

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
snake_case_        =			data_utils.TransfoXLTokenizer
snake_case_        =			data_utils.TransfoXLCorpus
snake_case_        =			data_utils
snake_case_        =			data_utils

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





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

          lowercase__   :       List[str]				=	corpus.__dict__
          corpus_dict_no_vocab.pop('vocab'     ,					SCREAMING_SNAKE_CASE_      )
          lowercase__   :       Any				=	pytorch_dump_folder_path + '/' + CORPUS_NAME
          print(f"""Save dataset to {pytorch_dataset_dump_path}"""      )
          torch.save(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          lowercase__   :       Any				=	os.path.abspath(SCREAMING_SNAKE_CASE_      )
          lowercase__   :       Union[str, Any]				=	os.path.abspath(SCREAMING_SNAKE_CASE_      )

          print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""      )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               lowercase__   :       Any				=	TransfoXLConfig()
          else:
               lowercase__   :       List[Any]				=	TransfoXLConfig.from_json_file(SCREAMING_SNAKE_CASE_      )
          print(f"""Building PyTorch model from configuration: {config}"""      )
          lowercase__   :       int				=	TransfoXLLMHeadModel(SCREAMING_SNAKE_CASE_      )

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


if __name__ == "__main__":
       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.''',
       )
       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 warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)

