


"""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    a__       ( __lowercase   ,	__lowercase   ,	__lowercase      )		->		Optional[Any]:
				# Initialise PyTorch model
				_A         =	FNetConfig.from_json_file(__lowercase      )
				print(f"""Building PyTorch model from configuration: {config}"""      )
				_A         =	FNetForPreTraining(__lowercase      )

				_A         =	restore_checkpoint(__lowercase   ,	__lowercase      )
				_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(__lowercase      )

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


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"""


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_											=			get_logger(__name__)


if TYPE_CHECKING:
		from .builder import DatasetBuilder






def    a__       ( __lowercase   ,	__lowercase = None      )		->		Optional[Any]:
				_A         =	importlib.import_module(__lowercase      )

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

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

								_A         =	"wrap_auth"
								return wrapper

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






def    a__       ( __lowercase      )		->		str:
				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(__lowercase   ,	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(__lowercase      ).__mro__[1:]  # make sure it's not the same module we've already patched
				    if issubclass(cls   ,	__lowercase      ) 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(__lowercase   ,	use_auth_token=builder.use_auth_token      )