'''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    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Optional[Any]=False      ,		__snake_case       : Optional[Any]=False      ,		__snake_case       : Tuple="data2vec."							):
     _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    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : Any=False      ,		__snake_case       : Any=False      ,		__snake_case       : str="data2vec_vision."							):
     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    _SCREAMING_SNAKE_CASE		(			):
     _A								=     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=2_2_4      ,		type=__snake_case      ,		help='images input size'							)
     parser.add_argument('--beit_checkpoint'      ,		default=''      ,		help='beit checkpoint'							)

     return parser.parse_args()





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Dict      ,		__snake_case       : Optional[int]      ,		__snake_case       : List[Any]							):
     def load_state_dict(__snake_case       : Optional[Any]      ,		__snake_case       : Dict      ,		__snake_case       : List[Any]=""      ,		__snake_case       : List[Any]="relative_position_index"							):
          _A								=     []
          _A								=     []
          _A								=     []
          # copy state_dict so _load_from_state_dict can modify it
          _A								=     getattr(__snake_case      ,		'_metadata'      ,		__snake_case							)
          _A								=     state_dict.copy()
          if metadata is not None:
               _A								=     metadata

          def load(__snake_case       : Optional[int]      ,		__snake_case       : List[Any]=""							):
               _A								=     {} 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							)

          _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(__snake_case							)
               else:
                    ignore_missing_keys.append(__snake_case							)

          _A								=     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							)							)

     _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_0_0_0,
                  'use_mean_pooling': True,
                  'init_scale': 0.0_01,
                  'use_rel_pos_bias': True,
              }							)

     _A								=     create_model(
         'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'      ,		**__snake_case      ,		)
     _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(__snake_case							)

          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(__snake_case      ,		__snake_case      ,		prefix=''							)

     return model





def    _SCREAMING_SNAKE_CASE		(			):
     _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_0_0_0

          _A								=     'huggingface/label-files'
          _A								=     'imagenet-1k-id2label.json'
          _A								=     json.load(open(hf_hub_download(__snake_case      ,		__snake_case      ,		repo_type='dataset'							)      ,		'r'							)							)
          _A								=     {int(__snake_case							): 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_0_2_4
          _A								=     4_0_9_6
          _A								=     2_4
          _A								=     1_6

     # 2. Load Beit model
     _A								=     load_beit_model(__snake_case      ,		__snake_case      ,		__snake_case							)
     orig_model.eval()

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

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

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

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

     read_in_q_k_v(__snake_case      ,		__snake_case      ,		hf_prefix=__snake_case      ,		has_lm_head=__snake_case							)
     _A		,					_A								=     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():
          _A								=     hf_model(__snake_case							)

     _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(__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"




'''simple docstring'''






import torch

from accelerate import Accelerator, DistributedType







class lowercase_					:







   """simple docstring"""

   def __enter__(			self			: Optional[int]    )     -> Union[str, Any]:
        if self.enabled:
             _A								=     self.model.no_sync()
             self.model_sync_obj.__enter__()

        return self
   def __exit__(			self			: Dict,   UpperCamelCase__			: Any,   UpperCamelCase__			: int,   UpperCamelCase__			: Any    )     -> str:
        if self.enabled:
             # Average all models on exit
             self._sync_and_avg_model_params()
             self.model_sync_obj.__exit__(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__    )
   def __init__(			self			: Optional[int],   UpperCamelCase__			: Accelerator,   UpperCamelCase__			: torch.nn.Module,   UpperCamelCase__			: int,   UpperCamelCase__			: bool = True    )     -> str:
        if accelerator.distributed_type not in [
            DistributedType.NO,
            DistributedType.MULTI_CPU,
            DistributedType.MULTI_GPU,
        ]:
             raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'    )
        _A								=     enabled and accelerator.distributed_type != DistributedType.NO
        _A								=     0
        if self.enabled:
             _A								=     accelerator
             _A								=     model
             _A								=     local_sgd_steps
   def   __UpperCAmelCase      (			self			: Any    )     -> int:
        self.num_steps += 1
        if not self.enabled:
             return

        if self.num_steps % self.local_sgd_steps == 0:
             self._sync_and_avg_model_params()

   def   __UpperCAmelCase      (			self			: Dict    )     -> int:
        self.accelerator.wait_for_everyone()
        with self.accelerator.autocast():
             for param in self.model.parameters():
                  _A								=     self.accelerator.reduce(param.data,   reduction='mean'    )



