


'''simple docstring'''







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def       lowercase					(_A       ):







       """simple docstring"""







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

       return orig_key






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       for key in orig_state_dict.copy().keys():
              _lowerCAmelCase     :				str       =							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     :				Optional[Any]       =							torch.arange(_A       ).expand((1, -1)       ) + 2

       return orig_state_dict






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							torch.load(_A ,    map_location='cpu'       )['model_state_dict']
       _lowerCAmelCase     :				List[Any]       =							YosoConfig.from_json_file(_A       )
       _lowerCAmelCase     :				str       =							YosoForMaskedLM(_A       )

       _lowerCAmelCase     :				Optional[int]       =							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__":
       lowerCAmelCase     :  Tuple											=    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."""
       )
       lowerCAmelCase     :  List[str]											=    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_tokenizers_available, is_torch_available


lowerCAmelCase     :  Any											=    {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """CanineForMultipleChoice""",
           """CanineForQuestionAnswering""",
           """CanineForSequenceClassification""",
           """CanineForTokenClassification""",
           """CanineLayer""",
           """CanineModel""",
           """CaninePreTrainedModel""",
           """load_tf_weights_in_canine""",
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

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

