

'''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  _lowerCAmelCase      (		_UpperCamelCase		:  Any							,				_UpperCamelCase		:  Any=False							,				_UpperCamelCase		:  List[Any]=False							,				_UpperCamelCase		:  Union[str, Any]="data2vec."       )   ->    List[str]:
     """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  _lowerCAmelCase      (		_UpperCamelCase		:  int							,				_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  Optional[int]=False							,				_UpperCamelCase		:  Any=False							,				_UpperCamelCase		:  Optional[int]="data2vec_vision."       )   ->    Tuple:
     """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  _lowerCAmelCase      (		)   ->    Tuple:
     """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=2_24							,				type=_UpperCamelCase							,				help='images input size'       )
     parser.add_argument('--beit_checkpoint'							,				default=''							,				help='beit checkpoint'       )

     return parser.parse_args()


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  Optional[int]       )   ->    Dict:
     """simple docstring"""




     def load_state_dict(_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  int							,				_UpperCamelCase		:  Dict=""							,				_UpperCamelCase		:  int="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(_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  str=""       ):
               _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': 10_00,
                  'use_mean_pooling': True,
                  'init_scale': 0.0_01,
                  '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  _lowerCAmelCase      (		)   ->    List[Any]:
     """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												=10_00

          _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												=10_24
          _SCREAMING_SNAKE_CASE												=40_96
          _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"



'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase				:   Dict      				=  logging.get_logger(__name__)

lowerCamelCase				:   Dict      				=  {
    "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 							A__					( A__						):
  A__					=       'nystromformer'





  def __init__(	self   :			List[str]							,      _a   :			Dict=3_0000							,      _a   :			Dict=768							,      _a   :			Union[str, Any]=12							,      _a   :			Optional[int]=12							,      _a   :			Union[str, Any]=3072							,      _a   :			Optional[Any]="gelu_new"							,      _a   :			Dict=0.1							,      _a   :			Dict=0.1							,      _a   :			Union[str, Any]=510							,      _a   :			Any=2							,      _a   :			List[Any]=64							,      _a   :			int=64							,      _a   :			Optional[Any]=65							,      _a   :			str=False							,      _a   :			List[Any]=0.02							,      _a   :			List[str]=1e-5							,      _a   :			Optional[Any]=1							,      _a   :			Optional[Any]=0							,      _a   :			List[Any]=2							,      **_a   :			List[Any]							,      )			->					List[Any]:





       '''simple docstring'''






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