





'''simple docstring'''

import argparse
import json
from pathlib import Path

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

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_					= logging.get_logger(__name__)





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Union[str, Any]=False    )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE									=			[]
    for i in range(config.num_hidden_layers    ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight')    )
        rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias')    )
        rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight')    )
        rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias')    )
        rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight')    )
        rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias')    )
        rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight')    )
        rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias')    )
        rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight')    )
        rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias')    )

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

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ("""norm.weight""", """layernorm.weight"""),
                ("""norm.bias""", """layernorm.bias"""),
                ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
            ]    )

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        _SCREAMING_SNAKE_CASE									=			[(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						(     __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Tuple=False    )						->str:
    for i in range(config.num_hidden_layers    ):
        if base_model:
            _SCREAMING_SNAKE_CASE									=			""""""
        else:
            _SCREAMING_SNAKE_CASE									=			"""vit."""
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        _SCREAMING_SNAKE_CASE									=			state_dict.pop(F'blocks.{i}.attn.qkv.weight'    )
        _SCREAMING_SNAKE_CASE									=			state_dict.pop(F'blocks.{i}.attn.qkv.bias'    )
        # next, add query, keys and values (in that order) to the state dict
        _SCREAMING_SNAKE_CASE									=			in_proj_weight[
            : config.hidden_size, :
        ]
        _SCREAMING_SNAKE_CASE									=			in_proj_bias[: config.hidden_size]
        _SCREAMING_SNAKE_CASE									=			in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        _SCREAMING_SNAKE_CASE									=			in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        _SCREAMING_SNAKE_CASE									=			in_proj_weight[
            -config.hidden_size :, :
        ]
        _SCREAMING_SNAKE_CASE									=			in_proj_bias[-config.hidden_size :]





def lowerCamelCase						(     __lowerCamelCase :    List[str]    )						->List[str]:
    _SCREAMING_SNAKE_CASE									=			["""head.weight""", """head.bias"""]
    for k in ignore_keys:
        state_dict.pop(__lowerCamelCase					,       __lowerCamelCase    )





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Dict					,       __lowerCamelCase :    str    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			dct.pop(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			val





def lowerCamelCase						(     )						->Optional[int]:
    _SCREAMING_SNAKE_CASE									=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _SCREAMING_SNAKE_CASE									=			Image.open(requests.get(__lowerCamelCase					,       stream=__lowerCamelCase    ).raw    )
    return im





@torch.no_grad()
def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Union[str, Any]    )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE									=			ViTConfig()
    _SCREAMING_SNAKE_CASE									=			False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        _SCREAMING_SNAKE_CASE									=			True
        _SCREAMING_SNAKE_CASE									=			int(vit_name[-12:-10]    )
        _SCREAMING_SNAKE_CASE									=			int(vit_name[-9:-6]    )
    else:
        _SCREAMING_SNAKE_CASE									=			1000
        _SCREAMING_SNAKE_CASE									=			"""huggingface/label-files"""
        _SCREAMING_SNAKE_CASE									=			"""imagenet-1k-id2label.json"""
        _SCREAMING_SNAKE_CASE									=			json.load(open(hf_hub_download(__lowerCamelCase					,       __lowerCamelCase					,       repo_type="""dataset"""    )					,       """r"""    )    )
        _SCREAMING_SNAKE_CASE									=			{int(__lowerCamelCase    ): v for k, v in idalabel.items()}
        _SCREAMING_SNAKE_CASE									=			idalabel
        _SCREAMING_SNAKE_CASE									=			{v: k for k, v in idalabel.items()}
        _SCREAMING_SNAKE_CASE									=			int(vit_name[-6:-4]    )
        _SCREAMING_SNAKE_CASE									=			int(vit_name[-3:]    )
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith("""tiny"""    ):
            _SCREAMING_SNAKE_CASE									=			192
            _SCREAMING_SNAKE_CASE									=			768
            _SCREAMING_SNAKE_CASE									=			12
            _SCREAMING_SNAKE_CASE									=			3
        elif vit_name[9:].startswith("""small"""    ):
            _SCREAMING_SNAKE_CASE									=			384
            _SCREAMING_SNAKE_CASE									=			1536
            _SCREAMING_SNAKE_CASE									=			12
            _SCREAMING_SNAKE_CASE									=			6
        else:
            pass
    else:
        if vit_name[4:].startswith("""small"""    ):
            _SCREAMING_SNAKE_CASE									=			768
            _SCREAMING_SNAKE_CASE									=			2304
            _SCREAMING_SNAKE_CASE									=			8
            _SCREAMING_SNAKE_CASE									=			8
        elif vit_name[4:].startswith("""base"""    ):
            pass
        elif vit_name[4:].startswith("""large"""    ):
            _SCREAMING_SNAKE_CASE									=			1024
            _SCREAMING_SNAKE_CASE									=			4096
            _SCREAMING_SNAKE_CASE									=			24
            _SCREAMING_SNAKE_CASE									=			16
        elif vit_name[4:].startswith("""huge"""    ):
            _SCREAMING_SNAKE_CASE									=			1280
            _SCREAMING_SNAKE_CASE									=			5120
            _SCREAMING_SNAKE_CASE									=			32
            _SCREAMING_SNAKE_CASE									=			16

