
"""simple docstring"""




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM





def    _a (						lowerCamelCase							):
 if "model" in orig_key:
  lowerCamelCase		:     Dict          =				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		:     Optional[Any]          =				orig_key.replace("""norm2""",     """output.LayerNorm"""							)
 if "norm" in orig_key:
  lowerCamelCase		:     str          =				orig_key.replace("""norm""",     """LayerNorm"""							)
 if "transformer" in orig_key:
  lowerCamelCase		:     Union[str, Any]          =				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		:     List[Any]          =				orig_key.replace("""mha""",     """attention"""							)
 if "W_q" in orig_key:
  lowerCamelCase		:     Optional[Any]          =				orig_key.replace("""W_q""",     """self.query"""							)
 if "W_k" in orig_key:
  lowerCamelCase		:     Optional[Any]          =				orig_key.replace("""W_k""",     """self.key"""							)
 if "W_v" in orig_key:
  lowerCamelCase		:     List[str]          =				orig_key.replace("""W_v""",     """self.value"""							)
 if "ff1" in orig_key:
  lowerCamelCase		:     Tuple          =				orig_key.replace("""ff1""",     """intermediate.dense"""							)
 if "ff2" in orig_key:
  lowerCamelCase		:     List[Any]          =				orig_key.replace("""ff2""",     """output.dense"""							)
 if "ff" in orig_key:
  lowerCamelCase		:     Tuple          =				orig_key.replace("""ff""",     """output.dense"""							)
 if "mlm_class" in orig_key:
  lowerCamelCase		:     Dict          =				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		:     str          =				"""nystromformer.""" + orig_key

 return orig_key





def    _a (						lowerCamelCase,     lowerCamelCase							):
 for key in orig_state_dict.copy().keys():
  lowerCamelCase		:     List[str]          =				orig_state_dict.pop(lowerCamelCase							)

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

 lowerCamelCase		:     Tuple          =				orig_state_dict["""cls.predictions.decoder.bias"""]
 lowerCamelCase		:     List[Any]          =				(
     torch.arange(config.max_position_embeddings							).expand((1, -1)							) + 2
 )

 return orig_state_dict





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Tuple          =				torch.load(lowerCamelCase,     map_location="""cpu"""							)["""model_state_dict"""]
 lowerCamelCase		:     str          =				NystromformerConfig.from_json_file(lowerCamelCase							)
 lowerCamelCase		:     Tuple          =				NystromformerForMaskedLM(lowerCamelCase							)

 lowerCamelCase		:     int          =				convert_checkpoint_helper(lowerCamelCase,     lowerCamelCase							)

 model.load_state_dict(lowerCamelCase							)
 model.eval()
 model.save_pretrained(lowerCamelCase							)

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


if __name__ == "__main__":
      _lowerCamelCase        =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."""
      )
      _lowerCamelCase        =parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 # Initialise PyTorch model
 lowerCamelCase		:     List[str]          =				FNetConfig.from_json_file(lowerCamelCase							)
 print(F'''Building PyTorch model from configuration: {config}'''							)
 lowerCamelCase		:     Any          =				FNetForPreTraining(lowerCamelCase							)

 lowerCamelCase		:     List[Any]          =				restore_checkpoint(lowerCamelCase,     lowerCamelCase							)
 lowerCamelCase		:     List[str]          =				checkpoint_dict["""target"""]

 # Embeddings
 # Position IDs
 lowerCamelCase		:     Optional[Any]          =				fnet_pretraining_model.state_dict()

 lowerCamelCase		:     str          =				state_dict["""fnet.embeddings.position_ids"""]
 lowerCamelCase		:     List[str]          =				{"""fnet.embeddings.position_ids""": position_ids}
 # Embedding Layers
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]							)
 lowerCamelCase		:     List[Any]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]							)
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]							)
 lowerCamelCase		:     Optional[int]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]							).T
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]							)
 lowerCamelCase		:     str          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]							)
 lowerCamelCase		:     Optional[int]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]							)

 # Encoder Layers
 for layer in range(config.num_hidden_layers							):
  lowerCamelCase		:     List[Any]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]							)
  lowerCamelCase		:     Any          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]							)

  lowerCamelCase		:     int          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]							).T
  lowerCamelCase		:     List[str]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]							)

  lowerCamelCase		:     List[Any]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""kernel"""]							).T
  lowerCamelCase		:     str          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""bias"""]							)

  lowerCamelCase		:     Tuple          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]							)
  lowerCamelCase		:     Optional[int]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]							)

 # Pooler Layers
 lowerCamelCase		:     Tuple          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]							).T
 lowerCamelCase		:     List[Any]          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]							)

 # Masked LM Layers
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""kernel"""]							).T
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""bias"""]							)
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""scale"""]							)
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""bias"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""predictions_output"""]["""output_bias"""]							)
 lowerCamelCase		:     Any          =				torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]							)

 # Seq Relationship Layers
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_kernel"""]							)
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_bias"""]							)

 # Load State Dict
 fnet_pretraining_model.load_state_dict(lowerCamelCase							)

 # Save PreTrained
 print(F'''Saving pretrained model to {save_path}'''							)
 fnet_pretraining_model.save_pretrained(lowerCamelCase							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--fnet_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained FNet model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      _lowerCamelCase        =parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




