





'''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()
__lowerCAmelCase							:	Any			=logging.get_logger(__name__)





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





def 						UpperCamelCase				(							_lowerCamelCase					:					Dict       ):
    A__				=			["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase						,       _lowerCamelCase       )





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					Dict       ):
    A__				=			dct.pop(_lowerCamelCase       )
    A__				=			val





def 						UpperCamelCase				(							):
    A__				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
    A__				=			Image.open(requests.get(_lowerCamelCase						,       stream=_lowerCamelCase       ).raw       )
    return im





@torch.no_grad()
def 						UpperCamelCase				(							_lowerCamelCase					:					int						,       _lowerCamelCase					:					List[Any]       ):
    A__				=			ViTConfig()
    A__				=			False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        A__				=			True
        A__				=			int(vit_name[-12:-10]       )
        A__				=			int(vit_name[-9:-6]       )
    else:
        A__				=			10_00
        A__				=			"huggingface/label-files"
        A__				=			"imagenet-1k-id2label.json"
        A__				=			json.load(open(hf_hub_download(_lowerCamelCase						,       _lowerCamelCase						,       repo_type="dataset"       )						,       "r"       )       )
        A__				=			{int(_lowerCamelCase       ): v for k, v in idalabel.items()}
        A__				=			idalabel
        A__				=			{v: k for k, v in idalabel.items()}
        A__				=			int(vit_name[-6:-4]       )
        A__				=			int(vit_name[-3:]       )
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith("tiny"       ):
            A__				=			1_92
            A__				=			7_68
            A__				=			12
            A__				=			3
        elif vit_name[9:].startswith("small"       ):
            A__				=			3_84
            A__				=			15_36
            A__				=			12
            A__				=			6
        else:
            pass
    else:
        if vit_name[4:].startswith("small"       ):
            A__				=			7_68
            A__				=			23_04
            A__				=			8
            A__				=			8
        elif vit_name[4:].startswith("base"       ):
            pass
        elif vit_name[4:].startswith("large"       ):
            A__				=			10_24
            A__				=			40_96
            A__				=			24
            A__				=			16
        elif vit_name[4:].startswith("huge"       ):
            A__				=			12_80
            A__				=			51_20
            A__				=			32
            A__				=			16

    # load original model from timm
    A__				=			timm.create_model(_lowerCamelCase						,       pretrained=_lowerCamelCase       )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    A__				=			timm_model.state_dict()
    if base_model:
        remove_classification_head_(_lowerCamelCase       )
    A__				=			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":
        A__				=			ViTModel(_lowerCamelCase       ).eval()
    else:
        A__				=			ViTForImageClassification(_lowerCamelCase       ).eval()
    model.load_state_dict(_lowerCamelCase       )

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

    if base_model:
        A__				=			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:
        A__				=			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__":
  __lowerCAmelCase							:	Optional[int]			=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."
  )

  __lowerCAmelCase							:	Any			=parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)










'''simple docstring'''

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    "stable diffusion controlnet",
    "0.22.0",
    "Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.",
    standard_warn=False,
    stacklevel=3,
)



