
"""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__ :       Optional[Any]						     =			logging.get_logger(__name__)





def __A						(   a_ :							Any		,		a_ :							str=False     )->							List[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Dict				= []
		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       :     Dict				= [(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 __A						(   a_ :							Any		,		a_ :							Optional[int]		,		a_ :							Any=False     )->							Any:




		'''simple docstring'''






		for i in range(config.num_hidden_layers     ):
				if base_model:
						SCREAMING_SNAKE_CASE       :     List[Any]				= ''''''
				else:
						SCREAMING_SNAKE_CASE       :     List[str]				= '''vit.'''
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				SCREAMING_SNAKE_CASE       :     int				= state_dict.pop(F"blocks.{i}.attn.qkv.weight"     )
				SCREAMING_SNAKE_CASE       :     List[str]				= 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       :     str				= in_proj_weight[
				    : config.hidden_size, :
				]
				SCREAMING_SNAKE_CASE       :     Any				= in_proj_bias[: config.hidden_size]
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				SCREAMING_SNAKE_CASE       :     List[str]				= in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				SCREAMING_SNAKE_CASE       :     Optional[int]				= in_proj_weight[
				    -config.hidden_size :, :
				]
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= in_proj_bias[-config.hidden_size :]





def __A						(   a_ :							Optional[Any]     )->							Optional[int]:




		'''simple docstring'''






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





def __A						(   a_ :							Optional[int]		,		a_ :							Tuple		,		a_ :							List[str]     )->							Optional[int]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= dct.pop(a_     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= val





def __A						(   )->							Dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= Image.open(requests.get(a_		,		stream=a_     ).raw     )
		return im





@torch.no_grad()
def __A						(   a_ :							List[Any]		,		a_ :							Union[str, Any]     )->							Optional[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Dict				= ViTConfig()
		SCREAMING_SNAKE_CASE       :     int				= 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       :     List[str]				= True
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= int(vit_name[-12:-10]     )
				SCREAMING_SNAKE_CASE       :     Any				= int(vit_name[-9:-6]     )
		else:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= 10_00
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= '''huggingface/label-files'''
				SCREAMING_SNAKE_CASE       :     Tuple				= '''imagenet-1k-id2label.json'''
				SCREAMING_SNAKE_CASE       :     Any				= json.load(open(hf_hub_download(a_		,		a_		,		repo_type='''dataset'''     )		,		'''r'''     )     )
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= {int(a_     ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE       :     Optional[int]				= idalabel
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= {v: k for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE       :     List[Any]				= int(vit_name[-6:-4]     )
				SCREAMING_SNAKE_CASE       :     Optional[int]				= int(vit_name[-3:]     )
		# size of the architecture
		if "deit" in vit_name:
				if vit_name[9:].startswith('''tiny'''     ):
						SCREAMING_SNAKE_CASE       :     List[str]				= 1_92
						SCREAMING_SNAKE_CASE       :     str				= 7_68
						SCREAMING_SNAKE_CASE       :     str				= 12
						SCREAMING_SNAKE_CASE       :     str				= 3
				elif vit_name[9:].startswith('''small'''     ):
						SCREAMING_SNAKE_CASE       :     List[Any]				= 3_84
						SCREAMING_SNAKE_CASE       :     Tuple				= 15_36
						SCREAMING_SNAKE_CASE       :     Dict				= 12
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 6
				else:
						pass
		else:
				if vit_name[4:].startswith('''small'''     ):
						SCREAMING_SNAKE_CASE       :     Optional[Any]				= 7_68
						SCREAMING_SNAKE_CASE       :     Tuple				= 23_04
						SCREAMING_SNAKE_CASE       :     str				= 8
						SCREAMING_SNAKE_CASE       :     str				= 8
				elif vit_name[4:].startswith('''base'''     ):
						pass
				elif vit_name[4:].startswith('''large'''     ):
						SCREAMING_SNAKE_CASE       :     Any				= 10_24
						SCREAMING_SNAKE_CASE       :     List[str]				= 40_96
						SCREAMING_SNAKE_CASE       :     int				= 24
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 16
				elif vit_name[4:].startswith('''huge'''     ):
						SCREAMING_SNAKE_CASE       :     Optional[int]				= 12_80
						SCREAMING_SNAKE_CASE       :     List[Any]				= 51_20
						SCREAMING_SNAKE_CASE       :     Tuple				= 32
						SCREAMING_SNAKE_CASE       :     Tuple				= 16

