





import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


__A       : int						    = get_logger(__name__)


if TYPE_CHECKING:
 from .builder import DatasetBuilder





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE = None							)       ->   Any:




    """simple docstring"""




    _A    =       importlib.import_module(_SCREAMING_SNAKE_CASE							)

    # TODO(QL): always update the module to add subsequent new authentication
    if hasattr(_SCREAMING_SNAKE_CASE       ,    '_patched_for_streaming'							) and module._patched_for_streaming:
        return

    def wrap_auth(_SCREAMING_SNAKE_CASE							):
        @wraps(_SCREAMING_SNAKE_CASE							)
        def wrapper(*_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE							):
            return function(*_SCREAMING_SNAKE_CASE       ,    use_auth_token=_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE							)

        _A    =       'wrap_auth'
        return wrapper

    # open files in a streaming fashion
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'open'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.listdir'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.walk'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'glob.glob'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    # allow to navigate in remote zip files
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.path.join'       ,    _SCREAMING_SNAKE_CASE							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.path.dirname'       ,    _SCREAMING_SNAKE_CASE							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.path.basename'       ,    _SCREAMING_SNAKE_CASE							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.path.relpath'       ,    _SCREAMING_SNAKE_CASE							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.path.split'       ,    _SCREAMING_SNAKE_CASE							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.path.splitext'       ,    _SCREAMING_SNAKE_CASE							).start()
    # allow checks on paths
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.path.exists'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.path.isdir'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.path.isfile'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'os.path.getsize'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'pathlib.Path'       ,    _SCREAMING_SNAKE_CASE							).start()
    # file readers
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'gzip.open'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'numpy.load'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'pandas.read_csv'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)       ,    attrs=['__version__']							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'pandas.read_excel'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)       ,    attrs=['__version__']							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'scipy.io.loadmat'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)       ,    attrs=['__version__']							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'xml.etree.ElementTree.parse'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    patch_submodule(_SCREAMING_SNAKE_CASE       ,    'xml.dom.minidom.parse'       ,    wrap_auth(_SCREAMING_SNAKE_CASE							)							).start()
    _A    =       True





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    extend_module_for_streaming(builder.__module__       ,    use_auth_token=builder.use_auth_token							)
    # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
    if not builder.__module__.startswith('datasets.'							):  # check that it's not a packaged builder like csv
        for imports in get_imports(inspect.getfile(builder.__class__							)							):
            if imports[0] == "internal":
                _A    =       imports[1]
                _A    =       '.'.join(builder.__module__.split('.'							)[:-1] + [internal_import_name]							)
                extend_module_for_streaming(_SCREAMING_SNAKE_CASE       ,    use_auth_token=builder.use_auth_token							)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
    from .builder import DatasetBuilder

    _A    =       [
        cls.__module__
        for cls in type(_SCREAMING_SNAKE_CASE							).__mro__[1:]  # make sure it's not the same module we've already patched
        if issubclass(cls       ,    _SCREAMING_SNAKE_CASE							) and cls.__module__ != DatasetBuilder.__module__
    ]  # check it's not a standard builder from datasets.builder
    for module in parent_builder_modules:
        extend_module_for_streaming(_SCREAMING_SNAKE_CASE       ,    use_auth_token=builder.use_auth_token							)












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 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[str]:




    """simple docstring"""




    _A    =       FNetConfig.from_json_file(_SCREAMING_SNAKE_CASE							)
    print(F"Building PyTorch model from configuration: {config}"							)
    _A    =       FNetForPreTraining(_SCREAMING_SNAKE_CASE							)

    _A    =       restore_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
    _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(_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       : str						    = 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       : Any						    = parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





