



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
__SCREAMING_SNAKE_CASE           =  data_utils.TransfoXLTokenizer
__SCREAMING_SNAKE_CASE           =  data_utils.TransfoXLCorpus
__SCREAMING_SNAKE_CASE           =  data_utils
__SCREAMING_SNAKE_CASE           =  data_utils




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

						A							:					Optional[int]              =						corpus.__dict__
						corpus_dict_no_vocab.pop("vocab" ,    _lowerCamelCase    )
						A							:					Optional[int]              =						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
						A							:					str              =						os.path.abspath(_lowerCamelCase    )
						A							:					Optional[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 == "":
									A							:					int              =						TransfoXLConfig()
						else:
									A							:					Dict              =						TransfoXLConfig.from_json_file(_lowerCamelCase    )
						print(f"""Building PyTorch model from configuration: {config}"""    )
						A							:					int              =						TransfoXLLMHeadModel(_lowerCamelCase    )

						A							:					str              =						load_tf_weights_in_transfo_xl(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
						# Save pytorch-model
						A							:					Optional[int]              =						os.path.join(_lowerCamelCase ,    _lowerCamelCase    )
						A							:					Tuple              =						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__":
							__SCREAMING_SNAKE_CASE           =  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.""",
							)
							__SCREAMING_SNAKE_CASE           =  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,
							)




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						RobertaPreLayerNormConfig.from_pretrained(
			    _lowerCamelCase ,    architectures=["RobertaPreLayerNormForMaskedLM"]    )

			# convert state_dict
			A							:					List[Any]              =						torch.load(hf_hub_download(repo_id=_lowerCamelCase ,    filename="pytorch_model.bin"    )    )
			A							:					Union[str, Any]              =						{}
			for tensor_key, tensor_value in original_state_dict.items():
						# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
						if tensor_key.startswith("roberta."    ):
									A							:					int              =						"roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

						# The original implementation contains weights which are not used, remove them from the state_dict
						if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
									continue

						A							:					Any              =						tensor_value

			A							:					Optional[int]              =						RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=_lowerCamelCase ,    config=_lowerCamelCase ,    state_dict=_lowerCamelCase    )
			model.save_pretrained(_lowerCamelCase    )

			# convert tokenizer
			A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(_lowerCamelCase    )
			tokenizer.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--checkpoint-repo""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)