
"""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		(		a_      :		Tuple     ,	a_      :		Tuple=False     ,	a_      :		Optional[Any]=False     ,	a_      :		Union[str, Any]="data2vec."					)  ->							Union[str, Any]:
   _UpperCAmelCase 						=       "backbone." if is_semantic else ""

   _UpperCAmelCase 						=       []
   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		(		a_      :		Optional[int]     ,	a_      :		List[str]     ,	a_      :		Any=False     ,	a_      :		Tuple=False     ,	a_      :		str="data2vec_vision."					)  ->							Optional[int]:
   for i in range(config.num_hidden_layers					):
      _UpperCAmelCase 						=       "backbone." if is_semantic else ""
      # queries, keys and values
      _UpperCAmelCase 						=       state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''					)
      _UpperCAmelCase 						=       state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''					)
      _UpperCAmelCase 						=       state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''					)

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

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

      _UpperCAmelCase 						=       gamma_a
      _UpperCAmelCase 						=       gamma_a

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

         _UpperCAmelCase 						=       table
         _UpperCAmelCase 						=       index
def       __UpperCAmelCase		(		)  ->							Union[str, Any]:
   _UpperCAmelCase 						=       argparse.ArgumentParser(
       "Convert Data2VecVision to HF for image classification and pretraining"     ,	add_help=a_					)
   parser.add_argument("--hf_checkpoint_name"     ,	type=a_					)
   parser.add_argument("--input_size"     ,	default=2_24     ,	type=a_     ,	help="images input size"					)
   parser.add_argument("--beit_checkpoint"     ,	default=""     ,	help="beit checkpoint"					)

   return parser.parse_args()
def       __UpperCAmelCase		(		a_      :		str     ,	a_      :		Tuple     ,	a_      :		Tuple					)  ->							List[str]:
   def load_state_dict(a_      :		List[str]     ,	a_      :		str     ,	a_      :		str=""     ,	a_      :		Union[str, Any]="relative_position_index"					):
      _UpperCAmelCase 						=       []
      _UpperCAmelCase 						=       []
      _UpperCAmelCase 						=       []
      # copy state_dict so _load_from_state_dict can modify it
      _UpperCAmelCase 						=       getattr(a_     ,	"_metadata"     ,	a_					)
      _UpperCAmelCase 						=       state_dict.copy()
      if metadata is not None:
         _UpperCAmelCase 						=       metadata

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

      load(a_     ,	prefix=a_					)

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

      _UpperCAmelCase 						=       warn_missing_keys

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

   _UpperCAmelCase 						=       {
       "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": 0.001,
              "use_rel_pos_bias": True,
          }					)

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

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

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

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

   load_state_dict(a_     ,	a_     ,	prefix=""					)

   return model
def       __UpperCAmelCase		(		)  ->							Dict:
   _UpperCAmelCase 						=       get_args()

   _UpperCAmelCase 						=       "ft1k" in args.hf_checkpoint_name
   _UpperCAmelCase 						=       "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 						=       DataaVecVisionConfig()
   if is_finetuned:
      _UpperCAmelCase 						=       True
      _UpperCAmelCase 						=       False
      _UpperCAmelCase 						=       True
      _UpperCAmelCase 						=       10_00

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

   if is_large:
      _UpperCAmelCase 						=       10_24
      _UpperCAmelCase 						=       40_96
      _UpperCAmelCase 						=       24
      _UpperCAmelCase 						=       16

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

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

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

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

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

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

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

   _UpperCAmelCase 						=       hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

   print(F'''max_absolute_diff = {max_absolute_diff}'''					)
   _UpperCAmelCase 						=       torch.allclose(a_     ,	a_     ,	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 warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,*lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          warnings.warn(
              "The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV1ImageProcessor instead."				,lowerCamelCase_				,)
          super().__init__(*lowerCamelCase_				,**lowerCamelCase_		)

