





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def      lowerCAmelCase_ (       __a		)     ->		Union[str, Any]:







  """simple docstring"""





  if "model" in orig_key:
    lowerCamelCase__:      Tuple			  =orig_key.replace("model."	,    ""		)
  if "norm1" in orig_key:
    lowerCamelCase__:      Any			  =orig_key.replace("norm1"	,    "attention.output.LayerNorm"		)
  if "norm2" in orig_key:
    lowerCamelCase__:      Optional[int]			  =orig_key.replace("norm2"	,    "output.LayerNorm"		)
  if "norm" in orig_key:
    lowerCamelCase__:      Dict			  =orig_key.replace("norm"	,    "LayerNorm"		)
  if "transformer" in orig_key:
    lowerCamelCase__:      int			  =orig_key.split("."		)[0].split("_"		)[-1]
    lowerCamelCase__:      Dict			  =orig_key.replace(F"""transformer_{layer_num}"""	,    F"""encoder.layer.{layer_num}"""		)
  if "mha.attn" in orig_key:
    lowerCamelCase__:      List[Any]			  =orig_key.replace("mha.attn"	,    "attention.self"		)
  if "mha" in orig_key:
    lowerCamelCase__:      str			  =orig_key.replace("mha"	,    "attention"		)
  if "W_q" in orig_key:
    lowerCamelCase__:      int			  =orig_key.replace("W_q"	,    "self.query"		)
  if "W_k" in orig_key:
    lowerCamelCase__:      Union[str, Any]			  =orig_key.replace("W_k"	,    "self.key"		)
  if "W_v" in orig_key:
    lowerCamelCase__:      Optional[Any]			  =orig_key.replace("W_v"	,    "self.value"		)
  if "ff1" in orig_key:
    lowerCamelCase__:      Union[str, Any]			  =orig_key.replace("ff1"	,    "intermediate.dense"		)
  if "ff2" in orig_key:
    lowerCamelCase__:      Tuple			  =orig_key.replace("ff2"	,    "output.dense"		)
  if "ff" in orig_key:
    lowerCamelCase__:      Union[str, Any]			  =orig_key.replace("ff"	,    "output.dense"		)
  if "mlm_class" in orig_key:
    lowerCamelCase__:      Optional[int]			  =orig_key.replace("mlm.mlm_class"	,    "cls.predictions.decoder"		)
  if "mlm" in orig_key:
    lowerCamelCase__:      Any			  =orig_key.replace("mlm"	,    "cls.predictions.transform"		)
  if "cls" not in orig_key:
    lowerCamelCase__:      Dict			  ="yoso." + orig_key

  return orig_key





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Optional[int]:







  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    lowerCamelCase__:      Dict			  =orig_state_dict.pop(__a		)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowerCamelCase__:      Union[str, Any]			  =val

  lowerCamelCase__:      Optional[Any]			  =orig_state_dict["cls.predictions.decoder.bias"]
  lowerCamelCase__:      Dict			  =torch.arange(__a		).expand((1, -1)		) + 2

  return orig_state_dict





def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      int			  =torch.load(__a	,    map_location="cpu"		)["model_state_dict"]
  lowerCamelCase__:      Any			  =YosoConfig.from_json_file(__a		)
  lowerCamelCase__:      Optional[int]			  =YosoForMaskedLM(__a		)

  lowerCamelCase__:      Dict			  =convert_checkpoint_helper(config.max_position_embeddings	,    __a		)

  print(model.load_state_dict(__a		)		)
  model.eval()
  model.save_pretrained(__a		)

  print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""		)


if __name__ == "__main__":
     __A									=				argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
     )
     parser.add_argument(
         "--config_file",
         default=None,
         type=str,
         required=True,
         help="The json file for YOSO model config.",
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     __A									=				parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A									=				{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				["DistilBertTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DistilBertForMaskedLM",
         "DistilBertForMultipleChoice",
         "DistilBertForQuestionAnswering",
         "DistilBertForSequenceClassification",
         "DistilBertForTokenClassification",
         "DistilBertModel",
         "DistilBertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDistilBertForMaskedLM",
         "TFDistilBertForMultipleChoice",
         "TFDistilBertForQuestionAnswering",
         "TFDistilBertForSequenceClassification",
         "TFDistilBertForTokenClassification",
         "TFDistilBertMainLayer",
         "TFDistilBertModel",
         "TFDistilBertPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "FlaxDistilBertForMaskedLM",
         "FlaxDistilBertForMultipleChoice",
         "FlaxDistilBertForQuestionAnswering",
         "FlaxDistilBertForSequenceClassification",
         "FlaxDistilBertForTokenClassification",
         "FlaxDistilBertModel",
         "FlaxDistilBertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_distilbert import (
         DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DistilBertConfig,
         DistilBertOnnxConfig,
     )
     from .tokenization_distilbert import DistilBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_distilbert_fast import DistilBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_distilbert import (
              DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
              DistilBertModel,
              DistilBertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_distilbert import (
              TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDistilBertForMaskedLM,
              TFDistilBertForMultipleChoice,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertMainLayer,
              TFDistilBertModel,
              TFDistilBertPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_distilbert import (
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertModel,
              FlaxDistilBertPreTrainedModel,
          )

else:
     import sys

     __A									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

