



"""simple docstring"""
def        A_  (      _lowercase     ):







			'''simple docstring'''


			if not isinstance(_lowercase, _lowercase     ):
						snake_case_			:str						=      f"""Input value of [number={number}] must be an integer"""
						raise TypeError(_lowercase     )
			if number < 0 or number % 2 == 1:
						return False
			snake_case_			:Optional[int]						=      int(number**0.5     )
			return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





"""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        A_  (      _lowercase, _lowercase=False, _lowercase=False, _lowercase="data2vec."     ):







						'''simple docstring'''


						snake_case_			:List[str]						=      """backbone.""" if is_semantic else """"""

						snake_case_			:Tuple						=      []
						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_  (      _lowercase, _lowercase, _lowercase=False, _lowercase=False, _lowercase="data2vec_vision."     ):







						'''simple docstring'''


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

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

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

												snake_case_			:str						=      gamma_a
												snake_case_			:Tuple						=      gamma_a

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

																		snake_case_			:Tuple						=      table
																		snake_case_			:List[Any]						=      index
def        A_  (      ):







						'''simple docstring'''


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

						return parser.parse_args()
def        A_  (      _lowercase, _lowercase, _lowercase     ):







						'''simple docstring'''


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

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

												load(_lowercase, prefix=_lowercase     )

												snake_case_			:Dict						=      []
												snake_case_			:Optional[int]						=      []
												for key in missing_keys:
																		snake_case_			:Dict						=      True
																		for ignore_key in ignore_missing.split("""|"""     ):
																								if ignore_key in key:
																														snake_case_			:int						=      False
																														break
																		if keep_flag:
																								warn_missing_keys.append(_lowercase     )
																		else:
																								ignore_missing_keys.append(_lowercase     )

												snake_case_			:int						=      warn_missing_keys

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

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

						snake_case_			:int						=      create_model(
						    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224""", **_lowercase, )
						snake_case_			:Tuple						=      model.patch_embed.patch_size
						snake_case_			:Optional[int]						=      (args.input_size // patch_size[0], args.input_size // patch_size[1])
						snake_case_			:Tuple						=      torch.load(args.beit_checkpoint, map_location="""cpu"""     )

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

						snake_case_			:Dict						=      list(checkpoint_model.keys()     )
						for key in all_keys:
												if "relative_position_index" in key:
																		checkpoint_model.pop(_lowercase     )

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

						load_state_dict(_lowercase, _lowercase, prefix=""""""     )

						return model
def        A_  (      ):







						'''simple docstring'''


						snake_case_			:int						=      get_args()

						snake_case_			:str						=      """ft1k""" in args.hf_checkpoint_name
						snake_case_			:Optional[int]						=      """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
						snake_case_			:Dict						=      DataaVecVisionConfig()
						if is_finetuned:
												snake_case_			:Optional[int]						=      True
												snake_case_			:List[str]						=      False
												snake_case_			:Dict						=      True
												snake_case_			:Union[str, Any]						=      1000

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

						if is_large:
												snake_case_			:Dict						=      1024
												snake_case_			:Union[str, Any]						=      4096
												snake_case_			:Optional[int]						=      24
												snake_case_			:Dict						=      16

						# 2. Load Beit model
						snake_case_			:Optional[Any]						=      load_beit_model(_lowercase, _lowercase, _lowercase     )
						orig_model.eval()

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

						snake_case_			:List[Any]						=      (pixel_values,) if is_finetuned else (pixel_values, None)
						with torch.no_grad():
												snake_case_			:str						=      orig_model(*_lowercase     )

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

						snake_case_			:int						=      create_rename_keys(_lowercase, hf_prefix=_lowercase, has_lm_head=_lowercase     )
						snake_case_			:int						=      orig_model.state_dict()
						for src, dest in rename_keys:
												snake_case_			:Optional[int]						=      state_dict.pop(_lowercase     )
												snake_case_			:Union[str, Any]						=      val

						read_in_q_k_v(_lowercase, _lowercase, hf_prefix=_lowercase, has_lm_head=_lowercase     )
						snake_case_,	snake_case_			:List[Any]						=      hf_model.load_state_dict(_lowercase, strict=_lowercase     )
						print("""HF missing""", _lowercase     )
						print("""HF unexpected_keys""", _lowercase     )

						# 5. Forward HF Data2VecVision model
						with torch.no_grad():
												snake_case_			:Any						=      hf_model(_lowercase     )

						snake_case_			:Any						=      hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

						# 6. Compare
						snake_case_			:str						=      torch.max(torch.abs(hf_output - orig_model_output     )     ).item()

						print(f"""max_absolute_diff = {max_absolute_diff}"""     )
						snake_case_			:str						=      torch.allclose(_lowercase, _lowercase, 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"
