import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=False     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		[]
    for i in range(config.num_hidden_layers     ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")     )
        rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")     )
        rename_keys.append(
            (f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")     )
        rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")     )
        rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")     )
        rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")     )
        rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")     )
        rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")     )
        rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")     )
        rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")     )

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ('''module.cls_token''', '''vit.embeddings.cls_token'''),
            ('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
            ('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
            ('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
        ]     )

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ('''module.norm.weight''', '''layernorm.weight'''),
                ('''module.norm.bias''', '''layernorm.bias'''),
            ]     )

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        __UpperCamelCase					:Optional[Any]        =		[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''     ) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''norm.weight''', '''vit.layernorm.weight'''),
                ('''norm.bias''', '''vit.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]     )

    return rename_keys



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=False     ):
    '''simple docstring'''







    for i in range(config.num_hidden_layers     ):
        if base_model:
            __UpperCamelCase					:Union[str, Any]        =		''''''
        else:
            __UpperCamelCase					:int        =		'''vit.'''
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        __UpperCamelCase					:Optional[int]        =		state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight"""     )
        __UpperCamelCase					:Optional[Any]        =		state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias"""     )
        # next, add query, keys and values (in that order) to the state dict
        __UpperCamelCase					:List[str]        =		in_proj_weight[
            : config.hidden_size, :
        ]
        __UpperCamelCase					:Any        =		in_proj_bias[: config.hidden_size]
        __UpperCamelCase					:Dict        =		in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __UpperCamelCase					:Union[str, Any]        =		in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        __UpperCamelCase					:Any        =		in_proj_weight[
            -config.hidden_size :, :
        ]
        __UpperCamelCase					:Optional[Any]        =		in_proj_bias[-config.hidden_size :]



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Union[str, Any]        =		[
        '''module.fc.fc1.weight''',
        '''module.fc.fc1.bias''',
        '''module.fc.bn1.weight''',
        '''module.fc.bn1.bias''',
        '''module.fc.bn1.running_mean''',
        '''module.fc.bn1.running_var''',
        '''module.fc.bn1.num_batches_tracked''',
        '''module.fc.fc2.weight''',
        '''module.fc.fc2.bias''',
        '''module.fc.bn2.weight''',
        '''module.fc.bn2.bias''',
        '''module.fc.bn2.running_mean''',
        '''module.fc.bn2.running_var''',
        '''module.fc.bn2.num_batches_tracked''',
        '''module.fc.fc3.weight''',
        '''module.fc.fc3.bias''',
    ]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:str        =		dct.pop(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Union[str, Any]        =		val



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		ViTMSNConfig()
    __UpperCamelCase					:Union[str, Any]        =		1_000

    __UpperCamelCase					:Optional[Any]        =		'''datasets/huggingface/label-files'''
    __UpperCamelCase					:Optional[int]        =		'''imagenet-1k-id2label.json'''
    __UpperCamelCase					:Optional[int]        =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )  ,      '''r'''     )     )
    __UpperCamelCase					:Tuple        =		{int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
    __UpperCamelCase					:int        =		idalabel
    __UpperCamelCase					:Optional[Any]        =		{v: k for k, v in idalabel.items()}

    if "s16" in checkpoint_url:
        __UpperCamelCase					:Optional[Any]        =		384
        __UpperCamelCase					:str        =		1_536
        __UpperCamelCase					:Any        =		6
    elif "l16" in checkpoint_url:
        __UpperCamelCase					:Union[str, Any]        =		1_024
        __UpperCamelCase					:Union[str, Any]        =		4_096
        __UpperCamelCase					:Optional[int]        =		24
        __UpperCamelCase					:Optional[int]        =		16
        __UpperCamelCase					:Union[str, Any]        =		0.1
    elif "b4" in checkpoint_url:
        __UpperCamelCase					:str        =		4
    elif "l7" in checkpoint_url:
        __UpperCamelCase					:Dict        =		7
        __UpperCamelCase					:int        =		1_024
        __UpperCamelCase					:List[str]        =		4_096
        __UpperCamelCase					:Optional[Any]        =		24
        __UpperCamelCase					:Any        =		16
        __UpperCamelCase					:Optional[Any]        =		0.1

    __UpperCamelCase					:Optional[Any]        =		ViTMSNModel(SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:int        =		torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )['''target_encoder''']

    __UpperCamelCase					:Optional[Any]        =		ViTImageProcessor(size=config.image_size     )

    remove_projection_head(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Union[str, Any]        =		create_rename_keys(SCREAMING_SNAKE_CASE  ,      base_model=SCREAMING_SNAKE_CASE     )

    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    read_in_q_k_v(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      base_model=SCREAMING_SNAKE_CASE     )

    model.load_state_dict(SCREAMING_SNAKE_CASE     )
    model.eval()

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

    __UpperCamelCase					:Tuple        =		Image.open(requests.get(SCREAMING_SNAKE_CASE  ,      stream=SCREAMING_SNAKE_CASE     ).raw     )
    __UpperCamelCase					:Union[str, Any]        =		ViTImageProcessor(
        size=config.image_size  ,      image_mean=SCREAMING_SNAKE_CASE  ,      image_std=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:List[str]        =		image_processor(images=SCREAMING_SNAKE_CASE  ,      return_tensors='''pt'''     )

    # forward pass
    torch.manual_seed(2     )
    __UpperCamelCase					:Optional[int]        =		model(**SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:List[str]        =		outputs.last_hidden_state

    # The following Colab Notebook was used to generate these outputs:
    # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
    if "s16" in checkpoint_url:
        __UpperCamelCase					:Optional[int]        =		torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]     )
    elif "b16" in checkpoint_url:
        __UpperCamelCase					:Optional[int]        =		torch.tensor([[14.2_889, -18.9_045, 11.7_281]]     )
    elif "l16" in checkpoint_url:
        __UpperCamelCase					:List[Any]        =		torch.tensor([[41.5_028, -22.8_681, 45.6_475]]     )
    elif "b4" in checkpoint_url:
        __UpperCamelCase					:int        =		torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]     )
    else:
        __UpperCamelCase					:Optional[Any]        =		torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]     )

    # verify logits
    assert torch.allclose(last_hidden_state[:, 0, :3]  ,      SCREAMING_SNAKE_CASE  ,      atol=1e-4     )

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

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


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--checkpoint_url''',
        default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
        type=str,
        help='''URL of the checkpoint you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    __lowercase										= parser.parse_args()
    convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from __future__ import annotations



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      ):
    '''simple docstring'''







    if (stress, tangential_force, area).count(0     ) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''     )
    elif stress < 0:
        raise ValueError('''Stress cannot be negative'''     )
    elif tangential_force < 0:
        raise ValueError('''Tangential Force cannot be negative'''     )
    elif area < 0:
        raise ValueError('''Area cannot be negative'''     )
    elif stress == 0:
        return (
            "stress",
            tangential_force / area,
        )
    elif tangential_force == 0:
        return (
            "tangential_force",
            stress * area,
        )
    else:
        return (
            "area",
            tangential_force / stress,
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





