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

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

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

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

												a_							:			List[Any]     		=					gamma_a
												a_							:			int     		=					gamma_a

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

																		a_							:			int     		=					table
																		a_							:			Tuple     		=					index

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

						return parser.parse_args()

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

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

												load(_SCREAMING_SNAKE_CASE		,			prefix=_SCREAMING_SNAKE_CASE  )

												a_							:			Tuple     		=					[]
												a_							:			str     		=					[]
												for key in missing_keys:
																		a_							:			Optional[Any]     		=					True
																		for ignore_key in ignore_missing.split("|"  ):
																								if ignore_key in key:
																														a_							:			List[Any]     		=					False
																														break
																		if keep_flag:
																								warn_missing_keys.append(_SCREAMING_SNAKE_CASE  )
																		else:
																								ignore_missing_keys.append(_SCREAMING_SNAKE_CASE  )

												a_							:			str     		=					warn_missing_keys

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

						a_							:			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": 0.001,
												        "use_rel_pos_bias": True,
												    }  )

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

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

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

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

						load_state_dict(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			prefix=""  )

						return model

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

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

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

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

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

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

						a_							:			Any     		=					(pixel_values,) if is_finetuned else (pixel_values, None)
						with torch.no_grad():
												a_							:			str     		=					orig_model(*_SCREAMING_SNAKE_CASE  )

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

						a_							:			Any     		=					create_rename_keys(_SCREAMING_SNAKE_CASE		,			hf_prefix=_SCREAMING_SNAKE_CASE		,			has_lm_head=_SCREAMING_SNAKE_CASE  )
						a_							:			Any     		=					orig_model.state_dict()
						for src, dest in rename_keys:
												a_							:			Optional[Any]     		=					state_dict.pop(_SCREAMING_SNAKE_CASE  )
												a_							:			str     		=					val

						read_in_q_k_v(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			hf_prefix=_SCREAMING_SNAKE_CASE		,			has_lm_head=_SCREAMING_SNAKE_CASE  )
						a_							:			str     		=					hf_model.load_state_dict(_SCREAMING_SNAKE_CASE		,			strict=_SCREAMING_SNAKE_CASE  )
						print("HF missing"		,			_SCREAMING_SNAKE_CASE  )
						print("HF unexpected_keys"		,			_SCREAMING_SNAKE_CASE  )

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

						a_							:			List[str]     		=					hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

						print(F'''max_absolute_diff = {max_absolute_diff}'''  )
						a_							:			List[Any]     		=					torch.allclose(_SCREAMING_SNAKE_CASE		,			_SCREAMING_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"





"""simple docstring"""






from __future__ import annotations

from queue import Queue

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:int  )				-> tuple[int, int]:
						a ^= b
						b ^= a
						a ^= b
						return a, b

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:list[list[int]]  )				-> list[list[int]]:
						a_							:			List[str]     		=					1
						while (1 << j) < max_node:
												for i in range(1		,			max_node + 1  ):
																		a_							:			Union[str, Any]     		=					parent[j - 1][parent[j - 1][i]]
												j += 1
						return parent

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:list[int]		,			_SCREAMING_SNAKE_CASE	:list[list[int]]  )				-> int:
						# u must be deeper in the tree than v
						if level[u] < level[v]:
												a_		,					a_							:			Tuple     		=					swap(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
						# making depth of u same as depth of v
						for i in range(18		,			-1		,			-1  ):
												if level[u] - (1 << i) >= level[v]:
																		a_							:			Union[str, Any]     		=					parent[i][u]
    # at the same depth if u==v that mean lca is found
						if u == v:
												return u
						# moving both nodes upwards till lca in found
						for i in range(18		,			-1		,			-1  ):
												if parent[i][u] not in [0, parent[i][v]]:
																		a_		,					a_							:			Optional[Any]     		=					parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
						return parent[0][u]

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:list[int]		,			_SCREAMING_SNAKE_CASE	:list[list[int]]		,			_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:dict[int, list[int]]		,			_SCREAMING_SNAKE_CASE	:int = 1		,			)				-> tuple[list[int], list[list[int]]]:
						a_							:			List[Any]     		=					0
						a_							:			Queue[int]     		=					Queue(maxsize=_SCREAMING_SNAKE_CASE  )
						q.put(_SCREAMING_SNAKE_CASE  )
						while q.qsize() != 0:
												a_							:			List[str]     		=					q.get()
												for v in graph[u]:
																		if level[v] == -1:
																								a_							:			Tuple     		=					level[u] + 1
																								q.put(_SCREAMING_SNAKE_CASE  )
																								a_							:			Optional[int]     		=					u
						return level, parent

def  lowerCAmelCase_      ()				-> None:
						a_							:			List[Any]     		=					13
						# initializing with 0
						a_							:			Dict     		=					[[0 for _ in range(max_node + 10  )] for _ in range(20  )]
						# initializing with -1 which means every node is unvisited
						a_							:			Optional[int]     		=					[-1 for _ in range(max_node + 10  )]
						a_							:			dict[int, list[int]]     		=					{
						    1: [2, 3, 4],
						    2: [5],
						    3: [6, 7],
						    4: [8],
						    5: [9, 10],
						    6: [11],
						    7: [],
						    8: [12, 13],
						    9: [],
						    10: [],
						    11: [],
						    12: [],
						    13: [],
						}
						a_		,					a_							:			Any     		=					breadth_first_search(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			1  )
						a_							:			Any     		=					create_sparse(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
						print("LCA of node 1 and 3 is: "		,			lowest_common_ancestor(1		,			3		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 5 and 6 is: "		,			lowest_common_ancestor(5		,			6		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 7 and 11 is: "		,			lowest_common_ancestor(7		,			11		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 6 and 7 is: "		,			lowest_common_ancestor(6		,			7		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 4 and 12 is: "		,			lowest_common_ancestor(4		,			12		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 8 and 8 is: "		,			lowest_common_ancestor(8		,			8		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )


if __name__ == "__main__":
				main()




