



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(						__UpperCamelCase							:	str  ,__UpperCamelCase							:	Optional[int]=False  ,__UpperCamelCase							:	Optional[Any]=False  ,__UpperCamelCase							:	List[str]="data2vec."				):
		lowerCAmelCase_    :  List[Any]             =				'''backbone.''' if is_semantic else ''''''

		lowerCAmelCase_    :  Optional[Any]             =				[]
		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(						__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	Optional[Any]=False  ,__UpperCamelCase							:	Tuple=False  ,__UpperCamelCase							:	Tuple="data2vec_vision."				):
		for i in range(config.num_hidden_layers				):
				lowerCAmelCase_    :  Dict             =				'''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_    :  str             =				state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""				)

				lowerCAmelCase_    :  str             =				in_proj_weight[
				    : config.hidden_size, :
				]
				lowerCAmelCase_    :  Any             =				q_bias
				lowerCAmelCase_    :  Any             =				in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase_    :  Any             =				in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCAmelCase_    :  Union[str, Any]             =				v_bias

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

				lowerCAmelCase_    :  Tuple             =				gamma_a
				lowerCAmelCase_    :  Any             =				gamma_a

				# relative_position bias table + index
				if not has_lm_head:
						# each layer has its own relative position bias
						lowerCAmelCase_    :  str             =				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     UpperCamelCase(						):
		lowerCAmelCase_    :  int             =				argparse.ArgumentParser(
		    '''Convert Data2VecVision to HF for image classification and pretraining'''  ,add_help=__UpperCamelCase				)
		parser.add_argument('''--hf_checkpoint_name'''  ,type=__UpperCamelCase				)
		parser.add_argument('''--input_size'''  ,default=224  ,type=__UpperCamelCase  ,help='''images input size'''				)
		parser.add_argument('''--beit_checkpoint'''  ,default=''''''  ,help='''beit checkpoint'''				)

		return parser.parse_args()


def     UpperCamelCase(						__UpperCamelCase							:	Any  ,__UpperCamelCase							:	int  ,__UpperCamelCase							:	Dict				):
		def load_state_dict(__UpperCamelCase							:	Tuple  ,__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	Tuple=""  ,__UpperCamelCase							:	str="relative_position_index"				):
				lowerCAmelCase_    :  int             =				[]
				lowerCAmelCase_    :  Union[str, Any]             =				[]
				lowerCAmelCase_    :  Union[str, Any]             =				[]
				# copy state_dict so _load_from_state_dict can modify it
				lowerCAmelCase_    :  int             =				getattr(__UpperCamelCase  ,'''_metadata'''  ,__UpperCamelCase				)
				lowerCAmelCase_    :  Union[str, Any]             =				state_dict.copy()
				if metadata is not None:
						lowerCAmelCase_    :  Union[str, Any]             =				metadata

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

				load(__UpperCamelCase  ,prefix=__UpperCamelCase				)

				lowerCAmelCase_    :  List[Any]             =				[]
				lowerCAmelCase_    :  int             =				[]
				for key in missing_keys:
						lowerCAmelCase_    :  List[Any]             =				True
						for ignore_key in ignore_missing.split('''|'''				):
								if ignore_key in key:
										lowerCAmelCase_    :  Any             =				False
										break
						if keep_flag:
								warn_missing_keys.append(__UpperCamelCase				)
						else:
								ignore_missing_keys.append(__UpperCamelCase				)

				lowerCAmelCase_    :  Dict             =				warn_missing_keys

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

		lowerCAmelCase_    :  Optional[int]             =				{
		    '''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_0_1,
				        '''use_rel_pos_bias''': True,
				    }				)

		lowerCAmelCase_    :  Union[str, Any]             =				create_model(
		    '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''  ,**__UpperCamelCase  ,)
		lowerCAmelCase_    :  Dict             =				model.patch_embed.patch_size
		lowerCAmelCase_    :  str             =				(args.input_size // patch_size[0], args.input_size // patch_size[1])
		lowerCAmelCase_    :  List[str]             =				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_    :  Optional[Any]             =				checkpoint[model_key]
						print(f"""Load state_dict by model_key = {model_key}"""				)
						break

		lowerCAmelCase_    :  str             =				list(checkpoint_model.keys()				)
		for key in all_keys:
				if "relative_position_index" in key:
						checkpoint_model.pop(__UpperCamelCase				)

