






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 __A					(   __lowerCAmelCase				,	__lowerCAmelCase=False				,	__lowerCAmelCase=False				,	__lowerCAmelCase="data2vec."			)->						Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase             =    'backbone.' if is_semantic else ''

						_UpperCAmelCase             =    []
						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 __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase=False				,	__lowerCAmelCase=False				,	__lowerCAmelCase="data2vec_vision."			)->						Tuple:





						"""simple docstring"""



						for i in range(config.num_hidden_layers			):
												_UpperCAmelCase             =    'backbone.' if is_semantic else ''
												# queries, keys and values
												_UpperCAmelCase             =    state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""			)
												_UpperCAmelCase             =    state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""			)
												_UpperCAmelCase             =    state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""			)

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

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

												_UpperCAmelCase             =    gamma_a
												_UpperCAmelCase             =    gamma_a

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

																		_UpperCAmelCase             =    table
																		_UpperCAmelCase             =    index


def __A					(   )->						List[Any]:





						"""simple docstring"""



						_UpperCAmelCase             =    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 __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						Tuple:





						"""simple docstring"""



						def load_state_dict(__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase=""				,	__lowerCAmelCase="relative_position_index"			):
												_UpperCAmelCase             =    []
												_UpperCAmelCase             =    []
												_UpperCAmelCase             =    []
												# copy state_dict so _load_from_state_dict can modify it
												_UpperCAmelCase             =    getattr(__lowerCAmelCase				,	'_metadata'				,	__lowerCAmelCase			)
												_UpperCAmelCase             =    state_dict.copy()
												if metadata is not None:
																		_UpperCAmelCase             =    metadata

												def load(__lowerCAmelCase				,	__lowerCAmelCase=""			):
																		_UpperCAmelCase             =    {} 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			)

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

												_UpperCAmelCase             =    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			)			)

						_UpperCAmelCase             =    {
						    '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': 1_000,
												        'use_mean_pooling': True,
												        'init_scale': 0.0_01,
												        'use_rel_pos_bias': True,
												    }			)

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

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

						_UpperCAmelCase             =    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:
																		_UpperCAmelCase             =    checkpoint_model[key]
																		_UpperCAmelCase      ,				_UpperCAmelCase             =    rel_pos_bias.size()
																		_UpperCAmelCase      ,				_UpperCAmelCase             =    model.state_dict()[key].size()
																		_UpperCAmelCase             =    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 __A					(   )->						str:





						"""simple docstring"""



						_UpperCAmelCase             =    get_args()

						_UpperCAmelCase             =    'ft1k' in args.hf_checkpoint_name
						_UpperCAmelCase             =    '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
						_UpperCAmelCase             =    DataaVecVisionConfig()
						if is_finetuned:
												_UpperCAmelCase             =    True
												_UpperCAmelCase             =    False
												_UpperCAmelCase             =    True
												_UpperCAmelCase             =    1_000

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

						if is_large:
												_UpperCAmelCase             =    1_024
												_UpperCAmelCase             =    4_096
												_UpperCAmelCase             =    24
												_UpperCAmelCase             =    16

						# 2. Load Beit model
						_UpperCAmelCase             =    load_beit_model(__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)
						orig_model.eval()

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

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

						# 4. Load HF Data2VecVision model
						if is_finetuned:
												_UpperCAmelCase             =    DataaVecVisionForImageClassification(__lowerCAmelCase			)
												hf_model.eval()
												_UpperCAmelCase             =    False
												_UpperCAmelCase             =    'data2vec_vision.'
						else:
												_UpperCAmelCase             =    DataaVecVisionModel(__lowerCAmelCase			)
												hf_model.eval()
												_UpperCAmelCase             =    True
												_UpperCAmelCase             =    ''

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

						read_in_q_k_v(__lowerCAmelCase				,	__lowerCAmelCase				,	hf_prefix=__lowerCAmelCase				,	has_lm_head=__lowerCAmelCase			)
						_UpperCAmelCase      ,				_UpperCAmelCase             =    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():
												_UpperCAmelCase             =    hf_model(__lowerCAmelCase			)

						_UpperCAmelCase             =    hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

						print(F"""max_absolute_diff = {max_absolute_diff}"""			)
						_UpperCAmelCase             =    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"








from typing import Any





class __lowerCamelCase				:





	"""simple docstring"""






	def __init__(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    []
							_UpperCAmelCase             =    0

	def __str__(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    '<' + str(self.stack					)[1:-1] + '>'
							return printed

	def  UpperCamelCase					(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							self.stack.append(UpperCAmelCase					)
							_UpperCAmelCase             =    self.length + 1

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							self.rotate(1					)
							_UpperCAmelCase             =    self.stack[self.length - 1]
							_UpperCAmelCase             =    self.stack[:-1]
							self.rotate(self.length - 1					)
							_UpperCAmelCase             =    self.length - 1
							return dequeued

	def  UpperCamelCase					(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							for _ in range(UpperCAmelCase					):
													_UpperCAmelCase             =    self.stack[0]
													_UpperCAmelCase             =    self.stack[1:]
													self.put(UpperCAmelCase					)
													_UpperCAmelCase             =    self.length - 1

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    self.get()
							self.put(UpperCAmelCase					)
							self.rotate(self.length - 1					)
							return front

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							return self.length
