






"""simple docstring"""




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Tuple		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	Optional[int]    )     ->   Union[str, Any]:
  # Initialise PyTorch model
  A__											=							BertConfig.from_pretrained(
      """bert-large-cased"""		,				vocab_size=UpperCamelCase		,				max_position_embeddings=512		,				is_decoder=UpperCamelCase		,				add_cross_attention=UpperCamelCase		,				)
  A__											=							bert_config.to_dict()
  del bert_config_dict["type_vocab_size"]
  A__											=							BertGenerationConfig(**UpperCamelCase    )
  if is_encoder:
    A__											=							BertGenerationEncoder(UpperCamelCase    )
  else:
    A__											=							BertGenerationDecoder(UpperCamelCase    )
  print(F"""Building PyTorch model from configuration: {config}"""    )

  # Load weights from tf checkpoint
  load_tf_weights_in_bert_generation(
      UpperCamelCase		,				UpperCamelCase		,				model_class="""bert"""		,				is_encoder_named_decoder=UpperCamelCase		,				is_encoder=UpperCamelCase		,				)

  # Save pytorch-model
  print(F"""Save PyTorch model and config to {pytorch_dump_path}"""    )
  model.save_pretrained(UpperCamelCase    )


if __name__ == "__main__":
      lowerCamelCase__			=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
      lowerCamelCase__			=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )












"""simple docstring"""




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 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any		,				UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	Dict    ):
  # Initialise PyTorch model
  A__											=							FNetConfig.from_json_file(UpperCamelCase    )
  print(F"""Building PyTorch model from configuration: {config}"""    )
  A__											=							FNetForPreTraining(UpperCamelCase    )

  A__											=							restore_checkpoint(UpperCamelCase		,				UpperCamelCase    )
  A__											=							checkpoint_dict["""target"""]

  # Embeddings
  # Position IDs
  A__											=							fnet_pretraining_model.state_dict()

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

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

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""intermediate"""]["""kernel"""]    ).T
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""intermediate"""]["""bias"""]    )

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""output"""]["""kernel"""]    ).T
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""output"""]["""bias"""]    )

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""output_layer_norm"""]["""scale"""]    )
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""output_layer_norm"""]["""bias"""]    )

  # Pooler Layers
  A__											=							torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]    ).T
  A__											=							torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]    )

  # Masked LM Layers
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_dense"""]["""kernel"""]    ).T
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_dense"""]["""bias"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_layer_norm"""]["""scale"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_layer_norm"""]["""bias"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_output"""]["""output_bias"""]    )
  A__											=							torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]    )

  # Seq Relationship Layers
  A__											=							torch.tensor(
      pretrained_model_params["""classification"""]["""output_kernel"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""classification"""]["""output_bias"""]    )

  # Load State Dict
  fnet_pretraining_model.load_state_dict(UpperCamelCase    )

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


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)




