




import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)






def  snake_case (							A						,       A=False						,       A=False						,       A="data2vec."      )							->							str:
	lowerCamelCase_							:		str							       =							"backbone." if is_semantic else ""

	lowerCamelCase_							:		Optional[int]							       =							[]
	for i in range(config.num_hidden_layers      ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (f"{prefix}blocks.{i}.norm1.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_before.weight")      )
		rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_before.bias")      )
		rename_keys.append(
		    (f"{prefix}blocks.{i}.attn.proj.weight", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.weight")      )
		rename_keys.append(
		    (f"{prefix}blocks.{i}.attn.proj.bias", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.bias")      )
		rename_keys.append(
		    (f"{prefix}blocks.{i}.norm2.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_after.weight")      )
		rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_after.bias")      )
		rename_keys.append(
		    (f"{prefix}blocks.{i}.mlp.fc1.weight", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.weight")      )
		rename_keys.append(
		    (f"{prefix}blocks.{i}.mlp.fc1.bias", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.bias")      )
		rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"{hf_prefix}encoder.layer.{i}.output.dense.weight")      )
		rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"{hf_prefix}encoder.layer.{i}.output.dense.bias")      )

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        (f"{prefix}cls_token", f"{hf_prefix}embeddings.cls_token"),
	        (f"{prefix}patch_embed.proj.weight", f"{hf_prefix}embeddings.patch_embeddings.projection.weight"),
	        (f"{prefix}patch_embed.proj.bias", f"{hf_prefix}embeddings.patch_embeddings.projection.bias"),
	    ]      )

	if has_lm_head:
		# mask token + shared relative position bias + layernorm
		rename_keys.extend(
		    [
		        ('mask_token', f"{hf_prefix}embeddings.mask_token"),
		        (
		            'rel_pos_bias.relative_position_bias_table',
		            f"{hf_prefix}encoder.relative_position_bias.relative_position_bias_table",
		        ),
		        (
		            'rel_pos_bias.relative_position_index',
		            f"{hf_prefix}encoder.relative_position_bias.relative_position_index",
		        ),
		        ('norm.weight', 'layernorm.weight'),
		        ('norm.bias', 'layernorm.bias'),
		    ]      )
	elif is_semantic:
		# semantic segmentation classification heads
		rename_keys.extend(
		    [
		        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
		        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
		        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
		        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
		    ]      )
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('fc_norm.weight', f"{hf_prefix}pooler.layernorm.weight"),
		        ('fc_norm.bias', f"{hf_prefix}pooler.layernorm.bias"),
		        ('head.weight', 'classifier.weight'),
		        ('head.bias', 'classifier.bias'),
		    ]      )

	return rename_keys






def  snake_case (							A						,       A						,       A=False						,       A=False						,       A="data2vec_vision."      )							->							Union[str, Any]:
	for i in range(config.num_hidden_layers      ):
		lowerCamelCase_							:		Union[str, Any]							       =							"backbone." if is_semantic else ""
		# queries, keys and values
		lowerCamelCase_							:		Union[str, Any]							       =							state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"      )
		lowerCamelCase_							:		Dict							       =							state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"      )
		lowerCamelCase_							:		int							       =							state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"      )

		lowerCamelCase_							:		str							       =							in_proj_weight[
		    : config.hidden_size, :
		]
		lowerCamelCase_							:		Optional[Any]							       =							q_bias
		lowerCamelCase_							:		List[str]							       =							in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowerCamelCase_							:		List[Any]							       =							in_proj_weight[
		    -config.hidden_size :, :
		]
		lowerCamelCase_							:		Any							       =							v_bias

		# gamma_1 and gamma_2
		# we call them lambda because otherwise they are renamed when using .from_pretrained
		lowerCamelCase_							:		List[str]							       =							state_dict.pop(f"{prefix}blocks.{i}.gamma_1"      )
		lowerCamelCase_							:		Optional[Any]							       =							state_dict.pop(f"{prefix}blocks.{i}.gamma_2"      )

		lowerCamelCase_							:		Dict							       =							gamma_a
		lowerCamelCase_							:		Dict							       =							gamma_a

		# relative_position bias table + index
		if not has_lm_head:
			# each layer has its own relative position bias
			lowerCamelCase_							:		Union[str, Any]							       =							state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table"      )
			lowerCamelCase_							:		Any							       =							state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index"      )

			lowerCamelCase_							:		Union[str, Any]							       =							table
			lowerCamelCase_							:		List[str]							       =							index






def  snake_case (							)							->							Optional[int]:
	lowerCamelCase_							:		str							       =							argparse.ArgumentParser(
	    'Convert Data2VecVision to HF for image classification and pretraining'						,       add_help=snake_case__      )
	parser.add_argument('--hf_checkpoint_name'						,       type=snake_case__      )
	parser.add_argument('--input_size'						,       default=224						,       type=snake_case__						,       help='images input size'      )
	parser.add_argument('--beit_checkpoint'						,       default=''						,       help='beit checkpoint'      )

	return parser.parse_args()






