

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 SCREAMING_SNAKE_CASE			(       lowercase_:		Dict						,  lowercase_:		int=False						,  lowercase_:		Union[str, Any]=False						,  lowercase_:		int="data2vec."			):
       _UpperCamelCase       :    str					  =					"backbone." if is_semantic else ""

       _UpperCamelCase       :    Optional[int]					  =					[]
       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 SCREAMING_SNAKE_CASE			(       lowercase_:		Any						,  lowercase_:		int						,  lowercase_:		List[Any]=False						,  lowercase_:		Union[str, Any]=False						,  lowercase_:		Union[str, Any]="data2vec_vision."			):
       for i in range(config.num_hidden_layers			):
              _UpperCamelCase       :    Union[str, Any]					  =					"backbone." if is_semantic else ""
              # queries, keys and values
              _UpperCamelCase       :    Union[str, Any]					  =					state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"			)
              _UpperCamelCase       :    Dict					  =					state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"			)
              _UpperCamelCase       :    int					  =					state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"			)

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

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

              _UpperCamelCase       :    Dict					  =					gamma_a
              _UpperCamelCase       :    Dict					  =					gamma_a

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

                     _UpperCamelCase       :    Union[str, Any]					  =					table
                     _UpperCamelCase       :    List[str]					  =					index


def SCREAMING_SNAKE_CASE			(       ):
       _UpperCamelCase       :    str					  =					argparse.ArgumentParser(
           'Convert Data2VecVision to HF for image classification and pretraining'						,  add_help=snake_case__			)
       parser.add_argument('--hf_checkpoint_name'						,  type=snake_case__			)
       parser.add_argument('--input_size'						,  default=224						,  type=snake_case__						,  help='images input size'			)
       parser.add_argument('--beit_checkpoint'						,  default=''						,  help='beit checkpoint'			)

       return parser.parse_args()


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

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

              load(snake_case__						,  prefix=snake_case__			)

              _UpperCamelCase       :    Optional[int]					  =					[]
              _UpperCamelCase       :    Optional[Any]					  =					[]
              for key in missing_keys:
                     _UpperCamelCase       :    Dict					  =					True
                     for ignore_key in ignore_missing.split('|'			):
                            if ignore_key in key:
                                   _UpperCamelCase       :    Optional[int]					  =					False
                                   break
                     if keep_flag:
                            warn_missing_keys.append(snake_case__			)
                     else:
                            ignore_missing_keys.append(snake_case__			)

              _UpperCamelCase       :    Optional[int]					  =					warn_missing_keys

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

       _UpperCamelCase       :    List[str]					  =					{
           "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,
                  }			)

       _UpperCamelCase       :    Dict					  =					create_model(
           'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'						,  **snake_case__						,  )
       _UpperCamelCase       :    Optional[int]					  =					model.patch_embed.patch_size
       _UpperCamelCase       :    List[Any]					  =					(args.input_size // patch_size[0], args.input_size // patch_size[1])
       _UpperCamelCase       :    Union[str, Any]					  =					torch.load(args.beit_checkpoint						,  map_location='cpu'			)

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

       _UpperCamelCase       :    Any					  =					list(checkpoint_model.keys()			)
       for key in all_keys:
              if "relative_position_index" in key:
                     checkpoint_model.pop(snake_case__			)

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

       load_state_dict(snake_case__						,  snake_case__						,  prefix=''			)

       return model


def SCREAMING_SNAKE_CASE			(       ):
       _UpperCamelCase       :    Any					  =					get_args()

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

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

       if is_large:
              _UpperCamelCase       :    Tuple					  =					1024
              _UpperCamelCase       :    str					  =					4096
              _UpperCamelCase       :    Optional[int]					  =					24
              _UpperCamelCase       :    Any					  =					16

       # 2. Load Beit model
       _UpperCamelCase       :    Dict					  =					load_beit_model(snake_case__						,  snake_case__						,  snake_case__			)
       orig_model.eval()

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

       _UpperCamelCase       :    str					  =					(pixel_values,) if is_finetuned else (pixel_values, None)
       with torch.no_grad():
              _UpperCamelCase       :    int					  =					orig_model(*snake_case__			)

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

       _UpperCamelCase       :    int					  =					create_rename_keys(snake_case__						,  hf_prefix=snake_case__						,  has_lm_head=snake_case__			)
       _UpperCamelCase       :    Any					  =					orig_model.state_dict()
       for src, dest in rename_keys:
              _UpperCamelCase       :    List[Any]					  =					state_dict.pop(snake_case__			)
              _UpperCamelCase       :    List[str]					  =					val

       read_in_q_k_v(snake_case__						,  snake_case__						,  hf_prefix=snake_case__						,  has_lm_head=snake_case__			)
       _UpperCamelCase       :    str					  =					hf_model.load_state_dict(snake_case__						,  strict=snake_case__			)
       print('HF missing'						,  snake_case__			)
       print('HF unexpected_keys'						,  snake_case__			)

       # 5. Forward HF Data2VecVision model
       with torch.no_grad():
              _UpperCamelCase       :    List[Any]					  =					hf_model(snake_case__			)

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

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

       print(F"max_absolute_diff = {max_absolute_diff}"			)
       _UpperCamelCase       :    List[Any]					  =					torch.allclose(snake_case__						,  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"




def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if not isinstance(lowercase_						,  lowercase_			):
              _UpperCamelCase       :    Optional[int]					  =					(
                  'operation can not be conducted on a object of type '
                  F"{type(lowercase_			).__name__}"
              )
              raise TypeError(lowercase_			)
       _UpperCamelCase       :    List[Any]					  =					''
       for _ in range(0						,  32			):
              bit_string += str(number % 2			)
              _UpperCamelCase       :    Dict					  =					number >> 1
       return bit_string


def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if number < 0:
              raise ValueError('the value of input must be positive'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('Input value must be a \'int\' type'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''			)
       _UpperCamelCase       :    List[Any]					  =					0
       # iterator over [1 to 32],since we are dealing with 32 bit integer
       for _ in range(1						,  33			):
              # left shift the bits by unity
              _UpperCamelCase       :    Tuple					  =					result << 1
              # get the end bit
              _UpperCamelCase       :    List[Any]					  =					number % 2
              # right shift the bits by unity
              _UpperCamelCase       :    Dict					  =					number >> 1
              # add that bit to our ans
              _UpperCamelCase       :    int					  =					result | end_bit
       return get_reverse_bit_string(lowercase_			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



