.no_grad():
						UpperCAmelCase_      :       Any          =       model(A		)
						UpperCAmelCase_      :       List[Any]          =       outputs.logits

			print('''Logits:'''			,       logits[0, :3]		)
			print('''Predicted class:'''			,       model.config.idalabel[logits.argmax(-1		).item()]		)
			UpperCAmelCase_      :       Union[str, Any]          =       timm_model(A		)
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(A			,       outputs.logits			,       atol=1e-3		)
			print('''Looks ok!'''		)

			if pytorch_dump_folder_path is not None:
						Path(A		).mkdir(exist_ok=A		)
						print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"		)
						model.save_pretrained(A		)
						processor.save_pretrained(A		)

			if push_to_hub:
						print(F"Pushing model {model_name} and processor to the hub"		)
						model.push_to_hub(F"ybelkada/{model_name}"		)
						processor.push_to_hub(F"ybelkada/{model_name}"		)


if __name__ == "__main__":
						_UpperCamelCase						:						Optional[Any]  =					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='resnetv2_50x1_bitm',
						    type=str,
						    help='Name of the BiT 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.'
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model to the hub.',
						)

						_UpperCamelCase						:						Optional[Any]  =					parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""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		(		lowercase    :    Optional[int]					)      ->		Tuple:


    """simple docstring"""





    snake_case       : Optional[Any]    =       SwinConfig()
    snake_case       : Union[str, Any]    =       swin_name.split("_"					)

    snake_case       : str    =       name_split[1]
    snake_case       : Tuple    =       int(name_split[4]					)
    snake_case       : str    =       int(name_split[3][-1]					)

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

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

    snake_case       : Tuple    =       img_size
    snake_case       : Any    =       num_classes
    snake_case       : str    =       embed_dim
    snake_case       : Any    =       depths
    snake_case       : Dict    =       num_heads
    snake_case       : List[Any]    =       window_size

    return config







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]					)      ->		Any:


    """simple docstring"""





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

    if name == "norm.weight":
        snake_case       : Optional[int]    =       "layernorm.weight"
    if name == "norm.bias":
        snake_case       : Tuple    =       "layernorm.bias"

    if "head" in name:
        snake_case       : Any    =       name.replace("head"		,				"classifier"					)
    else:
        snake_case       : Tuple    =       "swin." + name

    return name







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    List[Any]					)      ->		Any:


    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        snake_case       : int    =       orig_state_dict.pop(lowercase					)

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

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

    return orig_state_dict







def     __lowerCAmelCase		(		lowercase    :    Tuple		,				lowercase    :    Any					)      ->		Dict:


    """simple docstring"""





    snake_case       : str    =       timm.create_model(lowercase		,				pretrained=lowercase					)
    timm_model.eval()

    snake_case       : int    =       get_swin_config(lowercase					)
    snake_case       : Optional[int]    =       SwinForImageClassification(lowercase					)
    model.eval()

    snake_case       : int    =       convert_state_dict(timm_model.state_dict()		,				lowercase					)
    model.load_state_dict(lowercase					)

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

    snake_case       : Tuple    =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"		,				"-"					)					)					)
    snake_case       : Optional[int]    =       Image.open(requests.get(lowercase		,				stream=lowercase					).raw					)
    snake_case       : Dict    =       image_processor(images=lowercase		,				return_tensors="pt"					)

    snake_case       : str    =       timm_model(inputs["pixel_values"]					)
    snake_case       : Tuple    =       model(**lowercase					).logits

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

    print(F'Saving model {swin_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 __name__ == "__main__":
  __snake_case       =       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."""
  )

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





