





"""simple docstring"""



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def 		__lowercase					(       snake_case_  :  int						)       ->List[Any]:



    '''simple docstring'''







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

    return orig_key







def 		__lowercase					(       snake_case_  :  Tuple    ,snake_case_  :  List[str]						)       ->Any:



    '''simple docstring'''







    for key in orig_state_dict.copy().keys():
        __A   :						Optional[int]									=					orig_state_dict.pop(snake_case_						)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __A   :						int									=					val

    __A   :						Optional[Any]									=					orig_state_dict['''cls.predictions.decoder.bias''']
    __A   :						int									=					torch.arange(snake_case_						).expand((1, -1)						) + 2

    return orig_state_dict







def 		__lowercase					(       snake_case_  :  Optional[Any]    ,snake_case_  :  int    ,snake_case_  :  Optional[int]						)       ->Tuple:



    '''simple docstring'''







    __A   :						int									=					torch.load(snake_case_    ,map_location='''cpu'''						)['''model_state_dict''']
    __A   :						Tuple									=					YosoConfig.from_json_file(snake_case_						)
    __A   :						Optional[int]									=					YosoForMaskedLM(snake_case_						)

    __A   :						Optional[int]									=					convert_checkpoint_helper(config.max_position_embeddings    ,snake_case_						)

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

    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)











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_			=      {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TapasForMaskedLM""",
           """TapasForQuestionAnswering""",
           """TapasForSequenceClassification""",
           """TapasModel""",
           """TapasPreTrainedModel""",
           """load_tf_weights_in_tapas""",
       ]
try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFTapasForMaskedLM""",
           """TFTapasForQuestionAnswering""",
           """TFTapasForSequenceClassification""",
           """TFTapasModel""",
           """TFTapasPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
       from .tokenization_tapas import TapasTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tapas import (
                  TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TapasForMaskedLM,
                  TapasForQuestionAnswering,
                  TapasForSequenceClassification,
                  TapasModel,
                  TapasPreTrainedModel,
                  load_tf_weights_in_tapas,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_tapas import (
                  TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFTapasForMaskedLM,
                  TFTapasForQuestionAnswering,
                  TFTapasForSequenceClassification,
                  TFTapasModel,
                  TFTapasPreTrainedModel,
              )


else:
       import sys

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




