

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    (					_SCREAMING_SNAKE_CASE   :						Dict	,				_SCREAMING_SNAKE_CASE   :						Tuple=False	,				_SCREAMING_SNAKE_CASE   :						List[Any]=False	,				_SCREAMING_SNAKE_CASE   :						int="data2vec."    ):





    """simple docstring"""


    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    (					_SCREAMING_SNAKE_CASE   :						List[str]	,				_SCREAMING_SNAKE_CASE   :						Union[str, Any]	,				_SCREAMING_SNAKE_CASE   :						Optional[int]=False	,				_SCREAMING_SNAKE_CASE   :						Optional[int]=False	,				_SCREAMING_SNAKE_CASE   :						Union[str, Any]="data2vec_vision."    ):





    """simple docstring"""


    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    (					):





    """simple docstring"""


    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    (					_SCREAMING_SNAKE_CASE   :						Any	,				_SCREAMING_SNAKE_CASE   :						Optional[int]	,				_SCREAMING_SNAKE_CASE   :						Any    ):





    """simple docstring"""


    def load_state_dict(_SCREAMING_SNAKE_CASE   :						Dict	,				_SCREAMING_SNAKE_CASE   :						List[str]	,				_SCREAMING_SNAKE_CASE   :						Tuple=""	,				_SCREAMING_SNAKE_CASE   :						Union[str, Any]="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(_SCREAMING_SNAKE_CASE   :						Optional[Any]	,				_SCREAMING_SNAKE_CASE   :						Any=""    ):
            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    (					):





    """simple docstring"""


    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"





import cva
import numpy as np


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center    )
    except (IndexError, TypeError):
        return 0


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  image[x_coordinate][y_coordinate]
    SCREAMING_SNAKE_CASE_													=  [1, 2, 4, 8, 16, 32, 64, 128]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    SCREAMING_SNAKE_CASE_													=  [
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
    ]

    # Converting the binary value to decimal.
    return sum(
        binary_value * power for binary_value, power in zip(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )    )


if __name__ == "__main__":
       # Reading the image and converting it to grayscale.
       UpperCamelCase__					: Optional[int]								=      cva.imread(
           "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
       )

       # Create a numpy array as the same height and width of read image
       UpperCamelCase__					: Dict								=      np.zeros((image.shape[0], image.shape[1]))

       # Iterating through the image and calculating the
       # local binary pattern value for each pixel.
       for i in range(0, image.shape[0]):
              for j in range(0, image.shape[1]):
                     UpperCamelCase__					: Any								=      local_binary_value(image, i, j)

       cva.imshow("local binary pattern", lbp_image)
       cva.waitKey(0)
       cva.destroyAllWindows()


