


"""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 _A						(						lowercase		):







    """simple docstring"""
    a 		=SwinConfig()
    a 		=swin_name.split('''_'''		)

    a 		=name_split[1]
    a 		=int(name_split[4]		)
    a 		=int(name_split[3][-1]		)

    if model_size == "tiny":
        a 		=96
        a 		=(2, 2, 6, 2)
        a 		=(3, 6, 12, 24)
    elif model_size == "small":
        a 		=96
        a 		=(2, 2, 18, 2)
        a 		=(3, 6, 12, 24)
    elif model_size == "base":
        a 		=1_28
        a 		=(2, 2, 18, 2)
        a 		=(4, 8, 16, 32)
    else:
        a 		=1_92
        a 		=(2, 2, 18, 2)
        a 		=(6, 12, 24, 48)

    if "in22k" in swin_name:
        a 		=2_18_41
    else:
        a 		=10_00
        a 		='''huggingface/label-files'''
        a 		='''imagenet-1k-id2label.json'''
        a 		=json.load(open(hf_hub_download(lowercase       ,    lowercase       ,    repo_type='''dataset'''		)       ,    '''r'''		)		)
        a 		={int(lowercase		): v for k, v in idalabel.items()}
        a 		=idalabel
        a 		={v: k for k, v in idalabel.items()}

    a 		=img_size
    a 		=num_classes
    a 		=embed_dim
    a 		=depths
    a 		=num_heads
    a 		=window_size

    return config
def _A						(						lowercase		):







    """simple docstring"""







    if "patch_embed.proj" in name:
        a 		=name.replace('''patch_embed.proj'''       ,    '''embeddings.patch_embeddings.projection'''		)
    if "patch_embed.norm" in name:
        a 		=name.replace('''patch_embed.norm'''       ,    '''embeddings.norm'''		)
    if "layers" in name:
        a 		='''encoder.''' + name
    if "attn.proj" in name:
        a 		=name.replace('''attn.proj'''       ,    '''attention.output.dense'''		)
    if "attn" in name:
        a 		=name.replace('''attn'''       ,    '''attention.self'''		)
    if "norm1" in name:
        a 		=name.replace('''norm1'''       ,    '''layernorm_before'''		)
    if "norm2" in name:
        a 		=name.replace('''norm2'''       ,    '''layernorm_after'''		)
    if "mlp.fc1" in name:
        a 		=name.replace('''mlp.fc1'''       ,    '''intermediate.dense'''		)
    if "mlp.fc2" in name:
        a 		=name.replace('''mlp.fc2'''       ,    '''output.dense'''		)

    if name == "norm.weight":
        a 		='''layernorm.weight'''
    if name == "norm.bias":
        a 		='''layernorm.bias'''

    if "head" in name:
        a 		=name.replace('''head'''       ,    '''classifier'''		)
    else:
        a 		='''swin.''' + name

    return name
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""







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

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

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

    return orig_state_dict
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=timm.create_model(lowercase       ,    pretrained=lowercase		)
    timm_model.eval()

    a 		=get_swin_config(lowercase		)
    a 		=SwinForImageClassification(lowercase		)
    model.eval()

    a 		=convert_state_dict(timm_model.state_dict()       ,    lowercase		)
    model.load_state_dict(lowercase		)

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

    a 		=AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''       ,    '''-'''		)		)		)
    a 		=Image.open(requests.get(lowercase       ,    stream=lowercase		).raw		)
    a 		=image_processor(images=lowercase       ,    return_tensors='''pt'''		)

    a 		=timm_model(inputs['''pixel_values''']		)
    a 		=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__":
       lowerCamelCase_   :   Union[str, 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_   :   Optional[int]									=  parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						List[str]						=      """deformable_detr"""
    a__	:						Union[str, Any]						=      {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }







    def __init__(	self , __lowercase=True , __lowercase=None , __lowercase=3 , __lowercase=300 , __lowercase=1_024 , __lowercase=6 , __lowercase=1_024 , __lowercase=8 , __lowercase=6 , __lowercase=1_024 , __lowercase=8 , __lowercase=0.0 , __lowercase=True , __lowercase="relu" , __lowercase=256 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=1.0 , __lowercase=True , __lowercase=False , __lowercase="sine" , __lowercase="resnet50" , __lowercase=True , __lowercase=False , __lowercase=4 , __lowercase=4 , __lowercase=4 , __lowercase=False , __lowercase=300 , __lowercase=False , __lowercase=1 , __lowercase=5 , __lowercase=2 , __lowercase=1 , __lowercase=1 , __lowercase=5 , __lowercase=2 , __lowercase=0.1 , __lowercase=0.25 , __lowercase=False , **__lowercase , )       ->	int:
        if backbone_config is not None and use_timm_backbone:
            raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

