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       :					int		,							__SCREAMING_SNAKE_CASE       :					Dict=False		,							__SCREAMING_SNAKE_CASE       :					Optional[int]=False		,							__SCREAMING_SNAKE_CASE       :					Optional[int]="data2vec."   ):







    '''simple docstring'''
    __snake_case       :				List[Any] 			=    '''backbone.''' if is_semantic else ''''''

    __snake_case       :				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 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Any		,							__SCREAMING_SNAKE_CASE       :					Optional[Any]		,							__SCREAMING_SNAKE_CASE       :					List[str]=False		,							__SCREAMING_SNAKE_CASE       :					List[str]=False		,							__SCREAMING_SNAKE_CASE       :					Optional[int]="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       :				int 			=    state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''   )
        __snake_case       :				Tuple 			=    state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''   )
        __snake_case       :				str 			=    state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''   )

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

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        __snake_case       :				str 			=    state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''   )
        __snake_case       :				Optional[int] 			=    state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''   )

        __snake_case       :				Optional[int] 			=    gamma_a
        __snake_case       :				str 			=    gamma_a

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

            __snake_case       :				int 			=    table
            __snake_case       :				Dict 			=    index







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    __snake_case       :				List[Any] 			=    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=2_2_4		,							type=_UpperCamelCase		,							help="""images input size"""   )
    parser.add_argument("""--beit_checkpoint"""		,							default=""""""		,							help="""beit checkpoint"""   )

    return parser.parse_args()







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Dict		,							__SCREAMING_SNAKE_CASE       :					Any		,							__SCREAMING_SNAKE_CASE       :					Any   ):







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

        def load(__SCREAMING_SNAKE_CASE       :					str		,							__SCREAMING_SNAKE_CASE       :					Any=""   ):
            __snake_case       :				Optional[int] 			=    {} 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   )

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

        __snake_case       :				List[Any] 			=    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   )   )

    __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""": 1_0_0_0,
                """use_mean_pooling""": True,
                """init_scale""": 0.0_01,
                """use_rel_pos_bias""": True,
            }   )

    __snake_case       :				List[Any] 			=    create_model(
        """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""		,							**_UpperCamelCase		,							)
    __snake_case       :				Union[str, Any] 			=    model.patch_embed.patch_size
    __snake_case       :				int 			=    (args.input_size // patch_size[0], args.input_size // patch_size[1])
    __snake_case       :				Dict 			=    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       :				List[str] 			=    checkpoint[model_key]
            print(F'''Load state_dict by model_key = {model_key}'''   )
            break

    __snake_case       :				Optional[Any] 			=    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:
            __snake_case       :				int 			=    checkpoint_model[key]
            __snake_case       :				Union[str, Any] 			=    rel_pos_bias.size()
            __snake_case       :				List[Any] 			=    model.state_dict()[key].size()
            __snake_case       :				Any 			=    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 					__lowerCAmelCase				(		):







    '''simple docstring'''
    __snake_case       :				List[Any] 			=    get_args()

    __snake_case       :				Optional[Any] 			=    '''ft1k''' in args.hf_checkpoint_name
    __snake_case       :				Union[str, Any] 			=    '''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       :				Tuple 			=    DataaVecVisionConfig()
    if is_finetuned:
        __snake_case       :				int 			=    True
        __snake_case       :				List[Any] 			=    False
        __snake_case       :				str 			=    True
        __snake_case       :				Tuple 			=    1_0_0_0

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

    if is_large:
        __snake_case       :				Union[str, Any] 			=    1_0_2_4
        __snake_case       :				Optional[Any] 			=    4_0_9_6
        __snake_case       :				str 			=    2_4
        __snake_case       :				Optional[Any] 			=    1_6

    # 2. Load Beit model
    __snake_case       :				List[Any] 			=    load_beit_model(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase   )
    orig_model.eval()

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

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

    # 4. Load HF Data2VecVision model
    if is_finetuned:
        __snake_case       :				str 			=    DataaVecVisionForImageClassification(_UpperCamelCase   )
        hf_model.eval()
        __snake_case       :				str 			=    False
        __snake_case       :				int 			=    '''data2vec_vision.'''
    else:
        __snake_case       :				int 			=    DataaVecVisionModel(_UpperCamelCase   )
        hf_model.eval()
        __snake_case       :				Any 			=    True
        __snake_case       :				Tuple 			=    ''''''

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

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

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

    # 6. Compare
    __snake_case       :				List[str] 			=    torch.max(torch.abs(hf_output - orig_model_output   )   ).item()

    print(F'''max_absolute_diff = {max_absolute_diff}'''   )
    __snake_case       :				Optional[Any] 			=    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"








UpperCAmelCase__			:   Optional[int]      			=    [0.4_85, 0.4_56, 0.4_06]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.2_29, 0.2_24, 0.2_25]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   Optional[Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   str      			=    [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
UpperCAmelCase__			:   Dict      			=    [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
