




"""simple docstring"""




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Tuple:

    """simple docstring"""


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

    return orig_key




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		List[str]:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase											=  orig_state_dict.pop(lowercase_ )

        if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
            continue
        else:
            __UpperCamelCase											=  val

    __UpperCamelCase											=  orig_state_dict['''cls.predictions.decoder.bias''']
    __UpperCamelCase											=  (
        torch.arange(config.max_position_embeddings ).expand((1, -1) ) + 2
    )

    return orig_state_dict




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		str:

    """simple docstring"""


    __UpperCamelCase											=  torch.load(lowercase_   ,       map_location='''cpu''' )['''model_state_dict''']
    __UpperCamelCase											=  NystromformerConfig.from_json_file(lowercase_ )
    __UpperCamelCase											=  NystromformerForMaskedLM(lowercase_ )

    __UpperCamelCase											=  convert_checkpoint_helper(lowercase_   ,       lowercase_ )

    model.load_state_dict(lowercase_ )
    model.eval()
    model.save_pretrained(lowercase_ )

    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 Nystromformer pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for Nystromformer 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_nystromformer_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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_										=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["MarianTokenizer"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       [
     "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MarianForCausalLM",
     "MarianModel",
     "MarianMTModel",
     "MarianPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
 from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_marian import MarianTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_marian import (
      MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarianForCausalLM,
      MarianModel,
      MarianMTModel,
      MarianPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
 import sys

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






