




import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     s.rsplit(_lowercase					,		_lowercase )
     return new.join(_lowercase )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     {}

     UpperCAmelCase_     :				Any  =     ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
     for key, value in state_dict.items():
          for group_key in group_keys:
               if group_key in key:
                    UpperCAmelCase_     :				List[str]  =     key.replace(f'''{group_key}.'''					,		f'''{group_key}.group.''' )

          if "res_path" in key:
               UpperCAmelCase_     :				Tuple  =     key.replace('''res_path.'''					,		'''res_path.path.''' )

          if key.endswith('''.w''' ):
               UpperCAmelCase_     :				Tuple  =     rreplace(_lowercase					,		'''.w'''					,		'''.weight'''					,		1 )
          if key.endswith('''.b''' ):
               UpperCAmelCase_     :				int  =     rreplace(_lowercase					,		'''.b'''					,		'''.bias'''					,		1 )

          UpperCAmelCase_     :				str  =     value.float()

     return upgrade







@torch.no_grad()
def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase=None					,		_lowercase=True ):

     '''simple docstring'''


     from dall_e import Encoder

     UpperCAmelCase_     :				Dict  =     Encoder()
     if os.path.exists(_lowercase ):
          UpperCAmelCase_     :				List[str]  =     torch.load(_lowercase )
     else:
          UpperCAmelCase_     :				Dict  =     torch.hub.load_state_dict_from_url(_lowercase )

     if isinstance(_lowercase					,		_lowercase ):
          UpperCAmelCase_     :				Optional[int]  =     ckpt.state_dict()
     encoder.load_state_dict(_lowercase )

     if config_path is not None:
          UpperCAmelCase_     :				Dict  =     FlavaImageCodebookConfig.from_pretrained(_lowercase )
     else:
          UpperCAmelCase_     :				str  =     FlavaImageCodebookConfig()

     UpperCAmelCase_     :				Optional[Any]  =     FlavaImageCodebook(_lowercase ).eval()
     UpperCAmelCase_     :				Optional[int]  =     encoder.state_dict()

     UpperCAmelCase_     :				Tuple  =     upgrade_state_dict(_lowercase )
     hf_model.load_state_dict(_lowercase )
     UpperCAmelCase_     :				List[str]  =     hf_model.state_dict()
     UpperCAmelCase_     :				List[Any]  =     count_parameters(_lowercase )
     UpperCAmelCase_     :				List[str]  =     count_parameters(_lowercase )

     assert torch.allclose(_lowercase					,		_lowercase					,		atol=1E-3 )

     if save_checkpoint:
          hf_model.save_pretrained(_lowercase )
     else:
          return hf_state_dict


if __name__ == "__main__":
     __a							     =					argparse.ArgumentParser()
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     __a							     =					parser.parse_args()

     convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




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     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=False )	->						Tuple:
				a__	:							int      						= []

				# 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"
								a__	:							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"),
								    ] )
				# fmt: on

				return rename_keys






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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Tuple:
				a__	:							Optional[int]      						= ["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Optional[Any]:
				a__	:							Optional[Any]      						= dct.pop(__UpperCamelCase )
				a__	:							str      						= val






def     SCREAMING_SNAKE_CASE(      )	->						str:
				a__	:							Optional[int]      						= "http://images.cocodataset.org/val2017/000000039769.jpg"
				a__	:							Any      						= Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase ).raw )
				return im






@torch.no_grad()
def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False )	->						Tuple:
				a__	:							Dict      						= BitConfig(
				    global_padding="same"	,				layer_type="bottleneck"	,				depths=(3, 4, 9)	,				out_features=["stage3"]	,				embedding_dynamic_padding=__UpperCamelCase	,				)
				a__	:							List[Any]      						= ViTHybridConfig(backbone_config=__UpperCamelCase	,				image_size=3_84	,				num_labels=10_00 )
				a__	:							Union[str, Any]      						= False

				# load original model from timm
				a__	:							List[Any]      						= timm.create_model(__UpperCamelCase	,				pretrained=__UpperCamelCase )
				timm_model.eval()

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

				a__	:							str      						= "huggingface/label-files"
				a__	:							Optional[Any]      						= "imagenet-1k-id2label.json"
				a__	:							int      						= json.load(open(hf_hub_download(__UpperCamelCase	,				__UpperCamelCase	,				repo_type="dataset" )	,				"r" ) )
				a__	:							Any      						= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				a__	:							Dict      						= idalabel
				a__	:							List[str]      						= {v: k for k, v in idalabel.items()}

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								a__	:							str      						= ViTHybridModel(__UpperCamelCase ).eval()
				else:
								a__	:							Dict      						= ViTHybridForImageClassification(__UpperCamelCase ).eval()
				model.load_state_dict(__UpperCamelCase )

				# create image processor
				a__	:							Union[str, Any]      						= create_transform(**resolve_data_config({}	,				model=__UpperCamelCase ) )
				a__	:							str      						= transform.transforms

				a__	:							List[Any]      						= {
				    "bilinear": PILImageResampling.BILINEAR,
				    "bicubic": PILImageResampling.BICUBIC,
				    "nearest": PILImageResampling.NEAREST,
				}

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

				a__	:							Optional[Any]      						= prepare_img()
				a__	:							List[str]      						= transform(__UpperCamelCase ).unsqueeze(0 )
				a__	:							Optional[Any]      						= processor(__UpperCamelCase	,				return_tensors="pt" ).pixel_values

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

				# verify logits
				with torch.no_grad():
								a__	:							Dict      						= model(__UpperCamelCase )
								a__	:							Optional[Any]      						= outputs.logits

				print("Predicted class:"	,				logits.argmax(-1 ).item() )
				if base_model:
								a__	:							Optional[int]      						= timm_model.forward_features(__UpperCamelCase )
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(__UpperCamelCase	,				outputs.pooler_output	,				atol=1e-3 )
				else:
								a__	:							List[Any]      						= timm_model(__UpperCamelCase )
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(__UpperCamelCase	,				outputs.logits	,				atol=1e-3 )
				print("Looks ok!" )

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

				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)


