




"""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 					UpperCamelCase__       (						lowercase__					:       str    ,       lowercase__					:       int=False    ,       lowercase__					:       str=False    ,       lowercase__					:       Union[str, Any]="data2vec."				):
		snake_case						:  Tuple        =			"backbone." if is_semantic else ""

		snake_case						:  List[str]        =			[]
		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 					UpperCamelCase__       (						lowercase__					:       int    ,       lowercase__					:       Optional[int]    ,       lowercase__					:       str=False    ,       lowercase__					:       Union[str, Any]=False    ,       lowercase__					:       Optional[Any]="data2vec_vision."				):
		for i in range(config.num_hidden_layers				):
				snake_case						:  Tuple        =			"backbone." if is_semantic else ""
				# queries, keys and values
				snake_case						:  int        =			state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''				)
				snake_case						:  Optional[int]        =			state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''				)
				snake_case						:  int        =			state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''				)

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

				# gamma_1 and gamma_2
				# we call them lambda because otherwise they are renamed when using .from_pretrained
				snake_case						:  Dict        =			state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''				)
				snake_case						:  List[str]        =			state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''				)

				snake_case						:  Union[str, Any]        =			gamma_a
				snake_case						:  Optional[Any]        =			gamma_a

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

						snake_case						:  str        =			table
						snake_case						:  Optional[Any]        =			index



def 					UpperCamelCase__       (						):
		snake_case						:  Union[str, Any]        =			argparse.ArgumentParser(
		    "Convert Data2VecVision to HF for image classification and pretraining"    ,       add_help=lowercase__				)
		parser.add_argument("--hf_checkpoint_name"    ,       type=lowercase__				)
		parser.add_argument("--input_size"    ,       default=224    ,       type=lowercase__    ,       help="images input size"				)
		parser.add_argument("--beit_checkpoint"    ,       default=""    ,       help="beit checkpoint"				)

		return parser.parse_args()



def 					UpperCamelCase__       (						lowercase__					:       Union[str, Any]    ,       lowercase__					:       Optional[int]    ,       lowercase__					:       Tuple				):
		def load_state_dict(lowercase__					:       Optional[Any]    ,       lowercase__					:       Optional[int]    ,       lowercase__					:       Dict=""    ,       lowercase__					:       Optional[Any]="relative_position_index"				):
				snake_case						:  List[str]        =			[]
				snake_case						:  Any        =			[]
				snake_case						:  Any        =			[]
				# copy state_dict so _load_from_state_dict can modify it
				snake_case						:  Union[str, Any]        =			getattr(lowercase__    ,       "_metadata"    ,       lowercase__				)
				snake_case						:  Optional[Any]        =			state_dict.copy()
				if metadata is not None:
						snake_case						:  str        =			metadata

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

				load(lowercase__    ,       prefix=lowercase__				)

				snake_case						:  Dict        =			[]
				snake_case						:  Dict        =			[]
				for key in missing_keys:
						snake_case						:  int        =			True
						for ignore_key in ignore_missing.split("|"				):
								if ignore_key in key:
										snake_case						:  Union[str, Any]        =			False
										break
						if keep_flag:
								warn_missing_keys.append(lowercase__				)
						else:
								ignore_missing_keys.append(lowercase__				)

				snake_case						:  int        =			warn_missing_keys

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

		snake_case						:  Dict        =			{
		    "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": 0.0_01,
				        "use_rel_pos_bias": True,
				    }				)

		snake_case						:  List[Any]        =			create_model(
		    "beit_large_patch16_224" if is_large else "beit_base_patch16_224"    ,       **lowercase__    ,       )
		snake_case						:  Tuple        =			model.patch_embed.patch_size
		snake_case						:  str        =			(args.input_size // patch_size[0], args.input_size // patch_size[1])
		snake_case						:  str        =			torch.load(args.beit_checkpoint    ,       map_location="cpu"				)

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

		snake_case						:  Tuple        =			list(checkpoint_model.keys()				)
		for key in all_keys:
				if "relative_position_index" in key:
						checkpoint_model.pop(lowercase__				)

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

		load_state_dict(lowercase__    ,       lowercase__    ,       prefix=""				)

		return model



def 					UpperCamelCase__       (						):
		snake_case						:  Union[str, Any]        =			get_args()

		snake_case						:  Dict        =			"ft1k" in args.hf_checkpoint_name
		snake_case						:  Optional[int]        =			"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
		snake_case						:  Any        =			DataaVecVisionConfig()
		if is_finetuned:
				snake_case						:  Tuple        =			True
				snake_case						:  List[Any]        =			False
				snake_case						:  Dict        =			True
				snake_case						:  Union[str, Any]        =			1000

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

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

		# 2. Load Beit model
		snake_case						:  List[str]        =			load_beit_model(lowercase__    ,       lowercase__    ,       lowercase__				)
		orig_model.eval()

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

		snake_case						:  int        =			(pixel_values,) if is_finetuned else (pixel_values, None)
		with torch.no_grad():
				snake_case						:  Any        =			orig_model(*lowercase__				)

		# 4. Load HF Data2VecVision model
		if is_finetuned:
				snake_case						:  Dict        =			DataaVecVisionForImageClassification(lowercase__				)
				hf_model.eval()
				snake_case						:  List[Any]        =			False
				snake_case						:  Optional[Any]        =			"data2vec_vision."
		else:
				snake_case						:  Union[str, Any]        =			DataaVecVisionModel(lowercase__				)
				hf_model.eval()
				snake_case						:  Optional[int]        =			True
				snake_case						:  int        =			""

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

		read_in_q_k_v(lowercase__    ,       lowercase__    ,       hf_prefix=lowercase__    ,       has_lm_head=lowercase__				)
		snake_case     ,			snake_case						:  Dict        =			hf_model.load_state_dict(lowercase__    ,       strict=lowercase__				)
		print("HF missing"    ,       lowercase__				)
		print("HF unexpected_keys"    ,       lowercase__				)

		# 5. Forward HF Data2VecVision model
		with torch.no_grad():
				snake_case						:  Dict        =			hf_model(lowercase__				)

		snake_case						:  Optional[int]        =			hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

		# 6. Compare
		snake_case						:  int        =			torch.max(torch.abs(hf_output - orig_model_output				)				).item()

		print(F'''max_absolute_diff = {max_absolute_diff}'''				)
		snake_case						:  Any        =			torch.allclose(lowercase__    ,       lowercase__    ,       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 					UpperCamelCase__       (						lowercase__					:       int				):
		if not isinstance(lowercase__    ,       lowercase__				):
				raise ValueError("double_factorial() only accepts integral values"				)
		if n < 0:
				raise ValueError("double_factorial() not defined for negative values"				)
		return 1 if n <= 1 else n * double_factorial(n - 2				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

