'''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 (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=False	,							SCREAMING_SNAKE_CASE_=False	,							SCREAMING_SNAKE_CASE_="data2vec."				):
     lowercase_       :							Tuple  					=       'backbone.' if is_semantic else ''

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

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

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          lowercase_       :							int  					=       state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''				)
          lowercase_       :							List[str]  					=       state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''				)

          lowercase_       :							Union[str, Any]  					=       gamma_a
          lowercase_       :							int  					=       gamma_a

          # relative_position bias table + index
          if not has_lm_head:
               # each layer has its own relative position bias
               lowercase_       :							List[Any]  					=       state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''				)
               lowercase_       :							int  					=       state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''				)

               lowercase_       :							int  					=       table
               lowercase_       :							Optional[Any]  					=       index

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

          def load(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=""				):
               lowercase_       :							Dict  					=       {} 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_				)

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

          lowercase_       :							str  					=       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_				)				)

     lowercase_       :							int  					=       {
         '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,
              }				)

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

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

     lowercase_       :							Optional[Any]  					=       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:
               lowercase_       :							Optional[Any]  					=       checkpoint_model[key]
               lowercase_					,lowercase_       :							str  					=       rel_pos_bias.size()
               lowercase_					,lowercase_       :							Optional[int]  					=       model.state_dict()[key].size()
               lowercase_       :							Optional[Any]  					=       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 (							):
     lowercase_       :							int  					=       get_args()

     lowercase_       :							List[str]  					=       'ft1k' in args.hf_checkpoint_name
     lowercase_       :							Optional[Any]  					=       '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
     lowercase_       :							Dict  					=       DataaVecVisionConfig()
     if is_finetuned:
          lowercase_       :							List[Any]  					=       True
          lowercase_       :							str  					=       False
          lowercase_       :							Optional[int]  					=       True
          lowercase_       :							List[Any]  					=       1_000

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

     if is_large:
          lowercase_       :							List[str]  					=       1_024
          lowercase_       :							Union[str, Any]  					=       4_096
          lowercase_       :							Tuple  					=       24
          lowercase_       :							Union[str, Any]  					=       16

     # 2. Load Beit model
     lowercase_       :							Optional[int]  					=       load_beit_model(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)
     orig_model.eval()

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

     lowercase_       :							Optional[Any]  					=       (pixel_values,) if is_finetuned else (pixel_values, None)
     with torch.no_grad():
          lowercase_       :							int  					=       orig_model(*SCREAMING_SNAKE_CASE_				)

     # 4. Load HF Data2VecVision model
     if is_finetuned:
          lowercase_       :							Union[str, Any]  					=       DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_				)
          hf_model.eval()
          lowercase_       :							List[str]  					=       False
          lowercase_       :							Tuple  					=       'data2vec_vision.'
     else:
          lowercase_       :							Tuple  					=       DataaVecVisionModel(SCREAMING_SNAKE_CASE_				)
          hf_model.eval()
          lowercase_       :							Union[str, Any]  					=       True
          lowercase_       :							List[Any]  					=       ''

     lowercase_       :							Dict  					=       create_rename_keys(SCREAMING_SNAKE_CASE_	,							hf_prefix=SCREAMING_SNAKE_CASE_	,							has_lm_head=SCREAMING_SNAKE_CASE_				)
     lowercase_       :							Union[str, Any]  					=       orig_model.state_dict()
     for src, dest in rename_keys:
          lowercase_       :							List[str]  					=       state_dict.pop(SCREAMING_SNAKE_CASE_				)
          lowercase_       :							List[Any]  					=       val

     read_in_q_k_v(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							hf_prefix=SCREAMING_SNAKE_CASE_	,							has_lm_head=SCREAMING_SNAKE_CASE_				)
     lowercase_					,lowercase_       :							Any  					=       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():
          lowercase_       :							List[str]  					=       hf_model(SCREAMING_SNAKE_CASE_				)

     lowercase_       :							List[str]  					=       hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

     print(f'''max_absolute_diff = {max_absolute_diff}'''				)
     lowercase_       :							Optional[Any]  					=       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"





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_A										= {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= [
     'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RwkvForCausalLM',
     'RwkvModel',
     'RwkvPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rwkv import (
      RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
      RwkvForCausalLM,
      RwkvModel,
      RwkvPreTrainedModel,
  )
else:
 import sys

 _A										= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




