



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
_lowercase						  =		data_utils.TransfoXLTokenizer
_lowercase						  =		data_utils.TransfoXLCorpus
_lowercase						  =		data_utils
_lowercase						  =		data_utils



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       Any ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       Dict	)->  Optional[int]:






    if transfo_xl_dataset_file:
        # Convert a pre-processed corpus (see original TensorFlow repo)
        with open(UpperCAmelCase__ ,       '''rb'''	) as fp:
            A__		 =					pickle.load(UpperCAmelCase__ ,       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(UpperCAmelCase__ ,       UpperCAmelCase__	)

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

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

        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(UpperCAmelCase__	)
        print(f"Building PyTorch model from configuration: {config}"	)
        A__		 =					TransfoXLLMHeadModel(UpperCAmelCase__	)

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


if __name__ == "__main__":
   _lowercase						  =		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.",
   )
   _lowercase						  =		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 jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		"T5Config"



def 		lowerCAmelCase__    (					UpperCamelCase_				:       jnp.array ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       int	)->  jnp.ndarray:
    A__		 =					jnp.zeros_like(UpperCamelCase_	)
    A__		 =					shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]	)
    A__		 =					shifted_input_ids.at[:, 0].set(UpperCamelCase_	)

    A__		 =					jnp.where(shifted_input_ids == -1_0_0 ,       UpperCamelCase_ ,       UpperCamelCase_	)
    return shifted_input_ids



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig
