

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()
A_ :				Tuple						 =    logging.get_logger(__name__)

def 		UpperCamelCase      (lowercase_:				Dict					,   lowercase_:				Tuple=False      )  ->   str:
	A__							:					Optional[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__							:					Tuple       =	[(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 		UpperCamelCase      (lowercase_:				Dict					,   lowercase_:				Tuple					,   lowercase_:				Optional[int]=False      )  ->   str:
	for i in range(config.num_hidden_layers      ):
		if base_model:
			A__							:					Optional[int]       =	""""""
		else:
			A__							:					List[str]       =	"""vit."""
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		A__							:					Tuple       =	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__							:					str       =	in_proj_weight[
		    : config.hidden_size, :
		]
		A__							:					Any       =	in_proj_bias[: config.hidden_size]
		A__							:					str       =	in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		A__							:					Any       =	in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		A__							:					str       =	in_proj_weight[
		    -config.hidden_size :, :
		]
		A__							:					Tuple       =	in_proj_bias[-config.hidden_size :]

def 		UpperCamelCase      (lowercase_:				List[Any]      )  ->   Optional[Any]:
	A__							:					int       =	["""head.weight""", """head.bias"""]
	for k in ignore_keys:
		state_dict.pop(lowercase_					,   lowercase_      )

def 		UpperCamelCase      (lowercase_:				int					,   lowercase_:				Any					,   lowercase_:				str      )  ->   Dict:
	A__							:					Optional[Any]       =	dct.pop(lowercase_      )
	A__							:					Tuple       =	val

def 		UpperCamelCase      ()  ->   Tuple:
	A__							:					List[str]       =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A__							:					str       =	Image.open(requests.get(lowercase_					,   stream=lowercase_      ).raw      )
	return im

@torch.no_grad()
def 		UpperCamelCase      (lowercase_:				Union[str, Any]					,   lowercase_:				Dict					,   lowercase_:				str=False      )  ->   str:
	A__							:					Dict       =	BitConfig(
	    global_padding="""same"""					,   layer_type="""bottleneck"""					,   depths=(3, 4, 9)					,   out_features=["""stage3"""]					,   embedding_dynamic_padding=lowercase_					,   )
	A__							:					List[Any]       =	ViTHybridConfig(backbone_config=lowercase_					,   image_size=384					,   num_labels=1000      )
	A__							:					Any       =	False

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

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

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		A__							:					Union[str, Any]       =	ViTHybridModel(lowercase_      ).eval()
	else:
		A__							:					Dict       =	ViTHybridForImageClassification(lowercase_      ).eval()
	model.load_state_dict(lowercase_      )

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

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

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

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

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

	# verify logits
	with torch.no_grad():
		A__							:					Optional[int]       =	model(lowercase_      )
		A__							:					str       =	outputs.logits

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

	if pytorch_dump_folder_path is not None:
		Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
		print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""      )
		model.save_pretrained(lowercase_      )
		print(f"""Saving processor to {pytorch_dump_folder_path}"""      )
		processor.save_pretrained(lowercase_      )

	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__":
	A_ :				Tuple						 =    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.'
	)

	A_ :				int						 =    parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	if not isinstance(lowercase_					,   lowercase_      ):
		raise TypeError("""Input value must be an 'int' type"""      )

	A__							:					int       =	0
	while number:
		position += 1
		number >>= 1

	return position


if __name__ == "__main__":
	import doctest

	doctest.testmod()



