




"""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 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  :			Any        =       logging.get_logger(__name__)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=False	):




	"""simple docstring"""






	A_  :				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"
		A_  :				str	   =		[(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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=False	):




	"""simple docstring"""






	for i in range(config.num_hidden_layers	):
		if base_model:
			A_  :				int	   =		""
		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_  :				Any	   =		state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""	)
		# next, add query, keys and values (in that order) to the state dict
		A_  :				str	   =		in_proj_weight[
		    : config.hidden_size, :
		]
		A_  :				int	   =		in_proj_bias[: config.hidden_size]
		A_  :				Optional[Any]	   =		in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		A_  :				Any	   =		in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		A_  :				Tuple	   =		in_proj_weight[
		    -config.hidden_size :, :
		]
		A_  :				Any	   =		in_proj_bias[-config.hidden_size :]

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






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

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Any	   =		dct.pop(_lowerCAmelCase	)
	A_  :				Union[str, Any]	   =		val

def 		lowercase_      (		):




	"""simple docstring"""






	A_  :				Optional[int]	   =		"http://images.cocodataset.org/val2017/000000039769.jpg"
	A_  :				str	   =		Image.open(requests.get(_lowerCAmelCase  ,      stream=_lowerCAmelCase	).raw	)
	return im

@torch.no_grad()
def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=False	):




	"""simple docstring"""






	A_  :				Any	   =		BitConfig(
	    global_padding='''same'''  ,      layer_type='''bottleneck'''  ,      depths=(3, 4, 9)  ,      out_features=['''stage3''']  ,      embedding_dynamic_padding=_lowerCAmelCase  ,      )
	A_  :				List[Any]	   =		ViTHybridConfig(backbone_config=_lowerCAmelCase  ,      image_size=384  ,      num_labels=1000	)
	A_  :				Tuple	   =		False

	# load original model from timm
	A_  :				int	   =		timm.create_model(_lowerCAmelCase  ,      pretrained=_lowerCAmelCase	)
	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_(_lowerCAmelCase	)
	A_  :				Any	   =		create_rename_keys(_lowerCAmelCase  ,      _lowerCAmelCase	)
	for src, dest in rename_keys:
		rename_key(_lowerCAmelCase  ,      _lowerCAmelCase  ,      _lowerCAmelCase	)
	read_in_q_k_v(_lowerCAmelCase  ,      _lowerCAmelCase  ,      _lowerCAmelCase	)

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

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

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

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

	A_  :				Tuple	   =		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()  ,      )

	A_  :				Tuple	   =		prepare_img()
	A_  :				Dict	   =		transform(_lowerCAmelCase	).unsqueeze(0	)
	A_  :				Tuple	   =		processor(_lowerCAmelCase  ,      return_tensors='''pt'''	).pixel_values

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

	# verify logits
	with torch.no_grad():
		A_  :				Optional[Any]	   =		model(_lowerCAmelCase	)
		A_  :				str	   =		outputs.logits

	print('''Predicted class:'''  ,      logits.argmax(-1	).item()	)
	if base_model:
		A_  :				Union[str, Any]	   =		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_  :				List[str]	   =		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  :			int        =       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  :			Union[str, Any]        =       parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_lowerCamelCase  :			Any        =       {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Any         =   """luke"""







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					Union[str, Any]=5_02_67      ,     _lowerCamelCase   :					Tuple=50_00_00      ,     _lowerCamelCase   :					int=7_68      ,     _lowerCamelCase   :					Union[str, Any]=2_56      ,     _lowerCamelCase   :					Dict=12      ,     _lowerCamelCase   :					str=12      ,     _lowerCamelCase   :					Dict=30_72      ,     _lowerCamelCase   :					Union[str, Any]="gelu"      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					str=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					int=2      ,     _lowerCamelCase   :					Optional[int]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					List[str]=True      ,     _lowerCamelCase   :					str=None      ,     _lowerCamelCase   :					Union[str, Any]=1      ,     _lowerCamelCase   :					str=0      ,     _lowerCamelCase   :					Dict=2      ,     **_lowerCamelCase   :					Optional[int]      ,     ):



					"""simple docstring"""

					super().__init__(pad_token_id=_lowerCamelCase      ,     bos_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     **_lowerCamelCase       )

					A_  :				str	   =		vocab_size
					A_  :				Optional[Any]	   =		entity_vocab_size
					A_  :				Optional[Any]	   =		hidden_size
					A_  :				List[str]	   =		entity_emb_size
					A_  :				Union[str, Any]	   =		num_hidden_layers
					A_  :				Dict	   =		num_attention_heads
					A_  :				Union[str, Any]	   =		hidden_act
					A_  :				Dict	   =		intermediate_size
					A_  :				int	   =		hidden_dropout_prob
					A_  :				str	   =		attention_probs_dropout_prob
					A_  :				List[Any]	   =		max_position_embeddings
					A_  :				Optional[Any]	   =		type_vocab_size
					A_  :				Optional[int]	   =		initializer_range
					A_  :				str	   =		layer_norm_eps
					A_  :				List[str]	   =		use_entity_aware_attention
					A_  :				Optional[int]	   =		classifier_dropout





