






'''simple docstring'''







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				(			lowercase_					:     Dict      ,					lowercase_					:     Optional[Any]=False      ,					lowercase_					:     Dict=False      ,					lowercase_					:     Optional[Any]="data2vec."      ):
       """simple docstring"""




       a_	    = 'backbone.' if is_semantic else ''

       a_	    = []
       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				(			lowercase_					:     Union[str, Any]      ,					lowercase_					:     List[str]      ,					lowercase_					:     List[Any]=False      ,					lowercase_					:     Dict=False      ,					lowercase_					:     List[Any]="data2vec_vision."      ):
       """simple docstring"""




       for i in range(config.num_hidden_layers      ):
              a_	    = 'backbone.' if is_semantic else ''
              # queries, keys and values
              a_	    = state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'      )
              a_	    = state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'      )
              a_	    = state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'      )

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

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

              a_	    = gamma_a
              a_	    = gamma_a

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

                     a_	    = table
                     a_	    = index


def 			__UpperCamelCase				(			):
       """simple docstring"""




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

       return parser.parse_args()


def 			__UpperCamelCase				(			lowercase_					:     int      ,					lowercase_					:     Optional[Any]      ,					lowercase_					:     Optional[int]      ):
       """simple docstring"""




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

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

              load(lowercase_      ,					prefix=lowercase_      )

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

              a_	    = warn_missing_keys

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

       a_	    = {
           '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,
                  }      )

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

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

       a_	    = list(checkpoint_model.keys()      )
       for key in all_keys:
              if "relative_position_index" in key:
                     checkpoint_model.pop(lowercase_      )

              if "relative_position_bias_table" in key:
                     a_	    = checkpoint_model[key]
                     a_    ,  a_	    = rel_pos_bias.size()
                     a_    ,  a_	    = model.state_dict()[key].size()
                     a_	    = model.patch_embed.patch_shape
                     if dst_patch_shape[0] != dst_patch_shape[1]:
                            raise NotImplementedError()

       load_state_dict(lowercase_      ,					lowercase_      ,					prefix=''      )

       return model


def 			__UpperCamelCase				(			):
       """simple docstring"""




       a_	    = get_args()

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

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

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

       # 2. Load Beit model
       a_	    = load_beit_model(lowercase_      ,					lowercase_      ,					lowercase_      )
       orig_model.eval()

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

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

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

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

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

       # 5. Forward HF Data2VecVision model
       with torch.no_grad():
              a_	    = hf_model(lowercase_      )

       a_	    = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

       print(F'max_absolute_diff = {max_absolute_diff}'      )
       a_	    = torch.allclose(lowercase_      ,					lowercase_      ,					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"












'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}




class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  _a     :   Union[str, Any]			 =    '''nystromformer'''







  def __init__( self      ,  UpperCamelCase__=30_000      ,  UpperCamelCase__=768      ,  UpperCamelCase__=12      ,  UpperCamelCase__=12      ,  UpperCamelCase__=3_072      ,  UpperCamelCase__="gelu_new"      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=510      ,  UpperCamelCase__=2      ,  UpperCamelCase__=64      ,  UpperCamelCase__=64      ,  UpperCamelCase__=65      ,  UpperCamelCase__=False      ,  UpperCamelCase__=0.02      ,  UpperCamelCase__=1e-5      ,  UpperCamelCase__=1      ,  UpperCamelCase__=0      ,  UpperCamelCase__=2      ,  **UpperCamelCase__      ,  ):






         """simple docstring"""


         a_	    = vocab_size
         a_	    = max_position_embeddings
         a_	    = hidden_size
         a_	    = num_hidden_layers
         a_	    = num_attention_heads
         a_	    = intermediate_size
         a_	    = hidden_act
         a_	    = hidden_dropout_prob
         a_	    = attention_probs_dropout_prob
         a_	    = initializer_range
         a_	    = type_vocab_size
         a_	    = segment_means_seq_len
         a_	    = num_landmarks
         a_	    = conv_kernel_size
         a_	    = inv_coeff_init_option
         a_	    = layer_norm_eps
         super().__init__(pad_token_id=UpperCamelCase__      ,  bos_token_id=UpperCamelCase__      ,  eos_token_id=UpperCamelCase__      ,  **UpperCamelCase__  )




