

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


A__        =			{
    "configuration_speech_to_text_2": ["SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2Text2Config"],
    "processing_speech_to_text_2": ["Speech2Text2Processor"],
    "tokenization_speech_to_text_2": ["Speech2Text2Tokenizer"],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__        =			[
      "SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Speech2Text2ForCausalLM",
      "Speech2Text2PreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

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





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       (					__A ,       __A ,       __A	):
    # Initialise PyTorch model
    SCREAMING_SNAKE_CASE__					     =						FNetConfig.from_json_file(__A	)
    print(f"Building PyTorch model from configuration: {config}"	)
    SCREAMING_SNAKE_CASE__					     =						FNetForPreTraining(__A	)

    SCREAMING_SNAKE_CASE__					     =						restore_checkpoint(__A ,       __A	)
    SCREAMING_SNAKE_CASE__					     =						checkpoint_dict["target"]

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

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

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

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

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

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

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

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

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

    # Load State Dict
    fnet_pretraining_model.load_state_dict(__A	)

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


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)


