




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 					A      (				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE=False			,_SCREAMING_SNAKE_CASE=False			,_SCREAMING_SNAKE_CASE="data2vec."   )     ->			Tuple:
       lowerCamelCase       :				List[str]      			= "backbone." if is_semantic else ""

       lowerCamelCase       :				Union[str, 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 					A      (				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE=False			,_SCREAMING_SNAKE_CASE=False			,_SCREAMING_SNAKE_CASE="data2vec_vision."   )     ->			Any:






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

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

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

              lowerCamelCase       :				Any      			= gamma_a
              lowerCamelCase       :				List[Any]      			= gamma_a

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

                     lowerCamelCase       :				List[Any]      			= table
                     lowerCamelCase       :				Tuple      			= index

def 					A      (				)     ->			List[str]:
       lowerCamelCase       :				str      			= 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=224			,type=snake_case__			,help="images input size"   )
       parser.add_argument("--beit_checkpoint"			,default=""			,help="beit checkpoint"   )

       return parser.parse_args()

def 					A      (				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Tuple:






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

              def load(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE=""   ):
                     lowerCamelCase       :				str      			= {} 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__   )

              lowerCamelCase       :				Union[str, Any]      			= []
              lowerCamelCase       :				Any      			= []
              for key in missing_keys:
                     lowerCamelCase       :				str      			= True
                     for ignore_key in ignore_missing.split("|"   ):
                            if ignore_key in key:
                                   lowerCamelCase       :				Optional[Any]      			= False
                                   break
                     if keep_flag:
                            warn_missing_keys.append(snake_case__   )
                     else:
                            ignore_missing_keys.append(snake_case__   )

              lowerCamelCase       :				str      			= 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__   )   )

       lowerCamelCase       :				Any      			= {
           "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": 0.001,
                      "use_rel_pos_bias": True,
                  }   )

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

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

       lowerCamelCase       :				Dict      			= 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:
                     lowerCamelCase       :				Tuple      			= checkpoint_model[key]
                     lowerCamelCase			,				lowerCamelCase       :				Dict      			= rel_pos_bias.size()
                     lowerCamelCase			,				lowerCamelCase       :				Optional[int]      			= model.state_dict()[key].size()
                     lowerCamelCase       :				List[Any]      			= 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 					A      (				)     ->			List[str]:
       lowerCamelCase       :				int      			= get_args()

       lowerCamelCase       :				Any      			= "ft1k" in args.hf_checkpoint_name
       lowerCamelCase       :				Dict      			= "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
       lowerCamelCase       :				List[Any]      			= DataaVecVisionConfig()
       if is_finetuned:
              lowerCamelCase       :				Any      			= True
              lowerCamelCase       :				Optional[int]      			= False
              lowerCamelCase       :				Optional[Any]      			= True
              lowerCamelCase       :				Any      			= 1000

              lowerCamelCase       :				str      			= "huggingface/label-files"
              lowerCamelCase       :				Optional[Any]      			= "imagenet-1k-id2label.json"
              lowerCamelCase       :				Union[str, Any]      			= json.load(open(hf_hub_download(snake_case__			,snake_case__			,repo_type="dataset"   )			,"r"   )   )
              lowerCamelCase       :				Any      			= {int(snake_case__   ): v for k, v in idalabel.items()}
              lowerCamelCase       :				str      			= idalabel
              lowerCamelCase       :				Tuple      			= {v: k for k, v in idalabel.items()}
       else:
              lowerCamelCase       :				Union[str, Any]      			= False
              lowerCamelCase       :				Any      			= True
              lowerCamelCase       :				Any      			= False

       if is_large:
              lowerCamelCase       :				Optional[Any]      			= 1024
              lowerCamelCase       :				Dict      			= 4096
              lowerCamelCase       :				Any      			= 24
              lowerCamelCase       :				List[str]      			= 16

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

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

       lowerCamelCase       :				str      			= (pixel_values,) if is_finetuned else (pixel_values, None)
       with torch.no_grad():
              lowerCamelCase       :				Dict      			= orig_model(*snake_case__   )

       # 4. Load HF Data2VecVision model
       if is_finetuned:
              lowerCamelCase       :				List[str]      			= DataaVecVisionForImageClassification(snake_case__   )
              hf_model.eval()
              lowerCamelCase       :				Dict      			= False
              lowerCamelCase       :				int      			= "data2vec_vision."
       else:
              lowerCamelCase       :				Tuple      			= DataaVecVisionModel(snake_case__   )
              hf_model.eval()
              lowerCamelCase       :				List[Any]      			= True
              lowerCamelCase       :				Optional[Any]      			= ""

       lowerCamelCase       :				List[Any]      			= create_rename_keys(snake_case__			,hf_prefix=snake_case__			,has_lm_head=snake_case__   )
       lowerCamelCase       :				Optional[Any]      			= orig_model.state_dict()
       for src, dest in rename_keys:
              lowerCamelCase       :				str      			= state_dict.pop(snake_case__   )
              lowerCamelCase       :				Optional[Any]      			= val

       read_in_q_k_v(snake_case__			,snake_case__			,hf_prefix=snake_case__			,has_lm_head=snake_case__   )
       lowerCamelCase			,				lowerCamelCase       :				List[Any]      			= 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():
              lowerCamelCase       :				List[Any]      			= hf_model(snake_case__   )

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

       # 6. Compare
       lowerCamelCase       :				str      			= torch.max(torch.abs(hf_output - orig_model_output   )   ).item()

       print(f'''max_absolute_diff = {max_absolute_diff}'''   )
       lowerCamelCase       :				Dict      			= 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"












import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


SCREAMING_SNAKE_CASE__	:				int  =       logging.get_logger(__name__)




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     str     					=				"""speech-encoder-decoder"""
    lowerCamelCase_				:     List[str]     					=				True







    def __init__(   self					,  **UpperCamelCase__					)					->				Dict:
           super().__init__(**UpperCamelCase__					)
           if "encoder" not in kwargs or "decoder" not in kwargs:
                  raise ValueError(
                      F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                      F''' `decoder` sub-configurations are passed, but only {kwargs}'''					)

           lowerCamelCase       :				Dict      			= kwargs.pop("encoder"					)
           lowerCamelCase       :				Tuple      			= encoder_config.pop("model_type"					)
           lowerCamelCase       :				int      			= kwargs.pop("decoder"					)
           lowerCamelCase       :				List[Any]      			= decoder_config.pop("model_type"					)

           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= True







    @classmethod
    def    _lowercase					(   cls					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__					)					->				PretrainedConfig:
           logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"					)
           lowerCamelCase       :				Dict      			= True
           lowerCamelCase       :				Union[str, Any]      			= True

           return cls(encoder=encoder_config.to_dict()					,  decoder=decoder_config.to_dict()					,  **UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Tuple:
           lowerCamelCase       :				Tuple      			= copy.deepcopy(self.__dict__					)
           lowerCamelCase       :				Optional[int]      			= self.encoder.to_dict()
           lowerCamelCase       :				List[str]      			= self.decoder.to_dict()
           lowerCamelCase       :				Dict      			= self.__class__.model_type
           return output






