






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

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

														__lowercase					:							Union[str, Any]			=   load_tf_weights_in_transfo_xl(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
														# Save pytorch-model
														__lowercase					:							List[str]			=   os.path.join(lowerCAmelCase_				,				lowerCAmelCase_					)
														__lowercase					:							Optional[int]			=   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       :       Dict        =				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       :       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 argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 					snake_case_						(   lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  int					):
							# Initialise PyTorch model.
							# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
							# TapasConfig to False.

							# initialize configuration from json file
							__lowercase					:							Optional[int]			=   TapasConfig.from_json_file(lowerCAmelCase_					)
							# set absolute/relative position embeddings parameter
							__lowercase					:							Optional[Any]			=   reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__lowercase					:							Union[str, Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WTQ":
														# run_task_main.py hparams
														__lowercase					:							List[Any]			=   4
														__lowercase					:							Union[str, Any]			=   True
														# hparam_utils.py hparams
														__lowercase					:							Any			=   0.664_694
														__lowercase					:							Tuple			=   0.207_951
														__lowercase					:							Dict			=   0.121_194
														__lowercase					:							List[str]			=   True
														__lowercase					:							str			=   True
														__lowercase					:							Dict			=   False
														__lowercase					:							Tuple			=   0.0_352_513

														__lowercase					:							List[Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__lowercase					:							Optional[int]			=   4
														__lowercase					:							int			=   False
														# hparam_utils.py hparams
														__lowercase					:							Tuple			=   36.4_519
														__lowercase					:							str			=   0.903_421
														__lowercase					:							List[Any]			=   222.088
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Tuple			=   True
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Optional[Any]			=   0.763_141

														__lowercase					:							str			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "TABFACT":
														__lowercase					:							List[Any]			=   TapasForSequenceClassification(config=lowerCAmelCase_					)
							elif task == "MLM":
														__lowercase					:							Optional[int]			=   TapasForMaskedLM(config=lowerCAmelCase_					)
							elif task == "INTERMEDIATE_PRETRAINING":
														__lowercase					:							Dict			=   TapasModel(config=lowerCAmelCase_					)
							else:
														raise ValueError(F"Task {task} not supported."					)

							print(F"Building PyTorch model from configuration: {config}"					)
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)

							# Save pytorch-model (weights and configuration)
							print(F"Save PyTorch model to {pytorch_dump_path}"					)
							model.save_pretrained(lowerCAmelCase_					)

							# Save tokenizer files
							print(F"Save tokenizer files to {pytorch_dump_path}"					)
							__lowercase					:							Any			=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""				,				model_max_length=512					)
							tokenizer.save_pretrained(lowerCAmelCase_					)

							print("""Used relative position embeddings:"""				,				model.config.reset_position_index_per_cell					)


if __name__ == "__main__":
	lowerCamelCase       :       List[Any]        =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
	)
	parser.add_argument(
	    '''--reset_position_index_per_cell''',
	    default=False,
	    action='''store_true''',
	    help='''Whether to use relative position embeddings or not. Defaults to True.''',
	)
	parser.add_argument(
	    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
	)
	parser.add_argument(
	    '''--tapas_config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained TAPAS model. \n'''
	        '''This specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	lowerCamelCase       :       int        =				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.task,
	    args.reset_position_index_per_cell,
	    args.tf_checkpoint_path,
	    args.tapas_config_file,
	    args.pytorch_dump_path,
	)