
"""simple docstring"""







import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
							from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm
def 					__lowerCAmelCase       (_UpperCamelCase	):
	if is_torch_version('<' ,						'2.0.0'	) or not hasattr(_UpperCamelCase ,						'_dynamo'	):
		return False
	return isinstance(_UpperCamelCase ,						torch._dynamo.eval_frame.OptimizedModule	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase = True	):
	__lowerCAmelCase		    =   (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

	__lowerCAmelCase		    =   is_compiled_module(_UpperCamelCase	)
	if is_compiled:
		__lowerCAmelCase		    =   model
		__lowerCAmelCase		    =   model._orig_mod

	if is_deepspeed_available():
		options += (DeepSpeedEngine,)

	while isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase		    =   model.module

	if not keep_fpaa_wrapper:
		__lowerCAmelCase		    =   getattr(_UpperCamelCase ,						'forward'	)
		__lowerCAmelCase		    =   model.__dict__.pop('_original_forward' ,						_UpperCamelCase	)
		if original_forward is not None:
			while hasattr(_UpperCamelCase ,						'__wrapped__'	):
				__lowerCAmelCase		    =   forward.__wrapped__
				if forward == original_forward:
					break
			__lowerCAmelCase		    =   forward
		if getattr(_UpperCamelCase ,						'_converted_to_transformer_engine' ,						_UpperCamelCase	):
			convert_model(_UpperCamelCase ,						to_transformer_engine=_UpperCamelCase	)

	if is_compiled:
		__lowerCAmelCase		    =   model
		__lowerCAmelCase		    =   compiled_model

	return model
def 					__lowerCAmelCase       ():
	PartialState().wait_for_everyone()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	if PartialState().distributed_type == DistributedType.TPU:
		xm.save(_UpperCamelCase ,						_UpperCamelCase	)
	elif PartialState().local_process_index == 0:
		torch.save(_UpperCamelCase ,						_UpperCamelCase	)
@contextmanager
def 					__lowerCAmelCase       (**_UpperCamelCase	):
	for key, value in kwargs.items():
		__lowerCAmelCase		    =   str(_UpperCamelCase	)

	yield

	for key in kwargs:
		if key.upper() in os.environ:
			del os.environ[key.upper()]
def 					__lowerCAmelCase       (_UpperCamelCase	):
	if not hasattr(_UpperCamelCase ,						'__qualname__'	) and not hasattr(_UpperCamelCase ,						'__name__'	):
		__lowerCAmelCase		    =   getattr(_UpperCamelCase ,						'__class__' ,						_UpperCamelCase	)
	if hasattr(_UpperCamelCase ,						'__qualname__'	):
		return obj.__qualname__
	if hasattr(_UpperCamelCase ,						'__name__'	):
		return obj.__name__
	return str(_UpperCamelCase	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	for key, value in source.items():
		if isinstance(_UpperCamelCase ,						_UpperCamelCase	):
			__lowerCAmelCase		    =   destination.setdefault(_UpperCamelCase ,						{}	)
			merge_dicts(_UpperCamelCase ,						_UpperCamelCase	)
		else:
			__lowerCAmelCase		    =   value

	return destination
def 					__lowerCAmelCase       (_UpperCamelCase = None	):
	if port is None:
		__lowerCAmelCase		    =   2_9500
	with socket.socket(socket.AF_INET ,						socket.SOCK_STREAM	) as s:
		return s.connect_ex(('localhost', port)	) == 0

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

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

		__lowerCAmelCase    :		Dict		    =   load_tf_weights_in_transfo_xl(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
		# Save pytorch-model
		__lowerCAmelCase    :		str		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
		__lowerCAmelCase    :		Tuple		    =   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__":
							lowerCamelCase__           =	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__           =	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,
							)