





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_       ,		UpperCamelCase_=False       ,		UpperCamelCase_=False       ,		UpperCamelCase_="data2vec."	)  ->		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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=False       ,		UpperCamelCase_=False       ,		UpperCamelCase_="data2vec_vision."	)  ->		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 			lowerCAmelCase_			(				)  ->		List[str]:
       UpperCamelCase_ 							=	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=224       ,		type=UpperCamelCase_       ,		help="images input size"	)
       parser.add_argument("--beit_checkpoint"       ,		default=""       ,		help="beit checkpoint"	)

       return parser.parse_args()




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Dict:
       def load_state_dict(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=""       ,		UpperCamelCase_="relative_position_index"	):
              UpperCamelCase_ 							=	[]
              UpperCamelCase_ 							=	[]
              UpperCamelCase_ 							=	[]
              # copy state_dict so _load_from_state_dict can modify it
              UpperCamelCase_ 							=	getattr(UpperCamelCase_       ,		"_metadata"       ,		UpperCamelCase_	)
              UpperCamelCase_ 							=	state_dict.copy()
              if metadata is not None:
                     UpperCamelCase_ 							=	metadata

              def load(UpperCamelCase_       ,		UpperCamelCase_=""	):
                     UpperCamelCase_ 							=	{} 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_	)

              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(UpperCamelCase_	)
                     else:
                            ignore_missing_keys.append(UpperCamelCase_	)

              UpperCamelCase_ 							=	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_	)	)

       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": 1000,
                      "use_mean_pooling": True,
                      "init_scale": 0.0_01,
                      "use_rel_pos_bias": True,
                  }	)

       UpperCamelCase_ 							=	create_model(
           "beit_large_patch16_224" if is_large else "beit_base_patch16_224"       ,		**UpperCamelCase_       ,		)
       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(UpperCamelCase_	)

              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(UpperCamelCase_       ,		UpperCamelCase_       ,		prefix=""	)

       return model




def 			lowerCAmelCase_			(				)  ->		Union[str, Any]:
       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_ 							=	1000

              UpperCamelCase_ 							=	"huggingface/label-files"
              UpperCamelCase_ 							=	"imagenet-1k-id2label.json"
              UpperCamelCase_ 							=	json.load(open(hf_hub_download(UpperCamelCase_       ,		UpperCamelCase_       ,		repo_type="dataset"	)       ,		"r"	)	)
              UpperCamelCase_ 							=	{int(UpperCamelCase_	): 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_ 							=	1024
              UpperCamelCase_ 							=	4096
              UpperCamelCase_ 							=	24
              UpperCamelCase_ 							=	16

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

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

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

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

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

       read_in_q_k_v(UpperCamelCase_       ,		UpperCamelCase_       ,		hf_prefix=UpperCamelCase_       ,		has_lm_head=UpperCamelCase_	)
       UpperCamelCase_							,			UpperCamelCase_ 							=	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():
              UpperCamelCase_ 							=	hf_model(UpperCamelCase_	)

       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(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"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class  __A							(			_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 @register_to_config
 def __init__(						self					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A = False					,       )   ->	Optional[Any]:
     super().__init__()

     a											=nn.Linear(__A					,       __A					,       bias=__A		)

     a											=nn.Embedding(__A					,       __A		)
     a											=False

     a											=nn.Dropout(p=__A		)

     a											=TaConfig(
         d_model=__A					,       num_heads=__A					,       d_kv=__A					,       d_ff=__A					,       feed_forward_proj=__A					,       dropout_rate=__A					,       is_decoder=__A					,       is_encoder_decoder=__A					,       )
     a											=nn.ModuleList()
     for lyr_num in range(__A		):
         a											=TaBlock(__A		)
         self.encoders.append(__A		)

     a											=TaLayerNorm(__A		)
     a											=nn.Dropout(p=__A		)





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	str:
     a											=self.input_proj(__A		)

     # terminal relative positional encodings
     a											=encoder_inputs.shape[1]
     a											=torch.arange(__A					,       device=encoder_inputs.device		)

     a											=encoder_inputs_mask.sum(-1		)
     a											=torch.roll(input_positions.unsqueeze(0		)					,       tuple(seq_lens.tolist()		)					,       dims=0		)
     x += self.position_encoding(__A		)

     a											=self.dropout_pre(__A		)

     # inverted the attention mask
     a											=encoder_inputs.size()
     a											=self.get_extended_attention_mask(__A					,       __A		)

     for lyr in self.encoders:
         a											=lyr(__A					,       __A		)[0]
     a											=self.layer_norm(__A		)

     return self.dropout_post(__A		), encoder_inputs_mask