        if not use_timm_backbone:
            if backbone_config is None:
                logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
                __UpperCamelCase     :str			  =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
            elif isinstance(__lowercase , __lowercase):
                __UpperCamelCase     :str			  =		backbone_config.get('''model_type''')
                __UpperCamelCase     :Tuple			  =		CONFIG_MAPPING[backbone_model_type]
                __UpperCamelCase     :Any			  =		config_class.from_dict(__lowercase)
        __UpperCamelCase     :int			  =		use_timm_backbone
        __UpperCamelCase     :Dict			  =		backbone_config
        __UpperCamelCase     :Any			  =		num_channels
        __UpperCamelCase     :Optional[int]			  =		num_queries
        __UpperCamelCase     :Any			  =		max_position_embeddings
        __UpperCamelCase     :str			  =		d_model
        __UpperCamelCase     :Tuple			  =		encoder_ffn_dim
        __UpperCamelCase     :Union[str, Any]			  =		encoder_layers
        __UpperCamelCase     :List[Any]			  =		encoder_attention_heads
        __UpperCamelCase     :Any			  =		decoder_ffn_dim
        __UpperCamelCase     :List[str]			  =		decoder_layers
        __UpperCamelCase     :int			  =		decoder_attention_heads
        __UpperCamelCase     :str			  =		dropout
        __UpperCamelCase     :Any			  =		attention_dropout
        __UpperCamelCase     :int			  =		activation_dropout
        __UpperCamelCase     :List[Any]			  =		activation_function
        __UpperCamelCase     :List[Any]			  =		init_std
        __UpperCamelCase     :List[Any]			  =		init_xavier_std
        __UpperCamelCase     :int			  =		encoder_layerdrop
        __UpperCamelCase     :str			  =		auxiliary_loss
        __UpperCamelCase     :Optional[Any]			  =		position_embedding_type
        __UpperCamelCase     :Union[str, Any]			  =		backbone
        __UpperCamelCase     :Any			  =		use_pretrained_backbone
        __UpperCamelCase     :str			  =		dilation
        # deformable attributes
        __UpperCamelCase     :Optional[Any]			  =		num_feature_levels
        __UpperCamelCase     :str			  =		encoder_n_points
        __UpperCamelCase     :int			  =		decoder_n_points
        __UpperCamelCase     :Union[str, Any]			  =		two_stage
        __UpperCamelCase     :Optional[Any]			  =		two_stage_num_proposals
        __UpperCamelCase     :Dict			  =		with_box_refine
        if two_stage is True and with_box_refine is False:
            raise ValueError('''If two_stage is True, with_box_refine must be True.''')
        # Hungarian matcher
        __UpperCamelCase     :Optional[int]			  =		class_cost
        __UpperCamelCase     :List[Any]			  =		bbox_cost
        __UpperCamelCase     :str			  =		giou_cost
        # Loss coefficients
        __UpperCamelCase     :Tuple			  =		mask_loss_coefficient
        __UpperCamelCase     :Tuple			  =		dice_loss_coefficient
        __UpperCamelCase     :int			  =		bbox_loss_coefficient
        __UpperCamelCase     :Any			  =		giou_loss_coefficient
        __UpperCamelCase     :Dict			  =		eos_coefficient
        __UpperCamelCase     :Optional[Any]			  =		focal_alpha
        __UpperCamelCase     :Optional[Any]			  =		disable_custom_kernels
        super().__init__(is_encoder_decoder=__lowercase , **__lowercase)







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        return self.encoder_attention_heads







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        return self.d_model







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Dict			  =		copy.deepcopy(self.__dict__)
        if self.backbone_config is not None:
            __UpperCamelCase     :Tuple			  =		self.backbone_config.to_dict()
        __UpperCamelCase     :List[Any]			  =		self.__class__.model_type
        return output