    # load original model from timm
		SCREAMING_SNAKE_CASE       :     Tuple				= timm.create_model(a_		,		pretrained=a_     )
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= timm_model.state_dict()
		if base_model:
				remove_classification_head_(a_     )
		SCREAMING_SNAKE_CASE       :     str				= create_rename_keys(a_		,		a_     )
		for src, dest in rename_keys:
				rename_key(a_		,		a_		,		a_     )
		read_in_q_k_v(a_		,		a_		,		a_     )

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= ViTModel(a_     ).eval()
		else:
				SCREAMING_SNAKE_CASE       :     int				= ViTForImageClassification(a_     ).eval()
		model.load_state_dict(a_     )

		# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
		if "deit" in vit_name:
				SCREAMING_SNAKE_CASE       :     Dict				= DeiTImageProcessor(size=config.image_size     )
		else:
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= ViTImageProcessor(size=config.image_size     )
		SCREAMING_SNAKE_CASE       :     int				= image_processor(images=prepare_img()		,		return_tensors='''pt'''     )
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= encoding['''pixel_values''']
		SCREAMING_SNAKE_CASE       :     Optional[int]				= model(a_     )

		if base_model:
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= timm_model.forward_features(a_     )
				assert timm_pooled_output.shape == outputs.pooler_output.shape
				assert torch.allclose(a_		,		outputs.pooler_output		,		atol=1E-3     )
		else:
				SCREAMING_SNAKE_CASE       :     Any				= timm_model(a_     )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(a_		,		outputs.logits		,		atol=1E-3     )

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


if __name__ == "__main__":
					lowerCamelCase__ :       Union[str, Any]						     =			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"""






import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


lowerCamelCase__ :       List[Any]						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :Union[List[ControlNetModel], Tuple[ControlNetModel]]					)							->			Union[str, Any]:







			'''simple docstring'''




			super().__init__()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= nn.ModuleList(lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :Union[torch.Tensor, float, int]		, lowerCamelCase_    :torch.Tensor		, lowerCamelCase_    :List[torch.tensor]		, lowerCamelCase_    :List[float]		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[Dict[str, Any]] = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :bool = True		, )							->			Union[ControlNetOutput, Tuple]:







			'''simple docstring'''




			for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase_		, lowerCamelCase_		, self.nets					)					):
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= controlnet(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, )

					# merge samples
					if i == 0:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= down_samples, mid_sample
					else:
							SCREAMING_SNAKE_CASE       :     Optional[int]				= [
							    samples_prev + samples_curr
							    for samples_prev, samples_curr in zip(lowerCamelCase_		, lowerCamelCase_					)
							]
							mid_block_res_sample += mid_sample

			return down_block_res_samples, mid_block_res_sample





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Union[str, os.PathLike]		, lowerCamelCase_    :bool = True		, lowerCamelCase_    :Callable = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[str] = None		, )							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0
			SCREAMING_SNAKE_CASE       :     Any				= save_directory
			for controlnet in self.nets:
					controlnet.save_pretrained(
					    lowerCamelCase_		, is_main_process=lowerCamelCase_		, save_function=lowerCamelCase_		, safe_serialization=lowerCamelCase_		, variant=lowerCamelCase_		, )

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_path_to_save + f"_{idx}"







	@classmethod
	def   __lowerCAmelCase (						cls    :Dict		, lowerCamelCase_    :Optional[Union[str, os.PathLike]]		, **lowerCamelCase_    :Tuple					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= 0
			SCREAMING_SNAKE_CASE       :     Optional[int]				= []

			# load controlnet and append to list until no controlnet directory exists anymore
			# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
			# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
			SCREAMING_SNAKE_CASE       :     Dict				= pretrained_model_path
			while os.path.isdir(lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Tuple				= ControlNetModel.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)
					controlnets.append(lowerCamelCase_					)

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= pretrained_model_path + f"_{idx}"

			logger.info(f"{len(lowerCamelCase_					)} controlnets loaded from {pretrained_model_path}."					)

			if len(lowerCamelCase_					) == 0:
					raise ValueError(
					    f"No ControlNets found under {os.path.dirname(lowerCamelCase_					)}. Expected at least {pretrained_model_path + '_0'}."					)

			return cls(lowerCamelCase_					)





