





'''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  lowercase__(		_UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : List[Any]=False ,   _UpperCamelCase  : Tuple=False ,   _UpperCamelCase  : List[Any]="data2vec."				)->   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  lowercase__(		_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Optional[int]=False ,   _UpperCamelCase  : Optional[Any]=False ,   _UpperCamelCase  : Optional[Any]="data2vec_vision."				)->   List[str]:






					"""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  lowercase__(		)->   Optional[Any]:






					"""simple docstring"""

					_UpperCamelCase											=   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  lowercase__(		_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : List[str] ,   _UpperCamelCase  : Union[str, Any]				)->   Optional[Any]:






					"""simple docstring"""

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

										def load(_UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Any=""				):
															_UpperCamelCase											=   {} 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				)

										_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(_UpperCamelCase				)
															else:
																				ignore_missing_keys.append(_UpperCamelCase				)

										_UpperCamelCase											=   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				)				)

					_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": 1000,
										        "use_mean_pooling": True,
										        "init_scale": 0.001,
										        "use_rel_pos_bias": True,
										    }				)

					_UpperCamelCase											=   create_model(
					    "beit_large_patch16_224" if is_large else "beit_base_patch16_224" ,   **_UpperCamelCase ,   )
					_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(_UpperCamelCase				)

										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(_UpperCamelCase ,   _UpperCamelCase ,   prefix=""				)

					return model



def  lowercase__(		)->   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											=   1000

										_UpperCamelCase											=   "huggingface/label-files"
										_UpperCamelCase											=   "imagenet-1k-id2label.json"
										_UpperCamelCase											=   json.load(open(hf_hub_download(_UpperCamelCase ,   _UpperCamelCase ,   repo_type="dataset"				) ,   "r"				)				)
										_UpperCamelCase											=   {int(_UpperCamelCase				): 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											=   1024
										_UpperCamelCase											=   4096
										_UpperCamelCase											=   24
										_UpperCamelCase											=   16

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

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

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

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

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

					read_in_q_k_v(_UpperCamelCase ,   _UpperCamelCase ,   hf_prefix=_UpperCamelCase ,   has_lm_head=_UpperCamelCase				)
					_UpperCamelCase				,	_UpperCamelCase											=   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():
										_UpperCamelCase											=   hf_model(_UpperCamelCase				)

					_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(_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"










'''simple docstring'''



from __future__ import annotations



def  lowercase__(		_UpperCamelCase  : list[int]				)->   list[int]:






					"""simple docstring"""

					if not list_of_ints:
										return []

					if min(_UpperCamelCase				) < 0:
										raise ValueError("All numbers must be positive"				)

					_UpperCamelCase											=   max(len(bin(_UpperCamelCase				)[2:]				) for x in list_of_ints				)
					return _msd_radix_sort(_UpperCamelCase ,   _UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : list[int] ,   _UpperCamelCase  : int				)->   list[int]:






					"""simple docstring"""

					if bit_position == 0 or len(_UpperCamelCase				) in [0, 1]:
										return list_of_ints

					_UpperCamelCase											=   []
					_UpperCamelCase											=   []
					# Split numbers based on bit at bit_position from the right
					for number in list_of_ints:
										if (number >> (bit_position - 1)) & 1:
															# number has a one at bit bit_position
															ones.append(_UpperCamelCase				)
										else:
															# number has a zero at bit bit_position
															zeros.append(_UpperCamelCase				)

    # recursively split both lists further
					_UpperCamelCase											=   _msd_radix_sort(_UpperCamelCase ,   bit_position - 1				)
					_UpperCamelCase											=   _msd_radix_sort(_UpperCamelCase ,   bit_position - 1				)

					# recombine lists
					_UpperCamelCase											=   zeros
					res.extend(_UpperCamelCase				)

					return res



def  lowercase__(		_UpperCamelCase  : list[int]				)->   Union[str, Any]:






					"""simple docstring"""

					_UpperCamelCase											=   len(_UpperCamelCase				)
					if not list_of_ints or length == 1:
										return

					if min(_UpperCamelCase				) < 0:
										raise ValueError("All numbers must be positive"				)

					_UpperCamelCase											=   max(len(bin(_UpperCamelCase				)[2:]				) for x in list_of_ints				)
					_msd_radix_sort_inplace(_UpperCamelCase ,   _UpperCamelCase ,   0 ,   _UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : list[int] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int				)->   Tuple:






					"""simple docstring"""

					if bit_position == 0 or end_index - begin_index <= 1:
										return

					bit_position -= 1

					_UpperCamelCase											=   begin_index
					_UpperCamelCase											=   end_index - 1
					while i <= j:
										_UpperCamelCase											=   False
										if not (list_of_ints[i] >> bit_position) & 1:
															# found zero at the beginning
															i += 1
															_UpperCamelCase											=   True
										if (list_of_ints[j] >> bit_position) & 1:
															# found one at the end
															j -= 1
															_UpperCamelCase											=   True

										if changed:
															continue

										_UpperCamelCase				,	_UpperCamelCase											=   list_of_ints[j], list_of_ints[i]
										j -= 1
										if j != i:
															i += 1

					_msd_radix_sort_inplace(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)
					_msd_radix_sort_inplace(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



