


import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
   import torch


lowercase__				     =		logging.get_logger(__name__)





@dataclass
class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase=False     ,							__UpperCamelCase=False     ,							__UpperCamelCase=6.0     ,							__UpperCamelCase=None     ,							__UpperCamelCase=False     ,							__UpperCamelCase=False     ,							__UpperCamelCase=None     ,							__UpperCamelCase="fp4"     ,							__UpperCamelCase=False     ,							**__UpperCamelCase     ,							)       ->    Dict:
          _a						=		load_in_abit
          _a						=		load_in_abit
          _a						=		llm_inta_threshold
          _a						=		llm_inta_skip_modules
          _a						=		llm_inta_enable_fpaa_cpu_offload
          _a						=		llm_inta_has_fpaa_weight
          _a						=		bnb_abit_quant_type
          _a						=		bnb_abit_use_double_quant

          if bnb_abit_compute_dtype is None:
               _a						=		torch.floataa
          elif isinstance(__UpperCamelCase     ,							__UpperCamelCase		):
               _a						=		getattr(__UpperCamelCase     ,							__UpperCamelCase		)
          elif isinstance(__UpperCamelCase     ,							torch.dtype		):
               _a						=		bnb_abit_compute_dtype
          else:
               raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype"		)

          self.post_init()






     def  a_					(			self		)       ->    int:
          if not isinstance(self.llm_inta_threshold     ,							__UpperCamelCase		):
               raise ValueError("llm_int8_threshold must be a float"		)

          if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules     ,							__UpperCamelCase		):
               raise ValueError("llm_int8_skip_modules must be a list of strings"		)
          if not isinstance(self.llm_inta_enable_fpaa_cpu_offload     ,							__UpperCamelCase		):
               raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean"		)

          if not isinstance(self.llm_inta_has_fpaa_weight     ,							__UpperCamelCase		):
               raise ValueError("llm_int8_has_fp16_weight must be a boolean"		)

          if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype     ,							torch.dtype		):
               raise ValueError("bnb_4bit_compute_dtype must be torch.dtype"		)

          if not isinstance(self.bnb_abit_quant_type     ,							__UpperCamelCase		):
               raise ValueError("bnb_4bit_quant_type must be a string"		)

          if not isinstance(self.bnb_abit_use_double_quant     ,							__UpperCamelCase		):
               raise ValueError("bnb_4bit_use_double_quant must be a boolean"		)

          if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes"		)		) >= version.parse(
              "0.39.0"		):
               raise ValueError(
                   "4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"		)






     def  a_					(			self		)       ->    Any:
          return self.load_in_abit or self.load_in_abit






     def  a_					(			self		)       ->    str:
          if self.load_in_abit:
               return "llm_int8"
          elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
               return "fp4"
          elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
               return "nf4"
          else:
               return None






     @classmethod
     def  a_					(			cls     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Optional[int]:
          _a						=		cls(**__UpperCamelCase		)

          _a						=		[]
          for key, value in kwargs.items():
               if hasattr(__UpperCamelCase     ,							__UpperCamelCase		):
                    setattr(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)
                    to_remove.append(__UpperCamelCase		)
          for key in to_remove:
               kwargs.pop(__UpperCamelCase     ,							__UpperCamelCase		)

          if return_unused_kwargs:
               return config, kwargs
          else:
               return config






     def  a_					(			self     ,							__UpperCamelCase		)       ->    int:
          with open(__UpperCamelCase     ,							"w"     ,							encoding="utf-8"		) as writer:
               _a						=		self.to_dict()
               _a						=		json.dumps(__UpperCamelCase     ,							indent=2     ,							sort_keys=__UpperCamelCase		) + "\n"

               writer.write(__UpperCamelCase		)






     def  a_					(			self		)       ->    Dict[str, Any]:
          _a						=		copy.deepcopy(self.__dict__		)
          _a						=		str(output["bnb_4bit_compute_dtype"]		).split("."		)[1]

          return output






     def __repr__(			self		)       ->    List[Any]:
          return f"{self.__class__.__name__} {self.to_json_string()}"






     def  a_					(			self     ,							__UpperCamelCase = True		)       ->    str:
          if use_diff is True:
               _a						=		self.to_diff_dict()
          else:
               _a						=		self.to_dict()
          return json.dumps(__UpperCamelCase     ,							indent=2     ,							sort_keys=__UpperCamelCase		) + "\n"






     def  a_					(			self		)       ->    Dict[str, Any]:
          _a						=		self.to_dict()

          # get the default config dict
          _a						=		BitsAndBytesConfig().to_dict()

          _a						=		{}

          # only serialize values that differ from the default config
          for key, value in config_dict.items():
               if value != default_config_dict[key]:
                    _a						=		value

          return serializable_config_dict





'''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
lowercase__				     =		data_utils.TransfoXLTokenizer
lowercase__				     =		data_utils.TransfoXLCorpus
lowercase__				     =		data_utils
lowercase__				     =		data_utils






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: Tuple				)      ->     Dict:







     '''simple docstring'''






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

          _a						=		corpus.__dict__
          corpus_dict_no_vocab.pop("vocab"      ,  __lowerCamelCase				)
          _a						=		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						=		os.path.abspath(__lowerCamelCase				)
          _a						=		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						=		TransfoXLConfig()
          else:
               _a						=		TransfoXLConfig.from_json_file(__lowerCamelCase				)
          print(F"Building PyTorch model from configuration: {config}"				)
          _a						=		TransfoXLLMHeadModel(__lowerCamelCase				)

          _a						=		load_tf_weights_in_transfo_xl(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)
          # Save pytorch-model
          _a						=		os.path.join(__lowerCamelCase      ,  __lowerCamelCase				)
          _a						=		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__":
   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,
   )