    # load original model from timm
    _SCREAMING_SNAKE_CASE									=			timm.create_model(__lowerCamelCase					,       pretrained=__lowerCamelCase    )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    _SCREAMING_SNAKE_CASE									=			timm_model.state_dict()
    if base_model:
        remove_classification_head_(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			create_rename_keys(__lowerCamelCase					,       __lowerCamelCase    )
    for src, dest in rename_keys:
        rename_key(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    read_in_q_k_v(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        _SCREAMING_SNAKE_CASE									=			ViTModel(__lowerCamelCase    ).eval()
    else:
        _SCREAMING_SNAKE_CASE									=			ViTForImageClassification(__lowerCamelCase    ).eval()
    model.load_state_dict(__lowerCamelCase    )

    # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
    if "deit" in vit_name:
        _SCREAMING_SNAKE_CASE									=			DeiTImageProcessor(size=config.image_size    )
    else:
        _SCREAMING_SNAKE_CASE									=			ViTImageProcessor(size=config.image_size    )
    _SCREAMING_SNAKE_CASE									=			image_processor(images=prepare_img()					,       return_tensors="""pt"""    )
    _SCREAMING_SNAKE_CASE									=			encoding["""pixel_values"""]
    _SCREAMING_SNAKE_CASE									=			model(__lowerCamelCase    )

    if base_model:
        _SCREAMING_SNAKE_CASE									=			timm_model.forward_features(__lowerCamelCase    )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(__lowerCamelCase					,       outputs.pooler_output					,       atol=1e-3    )
    else:
        _SCREAMING_SNAKE_CASE									=			timm_model(__lowerCamelCase    )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(__lowerCamelCase					,       outputs.logits					,       atol=1e-3    )

    Path(__lowerCamelCase    ).mkdir(exist_ok=__lowerCamelCase    )
    print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'    )
    model.save_pretrained(__lowerCamelCase    )
    print(F'Saving image processor to {pytorch_dump_folder_path}'    )
    image_processor.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--vit_name""",
          default="""vit_base_patch16_224""",
          type=str,
          help="""Name of the ViT 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."""
      )

      lowercase_					= parser.parse_args()
      convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)










'''simple docstring'''

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowercase_					= HUGGINGFACE_HUB_CACHE


lowercase_					= """config.json"""
lowercase_					= """diffusion_pytorch_model.bin"""
lowercase_					= """diffusion_flax_model.msgpack"""
lowercase_					= """model.onnx"""
lowercase_					= """diffusion_pytorch_model.safetensors"""
lowercase_					= """weights.pb"""
lowercase_					= """https://huggingface.co"""
lowercase_					= default_cache_path
lowercase_					= """diffusers_modules"""
lowercase_					= os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowercase_					= ["""fp16""", """non-ema"""]
lowercase_					= """.self_attn"""