def  snake_case (							A						,       A						,       A      )							->							Optional[Any]:
	def load_state_dict(A						,       A						,       A=""						,       A="relative_position_index"      ):
		lowerCamelCase_							:		str							       =							[]
		lowerCamelCase_							:		Optional[int]							       =							[]
		lowerCamelCase_							:		Optional[Any]							       =							[]
		# copy state_dict so _load_from_state_dict can modify it
		lowerCamelCase_							:		int							       =							getattr(snake_case__						,       '_metadata'						,       snake_case__      )
		lowerCamelCase_							:		Optional[Any]							       =							state_dict.copy()
		if metadata is not None:
			lowerCamelCase_							:		Optional[int]							       =							metadata

		def load(A						,       A=""      ):
			lowerCamelCase_							:		Optional[int]							       =							{} if metadata is None else metadata.get(prefix[:-1]						,       {}      )
			module._load_from_state_dict(
			    snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						,       snake_case__      )
			for name, child in module._modules.items():
				if child is not None:
					load(snake_case__						,       prefix + name + '.'      )

		load(snake_case__						,       prefix=snake_case__      )

		lowerCamelCase_							:		Optional[int]							       =							[]
		lowerCamelCase_							:		Optional[Any]							       =							[]
		for key in missing_keys:
			lowerCamelCase_							:		Dict							       =							True
			for ignore_key in ignore_missing.split('|'      ):
				if ignore_key in key:
					lowerCamelCase_							:		Optional[int]							       =							False
					break
			if keep_flag:
				warn_missing_keys.append(snake_case__      )
			else:
				ignore_missing_keys.append(snake_case__      )

		lowerCamelCase_							:		Optional[int]							       =							warn_missing_keys

		if len(snake_case__      ) > 0:
			print(
			    'Weights of {} not initialized from pretrained model: {}'.format(
			        model.__class__.__name__						,       snake_case__      )      )
		if len(snake_case__      ) > 0:
			print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__						,       snake_case__      )      )
		if len(snake_case__      ) > 0:
			print(
			    'Ignored weights of {} not initialized from pretrained model: {}'.format(
			        model.__class__.__name__						,       snake_case__      )      )
		if len(snake_case__      ) > 0:
			print('\n'.join(snake_case__      )      )

	lowerCamelCase_							:		List[str]							       =							{
	    "pretrained": False,
	    "use_shared_rel_pos_bias": True,
	    "use_abs_pos_emb": False,
	    "init_values": 0.1,
	}

	if is_finetuned:
		model_kwargs.update(
		    {
		        'num_classes': 1000,
		        'use_mean_pooling': True,
		        'init_scale': 0.001,
		        'use_rel_pos_bias': True,
		    }      )

	lowerCamelCase_							:		Dict							       =							create_model(
	    'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'						,       **snake_case__						,       )
	lowerCamelCase_							:		Optional[int]							       =							model.patch_embed.patch_size
	lowerCamelCase_							:		List[Any]							       =							(args.input_size // patch_size[0], args.input_size // patch_size[1])
	lowerCamelCase_							:		Union[str, Any]							       =							torch.load(args.beit_checkpoint						,       map_location='cpu'      )

	print(f"Load ckpt from {args.beit_checkpoint}"      )
	lowerCamelCase_							:		Optional[Any]							       =							None
	for model_key in ("model", "module"):
		if model_key in checkpoint:
			lowerCamelCase_							:		int							       =							checkpoint[model_key]
			print(f"Load state_dict by model_key = {model_key}"      )
			break

	lowerCamelCase_							:		Any							       =							list(checkpoint_model.keys()      )
	for key in all_keys:
		if "relative_position_index" in key:
			checkpoint_model.pop(snake_case__      )

		if "relative_position_bias_table" in key:
			lowerCamelCase_							:		List[Any]							       =							checkpoint_model[key]
			lowerCamelCase_							:		List[Any]							       =							rel_pos_bias.size()
			lowerCamelCase_							:		Optional[Any]							       =							model.state_dict()[key].size()
			lowerCamelCase_							:		Optional[int]							       =							model.patch_embed.patch_shape
			if dst_patch_shape[0] != dst_patch_shape[1]:
				raise NotImplementedError()

	load_state_dict(snake_case__						,       snake_case__						,       prefix=''      )

	return model






def  snake_case (							)							->							Optional[Any]:
	lowerCamelCase_							:		Any							       =							get_args()

	lowerCamelCase_							:		str							       =							"ft1k" in args.hf_checkpoint_name
	lowerCamelCase_							:		Tuple							       =							"large" in args.hf_checkpoint_name

	if is_finetuned:
		# To convert Beit's data2vec_vision to HF you need to copy
		# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
		# into this folder.
		import modeling_finetune  # noqa: F401
	else:
		# To convert Beit's data2vec_vision to HF you need to copy
		# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
		# into this folder
		# IMPORTANT: Note that for now we've only converted the down-stream
		# model and not the full pretrained model. This means for the integration
		# test you need to add a `return x` after the following line:
		# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
		# to make the integration test pass.
		import modeling_cyclical  # noqa: F401

