






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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Union[str, Any]  ,						snake_case_      :							List[Any]=False     ):
	snake_case__				:   Dict     =						[]

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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	snake_case__				:   Dict     =						["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(snake_case_  ,						snake_case_     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							int  ,						snake_case_      :							str     ):
	snake_case__				:   str     =						dct.pop(snake_case_     )
	snake_case__				:   int     =						val







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   str     =						"http://images.cocodataset.org/val2017/000000039769.jpg"
	snake_case__				:   str     =						Image.open(requests.get(snake_case_  ,						stream=snake_case_     ).raw     )
	return im







@torch.no_grad()
def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[str]  ,						snake_case_      :							List[str]  ,						snake_case_      :							str=False     ):
	snake_case__				:   Union[str, Any]     =						BitConfig(
	    global_padding="same"  ,						layer_type="bottleneck"  ,						depths=(3, 4, 9)  ,						out_features=["stage3"]  ,						embedding_dynamic_padding=snake_case_  ,						)
	snake_case__				:   Optional[int]     =						ViTHybridConfig(backbone_config=snake_case_  ,						image_size=384  ,						num_labels=1000     )
	snake_case__				:   Optional[int]     =						False

	# load original model from timm
	snake_case__				:   Dict     =						timm.create_model(snake_case_  ,						pretrained=snake_case_     )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	snake_case__				:   str     =						timm_model.state_dict()
	if base_model:
		remove_classification_head_(snake_case_     )
	snake_case__				:   Any     =						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_     )

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		snake_case__				:   str     =						ViTHybridModel(snake_case_     ).eval()
	else:
		snake_case__				:   Any     =						ViTHybridForImageClassification(snake_case_     ).eval()
	model.load_state_dict(snake_case_     )

	# create image processor
	snake_case__				:   int     =						create_transform(**resolve_data_config({}  ,						model=snake_case_     )     )
	snake_case__				:   List[str]     =						transform.transforms

	snake_case__				:   Optional[int]     =						{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	snake_case__				:   str     =						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()  ,						)

	snake_case__				:   Dict     =						prepare_img()
	snake_case__				:   Optional[Any]     =						transform(snake_case_     ).unsqueeze(0     )
	snake_case__				:   Dict     =						processor(snake_case_  ,						return_tensors="pt"     ).pixel_values

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

	# verify logits
	with torch.no_grad():
		snake_case__				:   int     =						model(snake_case_     )
		snake_case__				:   Union[str, Any]     =						outputs.logits

	print("Predicted class:"  ,						logits.argmax(-1     ).item()     )
	if base_model:
		snake_case__				:   Optional[Any]     =						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:
		snake_case__				:   Optional[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__":
	__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)











def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	snake_case__				:   Any     =						[0] * len(snake_case_     )

	for i in range(1  ,						len(snake_case_     )     ):
		# use last results for better performance - dynamic programming
		snake_case__				:   Union[str, Any]     =						prefix_result[i - 1]
		while j > 0 and input_string[i] != input_string[j]:
			snake_case__				:   str     =						prefix_result[j - 1]

		if input_string[i] == input_string[j]:
			j += 1
		snake_case__				:   int     =						j

	return prefix_result







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	return max(prefix_function(snake_case_     )     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



