


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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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







def 			_lowerCamelCase(      lowercase__  ,					lowercase__=False      )		->      Any:

		'''simple docstring'''







		__lowercase=     []

		# fmt: off
		# stem:
		rename_keys.append(('cls_token', 'vit.embeddings.cls_token')      )
		rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')      )

		rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')      )
		rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')      )

		# backbone
		rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')      )
		rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')      )
		rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')      )

		for stage_idx in range(len(config.backbone_config.depths      )      ):
				for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')      )
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')      )
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')      )
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')      )
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')      )
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')      )
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')      )
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')      )
						rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')      )

				rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')      )
				rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')      )
				rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')      )

		# transformer encoder
		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')      )

		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"
				__lowercase=     [(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'),
				    ]      )
		# fmt: on

		return rename_keys







def 			_lowerCamelCase(      lowercase__  ,					lowercase__  ,					lowercase__=False      )		->      List[Any]:

		'''simple docstring'''







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







def 			_lowerCamelCase(      lowercase__      )		->      Any:

		'''simple docstring'''







		__lowercase=     ["""head.weight""", """head.bias"""]
		for k in ignore_keys:
				state_dict.pop(_lowerCAmelCase  ,					_lowerCAmelCase      )







def 			_lowerCamelCase(      lowercase__  ,					lowercase__  ,					lowercase__      )		->      Any:

		'''simple docstring'''







		__lowercase=     dct.pop(_lowerCAmelCase      )
		__lowercase=     val







def 			_lowerCamelCase(      )		->      List[str]:

		'''simple docstring'''







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







@torch.no_grad()
def 			_lowerCamelCase(      lowercase__  ,					lowercase__  ,					lowercase__=False      )		->      List[str]:

		'''simple docstring'''







		__lowercase=     BitConfig(
		    global_padding='same'  ,					layer_type='bottleneck'  ,					depths=(3, 4, 9)  ,					out_features=['stage3']  ,					embedding_dynamic_padding=_lowerCAmelCase  ,					)
		__lowercase=     ViTHybridConfig(backbone_config=_lowerCAmelCase  ,					image_size=3_8_4  ,					num_labels=1_0_0_0      )
		__lowercase=     False

		# load original model from timm
		__lowercase=     timm.create_model(_lowerCAmelCase  ,					pretrained=_lowerCAmelCase      )
		timm_model.eval()

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

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

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				__lowercase=     ViTHybridModel(_lowerCAmelCase      ).eval()
		else:
				__lowercase=     ViTHybridForImageClassification(_lowerCAmelCase      ).eval()
		model.load_state_dict(_lowerCAmelCase      )

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

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

		__lowercase=     ViTHybridImageProcessor(
		    do_resize=_lowerCAmelCase  ,					size={'shortest_edge': timm_transforms[0].size}  ,					resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,					do_center_crop=_lowerCAmelCase  ,					crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}  ,					do_normalize=_lowerCAmelCase  ,					image_mean=timm_transforms[-1].mean.tolist()  ,					image_std=timm_transforms[-1].std.tolist()  ,					)

		__lowercase=     prepare_img()
		__lowercase=     transform(_lowerCAmelCase      ).unsqueeze(0      )
		__lowercase=     processor(_lowerCAmelCase  ,					return_tensors='pt'      ).pixel_values

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

		# verify logits
		with torch.no_grad():
				__lowercase=     model(_lowerCAmelCase      )
				__lowercase=     outputs.logits

		print('Predicted class:'  ,					logits.argmax(-1      ).item()      )
		if base_model:
				__lowercase=     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:
				__lowercase=     timm_model(_lowerCAmelCase      )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(_lowerCAmelCase  ,					outputs.logits  ,					atol=1E-3      )
		print('Looks ok!'      )

		if pytorch_dump_folder_path is not None:
				Path(_lowerCAmelCase      ).mkdir(exist_ok=_lowerCAmelCase      )
				print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'      )
				model.save_pretrained(_lowerCAmelCase      )
				print(F'Saving processor to {pytorch_dump_folder_path}'      )
				processor.save_pretrained(_lowerCAmelCase      )

		if push_to_hub:
				print(F'Pushing model and processor to the hub {vit_name}'      )
				model.push_to_hub(F'ybelkada/{vit_name}'      )
				processor.push_to_hub(F'ybelkada/{vit_name}'      )


if __name__ == "__main__":
	lowerCAmelCase       			=						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--vit_name''',
	    default='''vit_base_r50_s16_384''',
	    type=str,
	    help='''Name of the hybrid 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.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
	)

	lowerCAmelCase       			=						parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      snake_case__    :       Tuple       		= OmegaConf.load(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= torch.load(_lowerCAmelCase	,  map_location="""cpu"""						)["""model"""]
      snake_case__    :       int       		= list(state_dict.keys()						)

      # extract state_dict for VQVAE
      snake_case__    :       Dict       		= {}
      snake_case__    :       Tuple       		= """first_stage_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

    # extract state_dict for UNetLDM
      snake_case__    :       Optional[Any]       		= {}
      snake_case__    :       Optional[int]       		= """model.diffusion_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

      snake_case__    :       Dict       		= config.model.params.first_stage_config.params
      snake_case__    :       Optional[Any]       		= config.model.params.unet_config.params

      snake_case__    :       Tuple       		= VQModel(**_lowerCAmelCase						).eval()
      vqvae.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= UNetLDMModel(**_lowerCAmelCase						).eval()
      unet.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= DDIMScheduler(
          timesteps=config.model.params.timesteps	,  beta_schedule="""scaled_linear"""	,  beta_start=config.model.params.linear_start	,  beta_end=config.model.params.linear_end	,  clip_sample=_lowerCAmelCase	,  )

      snake_case__    :       List[str]       		= LDMPipeline(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)
      pipeline.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
       __a  			=						argparse.ArgumentParser()
       parser.add_argument("--checkpoint_path", type=str, required=True)
       parser.add_argument("--config_path", type=str, required=True)
       parser.add_argument("--output_path", type=str, required=True)
       __a  			=						parser.parse_args()

       convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