	# 1. Create model config
	lowerCamelCase_							:		List[Any]							       =							DataaVecVisionConfig()
	if is_finetuned:
		lowerCamelCase_							:		int							       =							True
		lowerCamelCase_							:		Any							       =							False
		lowerCamelCase_							:		Dict							       =							True
		lowerCamelCase_							:		Optional[int]							       =							1000

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

	if is_large:
		lowerCamelCase_							:		Tuple							       =							1024
		lowerCamelCase_							:		str							       =							4096
		lowerCamelCase_							:		Optional[int]							       =							24
		lowerCamelCase_							:		Any							       =							16

	# 2. Load Beit model
	lowerCamelCase_							:		Dict							       =							load_beit_model(snake_case__						,       snake_case__						,       snake_case__      )
	orig_model.eval()

	# 3. Forward Beit model
	lowerCamelCase_							:		Dict							       =							BeitImageProcessor(size=config.image_size						,       do_center_crop=snake_case__      )
	lowerCamelCase_							:		Tuple							       =							Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'      )
	lowerCamelCase_							:		List[str]							       =							image_processor(images=snake_case__						,       return_tensors='pt'      )
	lowerCamelCase_							:		List[str]							       =							encoding["pixel_values"]

	lowerCamelCase_							:		str							       =							(pixel_values,) if is_finetuned else (pixel_values, None)
	with torch.no_grad():
		lowerCamelCase_							:		int							       =							orig_model(*snake_case__      )

	# 4. Load HF Data2VecVision model
	if is_finetuned:
		lowerCamelCase_							:		Tuple							       =							DataaVecVisionForImageClassification(snake_case__      )
		hf_model.eval()
		lowerCamelCase_							:		List[Any]							       =							False
		lowerCamelCase_							:		List[str]							       =							"data2vec_vision."
	else:
		lowerCamelCase_							:		List[Any]							       =							DataaVecVisionModel(snake_case__      )
		hf_model.eval()
		lowerCamelCase_							:		List[Any]							       =							True
		lowerCamelCase_							:		int							       =							""

	lowerCamelCase_							:		int							       =							create_rename_keys(snake_case__						,       hf_prefix=snake_case__						,       has_lm_head=snake_case__      )
	lowerCamelCase_							:		Any							       =							orig_model.state_dict()
	for src, dest in rename_keys:
		lowerCamelCase_							:		List[Any]							       =							state_dict.pop(snake_case__      )
		lowerCamelCase_							:		List[str]							       =							val

	read_in_q_k_v(snake_case__						,       snake_case__						,       hf_prefix=snake_case__						,       has_lm_head=snake_case__      )
	lowerCamelCase_							:		str							       =							hf_model.load_state_dict(snake_case__						,       strict=snake_case__      )
	print('HF missing'						,       snake_case__      )
	print('HF unexpected_keys'						,       snake_case__      )

	# 5. Forward HF Data2VecVision model
	with torch.no_grad():
		lowerCamelCase_							:		List[Any]							       =							hf_model(snake_case__      )

	lowerCamelCase_							:		Any							       =							hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

	# 6. Compare
	lowerCamelCase_							:		List[Any]							       =							torch.max(torch.abs(hf_output - orig_model_output      )      ).item()

	print(f"max_absolute_diff = {max_absolute_diff}"      )
	lowerCamelCase_							:		List[Any]							       =							torch.allclose(snake_case__						,       snake_case__						,       atol=1e-3      )
	print('Do both models output the same tensors?'						,       '🔥' if success else '💩'      )
	if not success:
		raise Exception('Something went wRoNg'      )

	# 7. Save
	print(f"Saving to {args.hf_checkpoint_name}"      )
	hf_model.save_pretrained(args.hf_checkpoint_name      )
	image_processor.save_pretrained(args.hf_checkpoint_name      )


if __name__ == "__main__":
							main()
							# Run the following to convert checkpoints
							#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
							#          --beit_checkpoint ./pretrained_base.pt \
							#          --hf_checkpoint_name "./data2vec-vision-base"
							#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
							#          --beit_checkpoint ./finetuned_base.pt \
							#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
							#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
							#          --beit_checkpoint ./pretrained_large.pt \
							#          --hf_checkpoint_name "./data2vec-vision-large"
							#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
							#          --beit_checkpoint ./finetuned_large.pt \
							#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"






UpperCamelCase__		=						{
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.0_009_842_073,
    "short-ton": 0.0_011_023_122,
    "pound": 2.2_046_244_202,
    "stone": 0.1_574_731_728,
    "ounce": 35.273_990_723,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.022136652e26,
}

UpperCamelCase__		=						{
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1_016.04_608,
    "short-ton": 907.184,
    "pound": 0.453_592,
    "stone": 6.35_029,
    "ounce": 0.0_283_495,
    "carrat": 0.0_002,
    "atomic-mass-unit": 1.660540199e-27,
}






def  snake_case (							A						,       A						,       A      )							->							float:




	'''simple docstring'''


	if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
		lowerCamelCase_							:		Union[str, Any]							       =							(
		    f"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
		    f"Supported values are: {', '.join(A      )}"
		)
		raise ValueError(A      )
	return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
							import doctest

							doctest.testmod()
