



"""simple docstring"""
import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD

def       _snake_case						(						):
 UpperCAmelCase	:					List[Any]	=	"https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
 UpperCAmelCase	:					Optional[int]	=	Image.open(requests.get(_snake_case			,		stream=_snake_case						).raw						).convert("""RGB"""						)

 return image

def       _snake_case						(						UpperCamelCase						:		List[Any]						):
 UpperCAmelCase	:					str	=	[]
 # fmt: off

 # vision encoder
 rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding""")						)
 rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding""")						)
 rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight""")						)
 rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias""")						)
 rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight""")						)
 rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias""")						)

 for i in range(config.vision_config.num_hidden_layers						):
  rename_keys.append((F"visual_encoder.blocks.{i}.norm1.weight", F"vision_model.encoder.layers.{i}.layer_norm1.weight")						)
  rename_keys.append((F"visual_encoder.blocks.{i}.norm1.bias", F"vision_model.encoder.layers.{i}.layer_norm1.bias")						)
  rename_keys.append((F"visual_encoder.blocks.{i}.norm2.weight", F"vision_model.encoder.layers.{i}.layer_norm2.weight")						)
  rename_keys.append((F"visual_encoder.blocks.{i}.norm2.bias", F"vision_model.encoder.layers.{i}.layer_norm2.bias")						)
  rename_keys.append((F"visual_encoder.blocks.{i}.attn.qkv.weight", F"vision_model.encoder.layers.{i}.self_attn.qkv.weight")						)
  rename_keys.append((F"visual_encoder.blocks.{i}.attn.proj.weight", F"vision_model.encoder.layers.{i}.self_attn.projection.weight",)						)
  rename_keys.append((F"visual_encoder.blocks.{i}.attn.proj.bias", F"vision_model.encoder.layers.{i}.self_attn.projection.bias")						)
  rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc1.weight", F"vision_model.encoder.layers.{i}.mlp.fc1.weight")						)
  rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc1.bias", F"vision_model.encoder.layers.{i}.mlp.fc1.bias")						)
  rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc2.weight", F"vision_model.encoder.layers.{i}.mlp.fc2.weight")						)
  rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc2.bias", F"vision_model.encoder.layers.{i}.mlp.fc2.bias")						)

 # QFormer
 rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.embeddings.layernorm.weight""")						)
 rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.embeddings.layernorm.bias""")						)

 # fmt: on
 return rename_keys

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		int			,		UpperCamelCase						:		str						):
 UpperCAmelCase	:					str	=	dct.pop(_snake_case						)
 UpperCAmelCase	:					Any	=	val

def       _snake_case						(						UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		int						):


 for i in range(config.vision_config.num_hidden_layers						):
  # read in original q and v biases
  UpperCAmelCase	:					Union[str, Any]	=	state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias"						)
  UpperCAmelCase	:					Dict	=	state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias"						)

  # next, set bias in the state dict
  UpperCAmelCase	:					List[Any]	=	torch.cat((q_bias, torch.zeros_like(_snake_case			,		requires_grad=_snake_case						), v_bias)						)
  UpperCAmelCase	:					Any	=	qkv_bias

def       _snake_case						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					int	=	364 if "coco" in model_name else 224
 UpperCAmelCase	:					Dict	=	InstructBlipVisionConfig(image_size=_snake_case						).to_dict()

 # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
 # seems like flan-T5 models don't have bos_token_id properly set?
 if "t5-xl" in model_name:
  UpperCAmelCase	:					Any	=	TaConfig.from_pretrained("""google/flan-t5-xl"""			,		dense_act_fn="""gelu"""			,		bos_token_id=1						).to_dict()
 elif "t5-xxl" in model_name:
  UpperCAmelCase	:					str	=	TaConfig.from_pretrained("""google/flan-t5-xxl"""			,		dense_act_fn="""gelu"""			,		bos_token_id=1						).to_dict()
 elif "vicuna-7b" in model_name:
  UpperCAmelCase	:					Union[str, Any]	=	LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""			,		vocab_size=32001						).to_dict()
 elif "vicuna-13b" in model_name:
  UpperCAmelCase	:					List[Any]	=	LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf"""			,		vocab_size=32001						).to_dict()
 else:
  raise ValueError("""Model name not supported"""						)

 # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
 UpperCAmelCase	:					List[Any]	=	InstructBlipQFormerConfig(vocab_size=30523						).to_dict()
 UpperCAmelCase	:					str	=	InstructBlipConfig(vision_config=_snake_case			,		text_config=_snake_case			,		qformer_config=_snake_case						)

 return config, image_size

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		Any			,		UpperCamelCase						:		Dict=None			,		UpperCamelCase						:		Tuple=False						):
 UpperCAmelCase	:					Any	=	AutoTokenizer.from_pretrained("""bert-base-uncased"""			,		truncation_side="""left"""						)
 qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}						)

 if "t5" in model_name:
  UpperCAmelCase	:					Optional[Any]	=	TaTokenizerFast.from_pretrained("""google/flan-t5-xl"""			,		truncation_side="""left"""						)
 elif "vicuna" in model_name:
  # the following was used in the original implementation:
  # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
  # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
  # tokenizer.add_special_tokens({"bos_token": "</s>"})
  # tokenizer.add_special_tokens({"eos_token": "</s>"})
  # tokenizer.add_special_tokens({"unk_token": "</s>"})
  UpperCAmelCase	:					List[Any]	=	LlamaTokenizerFast.from_pretrained(
      """huggyllama/llama-7b"""			,		truncation_side="""left"""			,		bos_token="""</s>"""			,		unk_token="""</s>"""						)
  tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""}						)

