


"""simple docstring"""






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 __lowercase  (   lowerCamelCase_	:   str    ,       lowerCamelCase_	:   str=False    ,       lowerCamelCase_	:   Union[str, Any]=False    ,       lowerCamelCase_	:   Optional[int]="data2vec."	):
				SCREAMING_SNAKE_CASE__  				=					"backbone." if is_semantic else ""

				SCREAMING_SNAKE_CASE__  				=					[]
				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 __lowercase  (   lowerCamelCase_	:   List[str]    ,       lowerCamelCase_	:   Any    ,       lowerCamelCase_	:   str=False    ,       lowerCamelCase_	:   Dict=False    ,       lowerCamelCase_	:   List[Any]="data2vec_vision."	):
				for i in range(config.num_hidden_layers	):
								SCREAMING_SNAKE_CASE__  				=					"backbone." if is_semantic else ""
								# queries, keys and values
								SCREAMING_SNAKE_CASE__  				=					state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''	)
								SCREAMING_SNAKE_CASE__  				=					state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''	)
								SCREAMING_SNAKE_CASE__  				=					state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''	)

								SCREAMING_SNAKE_CASE__  				=					in_proj_weight[
								    : config.hidden_size, :
								]
								SCREAMING_SNAKE_CASE__  				=					q_bias
								SCREAMING_SNAKE_CASE__  				=					in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								SCREAMING_SNAKE_CASE__  				=					in_proj_weight[
								    -config.hidden_size :, :
								]
								SCREAMING_SNAKE_CASE__  				=					v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								SCREAMING_SNAKE_CASE__  				=					state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''	)
								SCREAMING_SNAKE_CASE__  				=					state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''	)

								SCREAMING_SNAKE_CASE__  				=					gamma_a
								SCREAMING_SNAKE_CASE__  				=					gamma_a

								# relative_position bias table + index
								if not has_lm_head:
												# each layer has its own relative position bias
												SCREAMING_SNAKE_CASE__  				=					state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''	)
												SCREAMING_SNAKE_CASE__  				=					state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''	)

												SCREAMING_SNAKE_CASE__  				=					table
												SCREAMING_SNAKE_CASE__  				=					index


def __lowercase  (   ):
				SCREAMING_SNAKE_CASE__  				=					argparse.ArgumentParser(
				    "Convert Data2VecVision to HF for image classification and pretraining"    ,       add_help=lowerCamelCase_	)
				parser.add_argument("--hf_checkpoint_name"    ,       type=lowerCamelCase_	)
				parser.add_argument("--input_size"    ,       default=224    ,       type=lowerCamelCase_    ,       help="images input size"	)
				parser.add_argument("--beit_checkpoint"    ,       default=""    ,       help="beit checkpoint"	)

				return parser.parse_args()


def __lowercase  (   lowerCamelCase_	:   Optional[Any]    ,       lowerCamelCase_	:   List[Any]    ,       lowerCamelCase_	:   Tuple	):
				def load_state_dict(lowerCamelCase_	:   List[Any]    ,       lowerCamelCase_	:   List[Any]    ,       lowerCamelCase_	:   Any=""    ,       lowerCamelCase_	:   Dict="relative_position_index"	):
								SCREAMING_SNAKE_CASE__  				=					[]
								SCREAMING_SNAKE_CASE__  				=					[]
								SCREAMING_SNAKE_CASE__  				=					[]
								# copy state_dict so _load_from_state_dict can modify it
								SCREAMING_SNAKE_CASE__  				=					getattr(lowerCamelCase_    ,       "_metadata"    ,       lowerCamelCase_	)
								SCREAMING_SNAKE_CASE__  				=					state_dict.copy()
								if metadata is not None:
												SCREAMING_SNAKE_CASE__  				=					metadata

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

								load(lowerCamelCase_    ,       prefix=lowerCamelCase_	)

								SCREAMING_SNAKE_CASE__  				=					[]
								SCREAMING_SNAKE_CASE__  				=					[]
								for key in missing_keys:
												SCREAMING_SNAKE_CASE__  				=					True
												for ignore_key in ignore_missing.split("|"	):
																if ignore_key in key:
																				SCREAMING_SNAKE_CASE__  				=					False
																				break
												if keep_flag:
																warn_missing_keys.append(lowerCamelCase_	)
												else:
																ignore_missing_keys.append(lowerCamelCase_	)

								SCREAMING_SNAKE_CASE__  				=					warn_missing_keys

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

				SCREAMING_SNAKE_CASE__  				=					{
				    "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,
								    }	)

				SCREAMING_SNAKE_CASE__  				=					create_model(
				    "beit_large_patch16_224" if is_large else "beit_base_patch16_224"    ,       **lowerCamelCase_    ,       )
				SCREAMING_SNAKE_CASE__  				=					model.patch_embed.patch_size
				SCREAMING_SNAKE_CASE__  				=					(args.input_size // patch_size[0], args.input_size // patch_size[1])
				SCREAMING_SNAKE_CASE__  				=					torch.load(args.beit_checkpoint    ,       map_location="cpu"	)

				print(F'''Load ckpt from {args.beit_checkpoint}'''	)
				SCREAMING_SNAKE_CASE__  				=					None
				for model_key in ("model", "module"):
								if model_key in checkpoint:
												SCREAMING_SNAKE_CASE__  				=					checkpoint[model_key]
												print(F'''Load state_dict by model_key = {model_key}'''	)
												break

