



"""simple docstring"""




import torch

from transformers import AutoModel






class       UpperCAmelCase_    (							torch.nn.Module		):



      def __init__(	self	:      List[Any]	,      A	:      List[str]="sayef/fsner-bert-base-uncased"		):
           super(_lowercase	,      self		).__init__()

           _UpperCAmelCase     :      Optional[int]														=       AutoModel.from_pretrained(_lowercase	,      return_dict=_lowercase		)
           _UpperCAmelCase     :      int														=       torch.nn.CosineSimilarity(3	,      1e-08		)
           _UpperCAmelCase     :      Any														=       torch.nn.Softmax(dim=1		)



      def 		snake_case_   (	self	:      str	,      **A	:      int		):
           return self.bert(**_lowercase		).last_hidden_state



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Union[str, Any]		):
           return token_embeddings.sum(2	,      keepdim=_lowercase		)



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Dict	,      A	:      List[str]	,      A	:      List[str]=1		):
           return self.softmax(T * self.cos(_lowercase	,      _lowercase		)		)



      def 		snake_case_   (	self	:      Any	,      A	:      Optional[int]	,      A	:      Dict		):
           _UpperCAmelCase     :      str														=       W_supports["sizes"].tolist()
           _UpperCAmelCase     :      Optional[Any]														=       W_supports["start_token_id"].item()
           _UpperCAmelCase     :      str														=       W_supports["end_token_id"].item()

           del W_supports["sizes"]
           del W_supports["start_token_id"]
           del W_supports["end_token_id"]

           _UpperCAmelCase     :      Optional[int]														=       self.BERT(**_lowercase		)
           _UpperCAmelCase     :      List[str]														=       self.BERT(**_lowercase		)

           _UpperCAmelCase     :      List[Any]														=       None
           _UpperCAmelCase     :      str														=       None

           _UpperCAmelCase     :      str														=       W_supports["input_ids"] == start_token_id
           _UpperCAmelCase     :      Union[str, Any]														=       W_supports["input_ids"] == end_token_id

           for i, size in enumerate(_lowercase		):
                if i == 0:
                     _UpperCAmelCase     :      List[Any]														=       0
                else:
                     _UpperCAmelCase     :      str														=       support_sizes[i - 1]

                _UpperCAmelCase     :      int														=       S[s : s + size][start_token_masks[s : s + size]]
                _UpperCAmelCase     :      str														=       S[s : s + size][end_token_masks[s : s + size]]

                _UpperCAmelCase     :      Dict														=       torch.matmul(q[i]	,      s_start.T		).sum(1		).softmax(0		)
                _UpperCAmelCase     :      str														=       torch.matmul(q[i]	,      s_end.T		).sum(1		).softmax(0		)

                if p_starts is not None:
                     _UpperCAmelCase     :      int														=       torch.vstack((p_starts, p_start)		)
                     _UpperCAmelCase     :      Optional[Any]														=       torch.vstack((p_ends, p_end)		)
                else:
                     _UpperCAmelCase     :      Any														=       p_start
                     _UpperCAmelCase     :      Dict														=       p_end

           return p_starts, p_ends



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()
SCREAMING_SNAKE_CASE		:Optional[int]						=     logging.get_logger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						"huggingface/label-files"
    UpperCamelCase_			  =						"imagenet-1k-id2label.json"
    UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
    UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
    UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    UpperCamelCase_			  =						"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_			  =						BitConfig(
        conv_layer=SCREAMING_SNAKE_CASE_			,						num_labels=1_0_0_0			,						idalabel=SCREAMING_SNAKE_CASE_			,						labelaid=SCREAMING_SNAKE_CASE_			,						)

    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    if "stem.conv" in name:
        UpperCamelCase_			  =						name.replace("stem.conv"			,						"bit.embedder.convolution"			)
    if "blocks" in name:
        UpperCamelCase_			  =						name.replace("blocks"			,						"layers"			)
    if "head.fc" in name:
        UpperCamelCase_			  =						name.replace("head.fc"			,						"classifier.1"			)
    if name.startswith("norm"			):
        UpperCamelCase_			  =						"bit." + name
    if "bit" not in name and "classifier" not in name:
        UpperCamelCase_			  =						"bit.encoder." + name

    return name




def 			lowerCAmelCase(      )->						int:



    """simple docstring"""






    UpperCamelCase_			  =						"http://images.cocodataset.org/val2017/000000039769.jpg"
    UpperCamelCase_			  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,						stream=SCREAMING_SNAKE_CASE_			).raw			)
    return im




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						Tuple:



    """simple docstring"""






    UpperCamelCase_			  =						get_config(SCREAMING_SNAKE_CASE_			)

    # load original model from timm
    UpperCamelCase_			  =						create_model(SCREAMING_SNAKE_CASE_			,						pretrained=SCREAMING_SNAKE_CASE_			)
    timm_model.eval()

    # load state_dict of original model
    UpperCamelCase_			  =						timm_model.state_dict()
    for key in state_dict.copy().keys():
        UpperCamelCase_			  =						state_dict.pop(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						val.squeeze() if "head" in key else val

    # load HuggingFace model
    UpperCamelCase_			  =						BitForImageClassification(SCREAMING_SNAKE_CASE_			)
    model.eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE_			)

    # create image processor
    UpperCamelCase_			  =						create_transform(**resolve_data_config({}			,						model=SCREAMING_SNAKE_CASE_			)			)
    UpperCamelCase_			  =						transform.transforms

    UpperCamelCase_			  =						{
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

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

    UpperCamelCase_			  =						prepare_img()
    UpperCamelCase_			  =						transform(SCREAMING_SNAKE_CASE_			).unsqueeze(0			)
    UpperCamelCase_			  =						processor(SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			).pixel_values

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

    # verify logits
    with torch.no_grad():
        UpperCamelCase_			  =						model(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						outputs.logits

    print("Logits:"			,						logits[0, :3]			)
    print("Predicted class:"			,						model.config.idalabel[logits.argmax(-1			).item()]			)
    UpperCamelCase_			  =						timm_model(SCREAMING_SNAKE_CASE_			)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(SCREAMING_SNAKE_CASE_			,						outputs.logits			,						atol=1E-3			)
    print("Looks ok!"			)

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

    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__":
 SCREAMING_SNAKE_CASE		: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.""",
 )

 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