 UpperCAmelCase	:					Dict	=	get_blipa_config(_snake_case						)
 UpperCAmelCase	:					int	=	InstructBlipForConditionalGeneration(_snake_case						).eval()

 UpperCAmelCase	:					Optional[int]	=	{
     "instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
     "instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
     "instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
     "instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
 }

 UpperCAmelCase	:					List[Any]	=	model_name_to_original[model_name]

 # load original model
 print("""Loading original model..."""						)
 UpperCAmelCase	:					str	=	"cuda:1" if torch.cuda.is_available() else "cpu"
 UpperCAmelCase	:					Optional[int]	=	"cuda:2" if torch.cuda.is_available() else "cpu"
 UpperCAmelCase	:					List[str]	=	load_model_and_preprocess(
     name=_snake_case			,		model_type=_snake_case			,		is_eval=_snake_case			,		device=_snake_case						)
 original_model.eval()
 print("""Done!"""						)

 # update state dict keys
 UpperCAmelCase	:					str	=	original_model.state_dict()
 UpperCAmelCase	:					Optional[int]	=	create_rename_keys(_snake_case						)
 for src, dest in rename_keys:
  rename_key(_snake_case			,		_snake_case			,		_snake_case						)

 # some keys can be renamed efficiently
 for key, val in state_dict.copy().items():
  UpperCAmelCase	:					int	=	state_dict.pop(_snake_case						)
  if key.startswith("""Qformer.bert"""						):
   UpperCAmelCase	:					Dict	=	key.replace("""Qformer.bert"""			,		"""qformer"""						)
  if "attention.self" in key:
   UpperCAmelCase	:					str	=	key.replace("""self"""			,		"""attention"""						)
  if "llm_proj" in key:
   UpperCAmelCase	:					int	=	key.replace("""llm_proj"""			,		"""language_projection"""						)
  if "t5_proj" in key:
   UpperCAmelCase	:					Dict	=	key.replace("""t5_proj"""			,		"""language_projection"""						)
  if key.startswith("""llm_model"""						):
   UpperCAmelCase	:					Optional[int]	=	key.replace("""llm_model"""			,		"""language_model"""						)
  if key.startswith("""t5"""						):
   UpperCAmelCase	:					Optional[int]	=	key.replace("""t5"""			,		"""language"""						)
  UpperCAmelCase	:					Union[str, Any]	=	val

 # read in qv biases
 read_in_q_v_bias(_snake_case			,		_snake_case						)

 # note: weights get loaded in torch.float32 by default
 hf_model.load_state_dict(_snake_case			,		strict=_snake_case						)

 UpperCAmelCase	:					Union[str, Any]	=	load_demo_image()
 UpperCAmelCase	:					Tuple	=	"What is unusual about this image?"

 # create processor
 UpperCAmelCase	:					str	=	BlipImageProcessor(
     size={"""height""": image_size, """width""": image_size}			,		image_mean=_snake_case			,		image_std=_snake_case						)
 UpperCAmelCase	:					Tuple	=	InstructBlipProcessor(
     image_processor=_snake_case			,		tokenizer=_snake_case			,		qformer_tokenizer=_snake_case			,		)
 UpperCAmelCase	:					List[str]	=	processor(images=_snake_case			,		text=_snake_case			,		return_tensors="""pt"""						).to(_snake_case						)

 # make sure processor creates exact same pixel values
 UpperCAmelCase	:					List[Any]	=	vis_processors["eval"](_snake_case						).unsqueeze(0						).to(_snake_case						)
 UpperCAmelCase	:					Optional[Any]	=	inputs.pixel_values
 assert torch.allclose(original_pixel_values.to(pixel_values.device						)			,		_snake_case						)

 original_model.to(_snake_case						)
 hf_model.to(_snake_case						)
 with torch.no_grad():
  if "vicuna" in model_name:
   UpperCAmelCase	:					Tuple	=	original_model({"""image""": original_pixel_values, """text_input""": [prompt]}						).logits
   UpperCAmelCase	:					List[Any]	=	hf_model(**_snake_case						).logits
  else:
   UpperCAmelCase	:					int	=	original_model(
       {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}						).logits
   UpperCAmelCase	:					Dict	=	tokenizer("""\n"""			,		return_tensors="""pt"""						).input_ids.to(_snake_case						)
   UpperCAmelCase	:					Optional[Any]	=	label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id			,		-100						)
   UpperCAmelCase	:					Optional[Any]	=	hf_model(**_snake_case			,		labels=_snake_case						).logits

 print("""First values of original logits:"""			,		original_logits[0, :3, :3]						)
 print("""First values of HF logits:"""			,		logits[0, :3, :3]						)

