

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__			(   __A						:Dict  ,__A						:Optional[Any]=False  ,__A						:Any=False  ,__A						:Any="data2vec."		):


       """simple docstring"""




       __snake_case					  =       """backbone.""" if is_semantic else """"""

       __snake_case					  =       []
       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__			(   __A						:Optional[int]  ,__A						:Any  ,__A						:Any=False  ,__A						:Any=False  ,__A						:Optional[int]="data2vec_vision."		):


       """simple docstring"""




       for i in range(config.num_hidden_layers		):
              __snake_case					  =       """backbone.""" if is_semantic else """"""
              # queries, keys and values
              __snake_case					  =       state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'		)
              __snake_case					  =       state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'		)
              __snake_case					  =       state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'		)

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

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

              __snake_case					  =       gamma_a
              __snake_case					  =       gamma_a

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

                     __snake_case					  =       table
                     __snake_case					  =       index







def 	lowerCamelCase__			(   ):


       """simple docstring"""




       __snake_case					  =       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_2_4  ,type=__A  ,help="""images input size"""		)
       parser.add_argument("""--beit_checkpoint"""  ,default=""""""  ,help="""beit checkpoint"""		)

       return parser.parse_args()







def 	lowerCamelCase__			(   __A						:int  ,__A						:List[Any]  ,__A						:Union[str, Any]		):


       """simple docstring"""




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

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

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

              __snake_case					  =       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		)		)

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

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

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

       __snake_case					  =       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:
                     __snake_case					  =       checkpoint_model[key]
                     __snake_case  ,						__snake_case					  =       rel_pos_bias.size()
                     __snake_case  ,						__snake_case					  =       model.state_dict()[key].size()
                     __snake_case					  =       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 	lowerCamelCase__			(   ):


       """simple docstring"""




       __snake_case					  =       get_args()

       __snake_case					  =       """ft1k""" in args.hf_checkpoint_name
       __snake_case					  =       """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
       __snake_case					  =       DataaVecVisionConfig()
       if is_finetuned:
              __snake_case					  =       True
              __snake_case					  =       False
              __snake_case					  =       True
              __snake_case					  =       1_0_0_0

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

       if is_large:
              __snake_case					  =       1_0_2_4
              __snake_case					  =       4_0_9_6
              __snake_case					  =       2_4
              __snake_case					  =       1_6

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

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

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

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

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

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

       __snake_case					  =       hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

       print(F'max_absolute_diff = {max_absolute_diff}'		)
       __snake_case					  =       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"




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__		   =    logging.get_logger(__name__)

UpperCamelCase__		   =    {
    '''apple/mobilevitv2-1.0''': '''https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json''',
}

class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       "mobilevitv2"


       def __init__(		self			,   _UpperCamelCase=3			,   _UpperCamelCase=2_56			,   _UpperCamelCase=2			,   _UpperCamelCase=2.0			,   _UpperCamelCase="swish"			,   _UpperCamelCase=3			,   _UpperCamelCase=32			,   _UpperCamelCase=0.1			,   _UpperCamelCase=0.02			,   _UpperCamelCase=1E-5			,   _UpperCamelCase=5_12			,   _UpperCamelCase=[6, 12, 18]			,   _UpperCamelCase=0.1			,   _UpperCamelCase=2_55			,   _UpperCamelCase=[2, 4, 3]			,   _UpperCamelCase=[1_28, 1_92, 2_56]			,   _UpperCamelCase=1.0			,   _UpperCamelCase=2			,   _UpperCamelCase=0.0			,   _UpperCamelCase=0.0			,   **_UpperCamelCase			,   )			->       List[Any]:






              """simple docstring"""


              super().__init__(**_UpperCamelCase      )

              __snake_case					  =       num_channels
              __snake_case					  =       image_size
              __snake_case					  =       patch_size
              __snake_case					  =       expand_ratio
              __snake_case					  =       hidden_act
              __snake_case					  =       conv_kernel_size
              __snake_case					  =       output_stride
              __snake_case					  =       initializer_range
              __snake_case					  =       layer_norm_eps
              __snake_case					  =       n_attn_blocks
              __snake_case					  =       base_attn_unit_dims
              __snake_case					  =       width_multiplier
              __snake_case					  =       ffn_multiplier
              __snake_case					  =       ffn_dropout
              __snake_case					  =       attn_dropout
              __snake_case					  =       classifier_dropout_prob

              # decode head attributes for semantic segmentation
              __snake_case					  =       aspp_out_channels
              __snake_case					  =       atrous_rates
              __snake_case					  =       aspp_dropout_prob
              __snake_case					  =       semantic_loss_ignore_index






class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       version.parse("1.11"     )


       @property
       def  a		(		self      )			->       Mapping[str, Mapping[int, str]]:






              """simple docstring"""


              return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]      )


       @property
       def  a		(		self      )			->       Mapping[str, Mapping[int, str]]:






              """simple docstring"""


              if self.task == "image-classification":
                     return OrderedDict([("""logits""", {0: """batch"""})]      )
              else:
                     return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]      )






       @property
       def  a		(		self      )			->       float:






              """simple docstring"""


              return 1E-4