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

								if "relative_position_bias_table" in key:
												SCREAMING_SNAKE_CASE__  				=					checkpoint_model[key]
												SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					rel_pos_bias.size()
												SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					model.state_dict()[key].size()
												SCREAMING_SNAKE_CASE__  				=					model.patch_embed.patch_shape
												if dst_patch_shape[0] != dst_patch_shape[1]:
																raise NotImplementedError()

				load_state_dict(lowerCamelCase_    ,       lowerCamelCase_    ,       prefix=""	)

				return model


def __lowercase  (   ):
				SCREAMING_SNAKE_CASE__  				=					get_args()

				SCREAMING_SNAKE_CASE__  				=					"ft1k" in args.hf_checkpoint_name
				SCREAMING_SNAKE_CASE__  				=					"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
				SCREAMING_SNAKE_CASE__  				=					DataaVecVisionConfig()
				if is_finetuned:
								SCREAMING_SNAKE_CASE__  				=					True
								SCREAMING_SNAKE_CASE__  				=					False
								SCREAMING_SNAKE_CASE__  				=					True
								SCREAMING_SNAKE_CASE__  				=					1000

								SCREAMING_SNAKE_CASE__  				=					"huggingface/label-files"
								SCREAMING_SNAKE_CASE__  				=					"imagenet-1k-id2label.json"
								SCREAMING_SNAKE_CASE__  				=					json.load(open(hf_hub_download(lowerCamelCase_    ,       lowerCamelCase_    ,       repo_type="dataset"	)    ,       "r"	)	)
								SCREAMING_SNAKE_CASE__  				=					{int(lowerCamelCase_	): v for k, v in idalabel.items()}
								SCREAMING_SNAKE_CASE__  				=					idalabel
								SCREAMING_SNAKE_CASE__  				=					{v: k for k, v in idalabel.items()}
				else:
								SCREAMING_SNAKE_CASE__  				=					False
								SCREAMING_SNAKE_CASE__  				=					True
								SCREAMING_SNAKE_CASE__  				=					False

				if is_large:
								SCREAMING_SNAKE_CASE__  				=					1024
								SCREAMING_SNAKE_CASE__  				=					4096
								SCREAMING_SNAKE_CASE__  				=					24
								SCREAMING_SNAKE_CASE__  				=					16

				# 2. Load Beit model
				SCREAMING_SNAKE_CASE__  				=					load_beit_model(lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_	)
				orig_model.eval()

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

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

				# 4. Load HF Data2VecVision model
				if is_finetuned:
								SCREAMING_SNAKE_CASE__  				=					DataaVecVisionForImageClassification(lowerCamelCase_	)
								hf_model.eval()
								SCREAMING_SNAKE_CASE__  				=					False
								SCREAMING_SNAKE_CASE__  				=					"data2vec_vision."
				else:
								SCREAMING_SNAKE_CASE__  				=					DataaVecVisionModel(lowerCamelCase_	)
								hf_model.eval()
								SCREAMING_SNAKE_CASE__  				=					True
								SCREAMING_SNAKE_CASE__  				=					""

				SCREAMING_SNAKE_CASE__  				=					create_rename_keys(lowerCamelCase_    ,       hf_prefix=lowerCamelCase_    ,       has_lm_head=lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					orig_model.state_dict()
				for src, dest in rename_keys:
								SCREAMING_SNAKE_CASE__  				=					state_dict.pop(lowerCamelCase_	)
								SCREAMING_SNAKE_CASE__  				=					val

				read_in_q_k_v(lowerCamelCase_    ,       lowerCamelCase_    ,       hf_prefix=lowerCamelCase_    ,       has_lm_head=lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					hf_model.load_state_dict(lowerCamelCase_    ,       strict=lowerCamelCase_	)
				print("HF missing"    ,       lowerCamelCase_	)
				print("HF unexpected_keys"    ,       lowerCamelCase_	)

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								SCREAMING_SNAKE_CASE__  				=					hf_model(lowerCamelCase_	)

				SCREAMING_SNAKE_CASE__  				=					hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

				# 6. Compare
				SCREAMING_SNAKE_CASE__  				=					torch.max(torch.abs(hf_output - orig_model_output	)	).item()

				print(F'''max_absolute_diff = {max_absolute_diff}'''	)
				SCREAMING_SNAKE_CASE__  				=					torch.allclose(lowerCamelCase_    ,       lowerCamelCase_    ,       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"




"""simple docstring"""






def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					str(lowerCamelCase_	).strip()
				if not oct_string:
								raise ValueError("Empty string was passed to the function"	)
				SCREAMING_SNAKE_CASE__  				=					oct_string[0] == "-"
				if is_negative:
								SCREAMING_SNAKE_CASE__  				=					oct_string[1:]
				if not oct_string.isdigit() or not all(0 <= int(lowerCamelCase_	) <= 7 for char in oct_string	):
								raise ValueError("Non-octal value was passed to the function"	)
				SCREAMING_SNAKE_CASE__  				=					0
				for char in oct_string:
								SCREAMING_SNAKE_CASE__  				=					8 * decimal_number + int(lowerCamelCase_	)
				if is_negative:
								SCREAMING_SNAKE_CASE__  				=					-decimal_number
				return decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()
