




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  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=False    ,SCREAMING_SNAKE_CASE_=False    ,SCREAMING_SNAKE_CASE_="data2vec."    )      ->			Tuple:
							lowercase__  :	Dict        =							"backbone." if is_semantic else ""

							lowercase__  :	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  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=False    ,SCREAMING_SNAKE_CASE_=False    ,SCREAMING_SNAKE_CASE_="data2vec_vision."    )      ->			Any:






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

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

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

														lowercase__  :	Dict        =							gamma_a
														lowercase__  :	str        =							gamma_a

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

																					lowercase__  :	List[str]        =							table
																					lowercase__  :	Optional[int]        =							index
def  snake_case_					(		)      ->			List[str]:
							lowercase__  :	str        =							argparse.ArgumentParser(
							    "Convert Data2VecVision to HF for image classification and pretraining"    ,add_help=snake_case__    )
							parser.add_argument("--hf_checkpoint_name"    ,type=snake_case__    )
							parser.add_argument("--input_size"    ,default=2_24    ,type=snake_case__    ,help="images input size"    )
							parser.add_argument("--beit_checkpoint"    ,default=""    ,help="beit checkpoint"    )

							return parser.parse_args()
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Tuple:






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

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

														load(snake_case__    ,prefix=snake_case__    )

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

														lowercase__  :	Union[str, Any]        =							warn_missing_keys

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

							lowercase__  :	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": 10_00,
														        "use_mean_pooling": True,
														        "init_scale": 0.001,
														        "use_rel_pos_bias": True,
														    }    )

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

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

							lowercase__  :	Union[str, Any]        =							list(checkpoint_model.keys()    )
							for key in all_keys:
														if "relative_position_index" in key:
																					checkpoint_model.pop(snake_case__    )

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

							load_state_dict(snake_case__    ,snake_case__    ,prefix=""    )

							return model
def  snake_case_					(		)      ->			List[str]:
							lowercase__  :	int        =							get_args()

							lowercase__  :	List[str]        =							"ft1k" in args.hf_checkpoint_name
							lowercase__  :	str        =							"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
							lowercase__  :	Dict        =							DataaVecVisionConfig()
							if is_finetuned:
														lowercase__  :	Optional[Any]        =							True
														lowercase__  :	Union[str, Any]        =							False
														lowercase__  :	Optional[Any]        =							True
														lowercase__  :	Optional[int]        =							10_00

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

							if is_large:
														lowercase__  :	Optional[Any]        =							10_24
														lowercase__  :	Optional[int]        =							40_96
														lowercase__  :	Optional[Any]        =							24
														lowercase__  :	Optional[int]        =							16

							# 2. Load Beit model
							lowercase__  :	int        =							load_beit_model(snake_case__    ,snake_case__    ,snake_case__    )
							orig_model.eval()

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

							lowercase__  :	Dict        =							(pixel_values,) if is_finetuned else (pixel_values, None)
							with torch.no_grad():
														lowercase__  :	List[Any]        =							orig_model(*snake_case__    )

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

							lowercase__  :	List[str]        =							create_rename_keys(snake_case__    ,hf_prefix=snake_case__    ,has_lm_head=snake_case__    )
							lowercase__  :	Dict        =							orig_model.state_dict()
							for src, dest in rename_keys:
														lowercase__  :	List[Any]        =							state_dict.pop(snake_case__    )
														lowercase__  :	Tuple        =							val

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

							# 5. Forward HF Data2VecVision model
							with torch.no_grad():
														lowercase__  :	Optional[int]        =							hf_model(snake_case__    )

							lowercase__  :	int        =							hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

							print(F"""max_absolute_diff = {max_absolute_diff}"""    )
							lowercase__  :	int        =							torch.allclose(snake_case__    ,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"





from __future__ import annotations

import math
import random
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			list[int]:
							lowercase__  :	List[Any]        =							d * e - 1
							lowercase__  :	Any        =							0
							lowercase__  :	Dict        =							0
							while p == 0:
														lowercase__  :	Dict        =							random.randint(2    ,n - 1    )
														lowercase__  :	int        =							k
														while True:
																					if t % 2 == 0:
																												lowercase__  :	str        =							t // 2
																												lowercase__  :	int        =							(g**t) % n
																												lowercase__  :	List[Any]        =							math.gcd(x - 1    ,SCREAMING_SNAKE_CASE_    )
																												if x > 1 and y > 1:
																																			lowercase__  :	Any        =							y
																																			lowercase__  :	Optional[int]        =							n // y
																																			break  # find the correct factors
																					else:
																												break  # t is not divisible by 2, break and choose another g
							return sorted([p, q]    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()