




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 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase=False,				__UpperCamelCase=False,				__UpperCamelCase="data2vec."	):
    SCREAMING_SNAKE_CASE__								="""backbone.""" if is_semantic else """"""

    SCREAMING_SNAKE_CASE__								=[]
    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 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase=False,				__UpperCamelCase=False,				__UpperCamelCase="data2vec_vision."	):
    for i in range(config.num_hidden_layers	):
        SCREAMING_SNAKE_CASE__								="""backbone.""" if is_semantic else """"""
        # queries, keys and values
        SCREAMING_SNAKE_CASE__								=state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""	)
        SCREAMING_SNAKE_CASE__								=state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""	)
        SCREAMING_SNAKE_CASE__								=state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""	)

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

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

        SCREAMING_SNAKE_CASE__								=gamma_a
        SCREAMING_SNAKE_CASE__								=gamma_a

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

            SCREAMING_SNAKE_CASE__								=table
            SCREAMING_SNAKE_CASE__								=index



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

        def load(__UpperCamelCase,				__UpperCamelCase=""	):
            SCREAMING_SNAKE_CASE__								={} 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	)

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

        SCREAMING_SNAKE_CASE__								=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	)	)

    SCREAMING_SNAKE_CASE__								={
        """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""": 0.001,
                """use_rel_pos_bias""": True,
            }	)

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

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

    SCREAMING_SNAKE_CASE__								=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:
            SCREAMING_SNAKE_CASE__								=checkpoint_model[key]
            SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=rel_pos_bias.size()
            SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=model.state_dict()[key].size()
            SCREAMING_SNAKE_CASE__								=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 	UpperCAmelCase_    (	):
    SCREAMING_SNAKE_CASE__								=get_args()

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

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

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

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

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

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

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

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

    read_in_q_k_v(__UpperCamelCase,				__UpperCamelCase,				hf_prefix=__UpperCamelCase,				has_lm_head=__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=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():
        SCREAMING_SNAKE_CASE__								=hf_model(__UpperCamelCase	)

    SCREAMING_SNAKE_CASE__								=hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

    print(f"""max_absolute_diff = {max_absolute_diff}"""	)
    SCREAMING_SNAKE_CASE__								=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"












from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCamelCase_  						=   TypeVar("T", bound=bool)
class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Union[str, Any]				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=value
         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=None




     @property
     def   __A				(	self			: Optional[Any]						)		->   T:


         '''simple docstring'''





         return self._value




     @staticmethod
     def   __A				(	_UpperCamelCase			: RandomizedHeapNode[T] | None				,_UpperCamelCase			: RandomizedHeapNode[T] | None						)		->   RandomizedHeapNode[T] | None:


         '''simple docstring'''





         if not roota:
             return roota

         if not roota:
             return roota

         if roota.value > roota.value:
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=roota, roota

         if random.choice([True, False]						):
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=roota.right, roota.left

         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(roota.left				,_UpperCamelCase						)

         return roota






class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Any				,_UpperCamelCase			: Iterable[T] | None = ()						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None

         if data:
             for item in data:
                 self.insert(_UpperCamelCase						)




     def   __A				(	self			: Union[str, Any]				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(self._root				,RandomizedHeapNode(_UpperCamelCase						)						)




     def   __A				(	self			: Dict						)		->   T | None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.top()

         if self._root is None:
             return None

         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(self._root.left				,self._root.right						)

         return result




     def   __A				(	self			: Optional[Any]						)		->   T:


         '''simple docstring'''





         if not self._root:
             raise IndexError("""Can't get top element for the empty heap."""						)
         return self._root.value




     def   __A				(	self			: Optional[int]						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None




     def   __A				(	self			: List[Any]						)		->   list[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=[]
         while self:
             result.append(self.pop()						)

         return result




     def __bool__(	self			: Tuple						)		->   bool:


         '''simple docstring'''





         return self._root is not None


if __name__ == "__main__":
    import doctest

    doctest.testmod()






