

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        __lowercase   (  _A    ,     _A=False    ,     _A=False    ,     _A="data2vec."      )  ->    str:
      SCREAMING_SNAKE_CASE			:   Any	  = """backbone.""" if is_semantic else """"""

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

            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = in_proj_weight[
                : config.hidden_size, :
            ]
            SCREAMING_SNAKE_CASE			:   Dict	  = q_bias
            SCREAMING_SNAKE_CASE			:   Tuple	  = in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE			:   int	  = in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE			:   int	  = v_bias

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

            SCREAMING_SNAKE_CASE			:   Any	  = gamma_a
            SCREAMING_SNAKE_CASE			:   Tuple	  = gamma_a

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

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






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

      return parser.parse_args()






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    List[Any]:
      def load_state_dict(_A    ,     _A    ,     _A=""    ,     _A="relative_position_index"      ):
            SCREAMING_SNAKE_CASE			:   List[Any]	  = []
            SCREAMING_SNAKE_CASE			:   int	  = []
            SCREAMING_SNAKE_CASE			:   Any	  = []
            # copy state_dict so _load_from_state_dict can modify it
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = getattr(_A    ,     """_metadata"""    ,     _A      )
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = state_dict.copy()
            if metadata is not None:
                  SCREAMING_SNAKE_CASE			:   Tuple	  = metadata

            def load(_A    ,     _A=""      ):
                  SCREAMING_SNAKE_CASE			:   Any	  = {} 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      )

            SCREAMING_SNAKE_CASE			:   int	  = []
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = []
            for key in missing_keys:
                  SCREAMING_SNAKE_CASE			:   List[str]	  = True
                  for ignore_key in ignore_missing.split("""|"""      ):
                        if ignore_key in key:
                              SCREAMING_SNAKE_CASE			:   Any	  = False
                              break
                  if keep_flag:
                        warn_missing_keys.append(_A      )
                  else:
                        ignore_missing_keys.append(_A      )

            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = 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      )      )

      SCREAMING_SNAKE_CASE			:   Dict	  = {
          """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,
                }      )

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

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

      SCREAMING_SNAKE_CASE			:   List[Any]	  = 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:
                  SCREAMING_SNAKE_CASE			:   Any	  = checkpoint_model[key]
                  SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Optional[Any]	  = rel_pos_bias.size()
                  SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   List[str]	  = model.state_dict()[key].size()
                  SCREAMING_SNAKE_CASE			:   List[Any]	  = 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        __lowercase   (  )  ->    int:
      SCREAMING_SNAKE_CASE			:   List[Any]	  = get_args()

      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = """ft1k""" in args.hf_checkpoint_name
      SCREAMING_SNAKE_CASE			:   Union[str, 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
      SCREAMING_SNAKE_CASE			:   List[str]	  = DataaVecVisionConfig()
      if is_finetuned:
            SCREAMING_SNAKE_CASE			:   Any	  = True
            SCREAMING_SNAKE_CASE			:   int	  = False
            SCREAMING_SNAKE_CASE			:   str	  = True
            SCREAMING_SNAKE_CASE			:   str	  = 1000

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

      if is_large:
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = 1024
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 4096
            SCREAMING_SNAKE_CASE			:   Dict	  = 24
            SCREAMING_SNAKE_CASE			:   Any	  = 16

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

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

      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = (pixel_values,) if is_finetuned else (pixel_values, None)
      with torch.no_grad():
            SCREAMING_SNAKE_CASE			:   int	  = orig_model(*_A      )

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

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

      read_in_q_k_v(_A    ,     _A    ,     hf_prefix=_A    ,     has_lm_head=_A      )
      SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   List[str]	  = 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():
            SCREAMING_SNAKE_CASE			:   Dict	  = hf_model(_A      )

      SCREAMING_SNAKE_CASE			:   List[Any]	  = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

      print(F"max_absolute_diff = {max_absolute_diff}"      )
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = 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 typing import TYPE_CHECKING

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


UpperCAmelCase__			:  Tuple					     =     {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Tuple					     =     [
      """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2Text2ForCausalLM""",
      """Speech2Text2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__			:  Optional[int]					     =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




