


import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
A      :    Union[str, Any]		=					data_utils.TransfoXLTokenizer
A      :    str		=					data_utils.TransfoXLCorpus
A      :    List[Any]		=					data_utils
A      :    Any		=					data_utils

def       lowercase_		(		_A			:    int    ,					_A			:    str    ,					_A			:    int    ,					_A			:    Optional[int]						):

      """simple docstring"""




      if transfo_xl_dataset_file:
            # Convert a pre-processed corpus (see original TensorFlow repo)
            with open(__lowerCAmelCase    ,					"rb"						) as fp:
                  lowerCamelCase__		:				List[Any]    =  pickle.load(__lowerCAmelCase    ,					encoding="latin1"						)
            # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
            lowerCamelCase__		:				List[str]    =  pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
            print(F"Save vocabulary to {pytorch_vocab_dump_path}"						)
            lowerCamelCase__		:				str    =  corpus.vocab.__dict__
            torch.save(__lowerCAmelCase    ,					__lowerCAmelCase						)

            lowerCamelCase__		:				Optional[Any]    =  corpus.__dict__
            corpus_dict_no_vocab.pop("vocab"    ,					__lowerCAmelCase						)
            lowerCamelCase__		:				str    =  pytorch_dump_folder_path + "/" + CORPUS_NAME
            print(F"Save dataset to {pytorch_dataset_dump_path}"						)
            torch.save(__lowerCAmelCase    ,					__lowerCAmelCase						)

      if tf_checkpoint_path:
            # Convert a pre-trained TensorFlow model
            lowerCamelCase__		:				List[Any]    =  os.path.abspath(__lowerCAmelCase						)
            lowerCamelCase__		:				List[Any]    =  os.path.abspath(__lowerCAmelCase						)

            print(F"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."						)
            # Initialise PyTorch model
            if transfo_xl_config_file == "":
                  lowerCamelCase__		:				Optional[int]    =  TransfoXLConfig()
            else:
                  lowerCamelCase__		:				str    =  TransfoXLConfig.from_json_file(__lowerCAmelCase						)
            print(F"Building PyTorch model from configuration: {config}"						)
            lowerCamelCase__		:				int    =  TransfoXLLMHeadModel(__lowerCAmelCase						)

            lowerCamelCase__		:				int    =  load_tf_weights_in_transfo_xl(__lowerCAmelCase    ,					__lowerCAmelCase    ,					__lowerCAmelCase						)
            # Save pytorch-model
            lowerCamelCase__		:				Any    =  os.path.join(__lowerCAmelCase    ,					__lowerCAmelCase						)
            lowerCamelCase__		:				Optional[Any]    =  os.path.join(__lowerCAmelCase    ,					__lowerCAmelCase						)
            print(F"Save PyTorch model to {os.path.abspath(__lowerCAmelCase						)}"						)
            torch.save(model.state_dict()    ,					__lowerCAmelCase						)
            print(F"Save configuration file to {os.path.abspath(__lowerCAmelCase						)}"						)
            with open(__lowerCAmelCase    ,					"w"    ,					encoding="utf-8"						) as f:
                  f.write(config.to_json_string()						)


if __name__ == "__main__":
    A      :    Any		=					argparse.ArgumentParser()
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the folder to store the PyTorch model or dataset/vocab.",
    )
    parser.add_argument(
        "--tf_checkpoint_path",
        default="",
        type=str,
        help="An optional path to a TensorFlow checkpoint path to be converted.",
    )
    parser.add_argument(
        "--transfo_xl_config_file",
        default="",
        type=str,
        help=(
            "An optional config json file corresponding to the pre-trained BERT model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--transfo_xl_dataset_file",
        default="",
        type=str,
        help="An optional dataset file to be converted in a vocabulary.",
    )
    A      :    List[Any]		=					parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "WavLMForAudioFrameClassification",
					    "WavLMForCTC",
					    "WavLMForSequenceClassification",
					    "WavLMForXVector",
					    "WavLMModel",
					    "WavLMPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_wavlm import (
										    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WavLMForAudioFrameClassification,
										    WavLMForCTC,
										    WavLMForSequenceClassification,
										    WavLMForXVector,
										    WavLMModel,
										    WavLMPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			int												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



