
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_						: Optional[int]         =  data_utils.TransfoXLTokenizer
lowerCamelCase_						: Tuple         =  data_utils.TransfoXLCorpus
lowerCamelCase_						: List[Any]         =  data_utils
lowerCamelCase_						: Optional[int]         =  data_utils







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

								UpperCamelCase_:							List[str]	=						corpus.__dict__
								corpus_dict_no_vocab.pop("""vocab"""     ,    lowerCamelCase						)
								UpperCamelCase_:							Optional[Any]	=						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
								UpperCamelCase_:							List[Any]	=						os.path.abspath(lowerCamelCase						)
								UpperCamelCase_:							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 == "":
												UpperCamelCase_:							Optional[Any]	=						TransfoXLConfig()
								else:
												UpperCamelCase_:							List[str]	=						TransfoXLConfig.from_json_file(lowerCamelCase						)
								print(F'''Building PyTorch model from configuration: {config}'''						)
								UpperCamelCase_:							Optional[Any]	=						TransfoXLLMHeadModel(lowerCamelCase						)

								UpperCamelCase_:							int	=						load_tf_weights_in_transfo_xl(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)
								# Save pytorch-model
								UpperCamelCase_:							List[Any]	=						os.path.join(lowerCamelCase     ,    lowerCamelCase						)
								UpperCamelCase_:							List[str]	=						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__":
							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_						: Union[str, 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,
							)








import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowerCamelCase_						: Optional[int]         =  HUGGINGFACE_HUB_CACHE


lowerCamelCase_						: List[str]         =  """config.json"""
lowerCamelCase_						: Any         =  """diffusion_pytorch_model.bin"""
lowerCamelCase_						: Union[str, Any]         =  """diffusion_flax_model.msgpack"""
lowerCamelCase_						: Dict         =  """model.onnx"""
lowerCamelCase_						: List[Any]         =  """diffusion_pytorch_model.safetensors"""
lowerCamelCase_						: Optional[Any]         =  """weights.pb"""
lowerCamelCase_						: Optional[Any]         =  """https://huggingface.co"""
lowerCamelCase_						: Union[str, Any]         =  default_cache_path
lowerCamelCase_						: Tuple         =  """diffusers_modules"""
lowerCamelCase_						: Optional[Any]         =  os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowerCamelCase_						: str         =  ["""fp16""", """non-ema"""]
lowerCamelCase_						: List[Any]         =  """.self_attn"""