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

		load_state_dict(__UpperCamelCase  ,__UpperCamelCase  ,prefix=''''''				)

		return model


def     UpperCamelCase(						):
		lowerCAmelCase_    :  Union[str, Any]             =				get_args()

		lowerCAmelCase_    :  Any             =				'''ft1k''' in args.hf_checkpoint_name
		lowerCAmelCase_    :  List[Any]             =				'''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_    :  int             =				DataaVecVisionConfig()
		if is_finetuned:
				lowerCAmelCase_    :  Dict             =				True
				lowerCAmelCase_    :  int             =				False
				lowerCAmelCase_    :  Any             =				True
				lowerCAmelCase_    :  Optional[int]             =				1000

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

		if is_large:
				lowerCAmelCase_    :  List[Any]             =				1024
				lowerCAmelCase_    :  Union[str, Any]             =				4096
				lowerCAmelCase_    :  Union[str, Any]             =				24
				lowerCAmelCase_    :  Any             =				16

		# 2. Load Beit model
		lowerCAmelCase_    :  int             =				load_beit_model(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase				)
		orig_model.eval()

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

		lowerCAmelCase_    :  Optional[int]             =				(pixel_values,) if is_finetuned else (pixel_values, None)
		with torch.no_grad():
				lowerCAmelCase_    :  Dict             =				orig_model(*__UpperCamelCase				)

		# 4. Load HF Data2VecVision model
		if is_finetuned:
				lowerCAmelCase_    :  Union[str, Any]             =				DataaVecVisionForImageClassification(__UpperCamelCase				)
				hf_model.eval()
				lowerCAmelCase_    :  Any             =				False
				lowerCAmelCase_    :  Dict             =				'''data2vec_vision.'''
		else:
				lowerCAmelCase_    :  Tuple             =				DataaVecVisionModel(__UpperCamelCase				)
				hf_model.eval()
				lowerCAmelCase_    :  str             =				True
				lowerCAmelCase_    :  Any             =				''''''

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

		read_in_q_k_v(__UpperCamelCase  ,__UpperCamelCase  ,hf_prefix=__UpperCamelCase  ,has_lm_head=__UpperCamelCase				)
		lowerCAmelCase_      ,						lowerCAmelCase_    :  List[str]             =				hf_model.load_state_dict(__UpperCamelCase  ,strict=__UpperCamelCase				)
		print('''HF missing'''  ,__UpperCamelCase				)
		print('''HF unexpected_keys'''  ,__UpperCamelCase				)

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

		lowerCAmelCase_    :  str             =				hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

		# 6. Compare
		lowerCAmelCase_    :  Union[str, Any]             =				torch.max(torch.abs(hf_output - orig_model_output				)				).item()

		print(f"""max_absolute_diff = {max_absolute_diff}"""				)
		lowerCAmelCase_    :  List[Any]             =				torch.allclose(__UpperCamelCase  ,__UpperCamelCase  ,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"










from __future__ import annotations

A__						:						Tuple          =      '''Alexander Joslin'''


def     UpperCamelCase(						__UpperCamelCase							:	int				):

		if number <= 0:
				lowerCAmelCase_    :  str             =				f"""n must be greater than 0. Got n = {number}"""
				raise ValueError(__UpperCamelCase				)

		lowerCAmelCase_    :  str             =				[number + 1] * (number + 1)

		# starting position
		lowerCAmelCase_    :  List[Any]             =				0
		for i in range(1  ,__UpperCamelCase				):
				lowerCAmelCase_    :  int             =				min(table[i + 1]  ,table[i] + 1				)
				# check if out of bounds
				if i * 2 <= number:
						lowerCAmelCase_    :  Union[str, Any]             =				min(table[i * 2]  ,table[i] + 1				)
				# check if out of bounds
				if i * 3 <= number:
						lowerCAmelCase_    :  Union[str, Any]             =				min(table[i * 3]  ,table[i] + 1				)
		return table[number]


if __name__ == "__main__":
						import doctest

						doctest.testmod()