 # assert values
 assert original_logits.shape == logits.shape
 UpperCAmelCase	:					List[str]	=	1e-4 if "vicuna" in model_name else 1e-5
 assert torch.allclose(original_logits.to(logits.device						)			,		_snake_case			,		atol=_snake_case						)
 print("""Looks ok!"""						)

 print("""Generating with original model..."""						)
 UpperCAmelCase	:					Dict	=	original_model.generate({"""image""": original_pixel_values, """prompt""": prompt}			,		num_beams=5						)

 # important: we need to cast the weights of the HF model to the appropriate type
 print("""Generating with HF model..."""						)
 UpperCAmelCase	:					Optional[int]	=	hf_model.generate(
     **_snake_case			,		do_sample=_snake_case			,		num_beams=5			,		max_length=256			,		min_length=1			,		top_p=0.9			,		repetition_penalty=1.5			,		length_penalty=1.0			,		temperature=1			,		)
 if "vicuna" in model_name:
  # convert output id 0 to 2 (eos_token_id)
  # TODO add this in the generate method?
  UpperCAmelCase	:					Tuple	=	2
 print("""Original generation:"""			,		_snake_case						)
 UpperCAmelCase	:					Dict	=	processor.batch_decode(_snake_case			,		skip_special_tokens=_snake_case						)
 UpperCAmelCase	:					List[str]	=	[text.strip() for text in output_text]
 print("""HF generation:"""			,		_snake_case						)

 if pytorch_dump_folder_path is not None:
  processor.save_pretrained(_snake_case						)
  hf_model.save_pretrained(_snake_case						)

 if push_to_hub:
  processor.push_to_hub(F"Salesforce/{model_name}"						)
  hf_model.push_to_hub(F"Salesforce/{model_name}"						)


if __name__ == "__main__":
      A:   Union[str, Any]								=		argparse.ArgumentParser()
      A:   Tuple								=		[
          "instructblip-vicuna-7b",
          "instructblip-vicuna-13b",
          "instructblip-flan-t5-xl",
          "instructblip-flan-t5-xxl",
      ]
      parser.add_argument(
          "--model_name",
          default="instructblip-flan-t5-xl",
          choices=choices,
          type=str,
          help="Path to hf config.json of model to convert",
      )
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether to push the model and processor to the hub after converting",
      )

      A:   Optional[int]								=		parser.parse_args()

      convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


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






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Union[str, Any]=False   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     []

	# 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"
		__magic_name__		:		int	   =     [(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_						(					_snake_case      :  Any      ,   _snake_case      :  Any      ,   _snake_case      :  Dict=False   )				->		int:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     ["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(_snake_case      ,   _snake_case   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  int      ,   _snake_case      :  Union[str, Any]   )				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		int	   =     dct.pop(_snake_case   )
	__magic_name__		:		List[Any]	   =     val






def   lowerCAmelCase_						(					)				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
	__magic_name__		:		List[str]	   =     Image.open(requests.get(_snake_case      ,   stream=_snake_case   ).raw   )
	return im






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Any      ,   _snake_case      :  int=False   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     BitConfig(
	    global_padding="same"      ,   layer_type="bottleneck"      ,   depths=(3, 4, 9)      ,   out_features=["stage3"]      ,   embedding_dynamic_padding=_snake_case      ,   )
	__magic_name__		:		List[str]	   =     ViTHybridConfig(backbone_config=_snake_case      ,   image_size=384      ,   num_labels=1000   )
	__magic_name__		:		str	   =     False

	# load original model from timm
	__magic_name__		:		Union[str, Any]	   =     timm.create_model(_snake_case      ,   pretrained=_snake_case   )
	timm_model.eval()

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

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__magic_name__		:		List[str]	   =     ViTHybridModel(_snake_case   ).eval()
	else:
		__magic_name__		:		str	   =     ViTHybridForImageClassification(_snake_case   ).eval()
	model.load_state_dict(_snake_case   )

	# create image processor
	__magic_name__		:		List[Any]	   =     create_transform(**resolve_data_config({}      ,   model=_snake_case   )   )
	__magic_name__		:		int	   =     transform.transforms

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

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

	__magic_name__		:		List[Any]	   =     prepare_img()
	__magic_name__		:		Any	   =     transform(_snake_case   ).unsqueeze(0   )
	__magic_name__		:		Tuple	   =     processor(_snake_case      ,   return_tensors="pt"   ).pixel_values

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

	# verify logits
	with torch.no_grad():
		__magic_name__		:		Optional[int]	   =     model(_snake_case   )
		__magic_name__		:		List[str]	   =     outputs.logits

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

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

	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__":
				snake_case :		Any							      =  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."
				)

				snake_case :		List[Any]							      =  parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
