
'''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   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Optional[Any]            =  FNetConfig.from_json_file(__SCREAMING_SNAKE_CASE    )
    print(f'Building PyTorch model from configuration: {config}'    )
    snake_case_	:      Optional[int]            =  FNetForPreTraining(__SCREAMING_SNAKE_CASE    )

    snake_case_	:      Union[str, Any]            =  restore_checkpoint(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    )
    snake_case_	:      int            =  checkpoint_dict["target"]

    # Embeddings
    # Position IDs
    snake_case_	:      Union[str, Any]            =  fnet_pretraining_model.state_dict()

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

    # Encoder Layers
    for layer in range(config.num_hidden_layers    ):
        snake_case_	:      Union[str, Any]            =  torch.tensor(
            pretrained_model_params["encoder"][f'encoder_{layer}']["mixing_layer_norm"]["scale"]    )
        snake_case_	:      Optional[int]            =  torch.tensor(
            pretrained_model_params["encoder"][f'encoder_{layer}']["mixing_layer_norm"]["bias"]    )

        snake_case_	:      List[Any]            =  torch.tensor(
            pretrained_model_params["encoder"][f'feed_forward_{layer}']["intermediate"]["kernel"]    ).T
        snake_case_	:      Any            =  torch.tensor(
            pretrained_model_params["encoder"][f'feed_forward_{layer}']["intermediate"]["bias"]    )

        snake_case_	:      Dict            =  torch.tensor(
            pretrained_model_params["encoder"][f'feed_forward_{layer}']["output"]["kernel"]    ).T
        snake_case_	:      Optional[int]            =  torch.tensor(
            pretrained_model_params["encoder"][f'feed_forward_{layer}']["output"]["bias"]    )

        snake_case_	:      Optional[Any]            =  torch.tensor(
            pretrained_model_params["encoder"][f'encoder_{layer}']["output_layer_norm"]["scale"]    )
        snake_case_	:      Optional[Any]            =  torch.tensor(
            pretrained_model_params["encoder"][f'encoder_{layer}']["output_layer_norm"]["bias"]    )

    # Pooler Layers
    snake_case_	:      Dict            =  torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]    ).T
    snake_case_	:      Optional[Any]            =  torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]    )

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

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

    # Load State Dict
    fnet_pretraining_model.load_state_dict(__SCREAMING_SNAKE_CASE    )

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


if __name__ == "__main__":
  a_     =  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.")
  a_     =  parser.parse_args()
  convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

'''simple docstring'''
from typing import TYPE_CHECKING

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


a_     =  {
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  [
      "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SplinterForQuestionAnswering",
      "SplinterForPreTraining",
      "SplinterLayer",
      "SplinterModel",
      "SplinterPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
  from .tokenization_splinter import SplinterTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_splinter_fast import SplinterTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )


else:
  import sys

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




