

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       :		Tuple			, _SCREAMING_SNAKE_CASE       :		Any=False			, _SCREAMING_SNAKE_CASE       :		Union[str, Any]=False			, _SCREAMING_SNAKE_CASE       :		Any="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       :		str			, _SCREAMING_SNAKE_CASE       :		Tuple			, _SCREAMING_SNAKE_CASE       :		List[Any]=False			, _SCREAMING_SNAKE_CASE       :		str=False			, _SCREAMING_SNAKE_CASE       :		str="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=_SCREAMING_SNAKE_CASE							)
    parser.add_argument('--hf_checkpoint_name'			, type=_SCREAMING_SNAKE_CASE							)
    parser.add_argument('--input_size'			, default=224			, type=_SCREAMING_SNAKE_CASE			, help='images input size'							)
    parser.add_argument('--beit_checkpoint'			, default=''			, help='beit checkpoint'							)

    return parser.parse_args()


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[Any]			, _SCREAMING_SNAKE_CASE       :		List[Any]			, _SCREAMING_SNAKE_CASE       :		List[Any]							):





    """simple docstring"""


    def load_state_dict(_SCREAMING_SNAKE_CASE       :		int			, _SCREAMING_SNAKE_CASE       :		Optional[Any]			, _SCREAMING_SNAKE_CASE       :		Any=""			, _SCREAMING_SNAKE_CASE       :		List[str]="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(_SCREAMING_SNAKE_CASE			, '_metadata'			, _SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    state_dict.copy()
        if metadata is not None:
            SCREAMING_SNAKE_CASE_						=    metadata

        def load(_SCREAMING_SNAKE_CASE       :		Optional[int]			, _SCREAMING_SNAKE_CASE       :		List[str]=""							):
            SCREAMING_SNAKE_CASE_						=    {} if metadata is None else metadata.get(prefix[:-1]			, {}							)
            module._load_from_state_dict(
                _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
            for name, child in module._modules.items():
                if child is not None:
                    load(_SCREAMING_SNAKE_CASE			, prefix + name + '.'							)

        load(_SCREAMING_SNAKE_CASE			, prefix=_SCREAMING_SNAKE_CASE							)

        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(_SCREAMING_SNAKE_CASE							)
            else:
                ignore_missing_keys.append(_SCREAMING_SNAKE_CASE							)

        SCREAMING_SNAKE_CASE_						=    warn_missing_keys

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

    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'			, **_SCREAMING_SNAKE_CASE			, )
    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(_SCREAMING_SNAKE_CASE							)

        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(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, 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(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, repo_type='dataset'							)			, 'r'							)							)
        SCREAMING_SNAKE_CASE_						=    {int(_SCREAMING_SNAKE_CASE							): 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(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
    orig_model.eval()

    # 3. Forward Beit model
    SCREAMING_SNAKE_CASE_						=    BeitImageProcessor(size=config.image_size			, do_center_crop=_SCREAMING_SNAKE_CASE							)
    SCREAMING_SNAKE_CASE_						=    Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'							)
    SCREAMING_SNAKE_CASE_						=    image_processor(images=_SCREAMING_SNAKE_CASE			, 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(*_SCREAMING_SNAKE_CASE							)

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

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

    read_in_q_k_v(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, hf_prefix=_SCREAMING_SNAKE_CASE			, has_lm_head=_SCREAMING_SNAKE_CASE							)
    SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    hf_model.load_state_dict(_SCREAMING_SNAKE_CASE			, strict=_SCREAMING_SNAKE_CASE							)
    print('HF missing'			, _SCREAMING_SNAKE_CASE							)
    print('HF unexpected_keys'			, _SCREAMING_SNAKE_CASE							)

    # 5. Forward HF Data2VecVision model
    with torch.no_grad():
        SCREAMING_SNAKE_CASE_						=    hf_model(_SCREAMING_SNAKE_CASE							)

    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(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, 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 os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_reformer import ReformerTokenizer
else:
       UpperCamelCase__	:   Optional[int]								=     None


UpperCamelCase__	:   Union[str, Any]								=     logging.get_logger(__name__)


UpperCamelCase__	:   Any								=     "▁"

UpperCamelCase__	:   Any								=     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__	:   List[Any]								=     {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

UpperCamelCase__	:   Optional[Any]								=     {
    "google/reformer-crime-and-punishment": 524_288,
}



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						int    			=	VOCAB_FILES_NAMES
  __lowerCAmelCase  :						int    			=	PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase  :						Tuple    			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase  :						List[str]    			=	['input_ids', 'attention_mask']
  __lowerCAmelCase  :						Dict    			=	ReformerTokenizer

  def __init__(       self    ,		_A=None    ,		_A=None    ,		_A="</s>"    ,		_A="<unk>"    ,		_A=[]    ,		**_A    ,		):
      super().__init__(
          _A    ,		tokenizer_file=_A    ,		eos_token=_A    ,		unk_token=_A    ,		additional_special_tokens=_A    ,		**_A    ,		)

      SCREAMING_SNAKE_CASE_						=    vocab_file
      SCREAMING_SNAKE_CASE_						=    False if not self.vocab_file else True


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None):
      if not self.can_save_slow_tokenizer:
          raise ValueError(
              'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
              'tokenizer.')

      if not os.path.isdir(_A):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_						=    os.path.join(
          _A    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

      if os.path.abspath(self.vocab_file) != os.path.abspath(_A):
          copyfile(self.vocab_file    ,		_A)

      return (out_vocab_file,)


