





'''simple docstring'''







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
lowerCAmelCase_   :       Dict       =     data_utils.TransfoXLTokenizer
lowerCAmelCase_   :       Tuple       =     data_utils.TransfoXLCorpus
lowerCAmelCase_   :       int       =     data_utils
lowerCAmelCase_   :       Optional[Any]       =     data_utils






def 					_lowerCamelCase							(					lowercase		: Optional[Any]       ,				lowercase		: Dict       ,				lowercase		: Optional[Any]       ,				lowercase		: Union[str, Any]		)      ->      Union[str, Any]:





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

								_a				      =					corpus.__dict__
								corpus_dict_no_vocab.pop("vocab"       ,				A__		)
								_a				      =					pytorch_dump_folder_path + """/""" + CORPUS_NAME
								print(F'Save dataset to {pytorch_dataset_dump_path}'		)
								torch.save(A__       ,				A__		)

				if tf_checkpoint_path:
								# Convert a pre-trained TensorFlow model
								_a				      =					os.path.abspath(A__		)
								_a				      =					os.path.abspath(A__		)

								print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'		)
								# Initialise PyTorch model
								if transfo_xl_config_file == "":
												_a				      =					TransfoXLConfig()
								else:
												_a				      =					TransfoXLConfig.from_json_file(A__		)
								print(F'Building PyTorch model from configuration: {config}'		)
								_a				      =					TransfoXLLMHeadModel(A__		)

								_a				      =					load_tf_weights_in_transfo_xl(A__       ,				A__       ,				A__		)
								# Save pytorch-model
								_a				      =					os.path.join(A__       ,				A__		)
								_a				      =					os.path.join(A__       ,				A__		)
								print(F'Save PyTorch model to {os.path.abspath(A__		)}'		)
								torch.save(model.state_dict()       ,				A__		)
								print(F'Save configuration file to {os.path.abspath(A__		)}'		)
								with open(A__       ,				"w"       ,				encoding="utf-8"		) as f:
												f.write(config.to_json_string()		)


if __name__ == "__main__":
					lowerCAmelCase_   :       str       =     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.',
					)
					lowerCAmelCase_   :       Tuple       =     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,
					)




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A   :    str											= logging.get_logger(__name__)

__A   :    int											= {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roc_bert'

    def __init__(  self       :    str       ,		lowerCamelCase       :    Optional[Any]=3_05_22       ,		lowerCamelCase       :    List[Any]=7_68       ,		lowerCamelCase       :    int=12       ,		lowerCamelCase       :    str=12       ,		lowerCamelCase       :    Any=30_72       ,		lowerCamelCase       :    List[Any]="gelu"       ,		lowerCamelCase       :    Optional[int]=0.1       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    int=5_12       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    str=1E-12       ,		lowerCamelCase       :    int=True       ,		lowerCamelCase       :    Optional[Any]=0       ,		lowerCamelCase       :    List[Any]="absolute"       ,		lowerCamelCase       :    str=None       ,		lowerCamelCase       :    str=True       ,		lowerCamelCase       :    Tuple=True       ,		lowerCamelCase       :    str=7_68       ,		lowerCamelCase       :    str=9_10       ,		lowerCamelCase       :    Tuple=5_12       ,		lowerCamelCase       :    List[str]=2_48_58       ,		lowerCamelCase       :    Dict=True       ,		**lowerCamelCase       :    Tuple       ,		)  ->    Optional[int]:
      lowerCAmelCase_     : Optional[Any]     =  vocab_size
      lowerCAmelCase_     : Any     =  max_position_embeddings
      lowerCAmelCase_     : Any     =  hidden_size
      lowerCAmelCase_     : int     =  num_hidden_layers
      lowerCAmelCase_     : Tuple     =  num_attention_heads
      lowerCAmelCase_     : List[Any]     =  intermediate_size
      lowerCAmelCase_     : Tuple     =  hidden_act
      lowerCAmelCase_     : List[Any]     =  hidden_dropout_prob
      lowerCAmelCase_     : Dict     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Any     =  initializer_range
      lowerCAmelCase_     : Any     =  type_vocab_size
      lowerCAmelCase_     : Optional[int]     =  layer_norm_eps
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : List[Any]     =  enable_pronunciation
      lowerCAmelCase_     : List[Any]     =  enable_shape
      lowerCAmelCase_     : List[str]     =  pronunciation_embed_dim
      lowerCAmelCase_     : List[str]     =  pronunciation_vocab_size
      lowerCAmelCase_     : Dict     =  shape_embed_dim
      lowerCAmelCase_     : Optional[Any]     =  shape_vocab_size
      lowerCAmelCase_     : List[Any]     =  concat_input
      lowerCAmelCase_     : Any     =  position_embedding_type
      lowerCAmelCase_     : List[str]     =  classifier_dropout
      super().__init__(pad_token_id=lowerCamelCase       ,		**lowerCamelCase						)



