




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   :						int				=			data_utils.TransfoXLTokenizer
__lowerCamelCase   :						Union[str, Any]				=			data_utils.TransfoXLCorpus
__lowerCamelCase   :						List[Any]				=			data_utils
__lowerCamelCase   :						Any				=			data_utils







def 		lowercase__   (		__A:	Dict							,__A:	Any							,__A:	Optional[int]							,__A:	List[str]				):





					'''simple docstring'''






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

										__magic_name__							:	Optional[int]    							=							corpus.__dict__
										corpus_dict_no_vocab.pop('''vocab'''							,__A				)
										__magic_name__							:	Dict    							=							pytorch_dump_folder_path + '''/''' + CORPUS_NAME
										print(F'''Save dataset to {pytorch_dataset_dump_path}'''				)
										torch.save(__A							,__A				)

					if tf_checkpoint_path:
										# Convert a pre-trained TensorFlow model
										__magic_name__							:	Dict    							=							os.path.abspath(__A				)
										__magic_name__							:	str    							=							os.path.abspath(__A				)

										print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''				)
										# Initialise PyTorch model
										if transfo_xl_config_file == "":
															__magic_name__							:	Any    							=							TransfoXLConfig()
										else:
															__magic_name__							:	str    							=							TransfoXLConfig.from_json_file(__A				)
										print(F'''Building PyTorch model from configuration: {config}'''				)
										__magic_name__							:	List[str]    							=							TransfoXLLMHeadModel(__A				)

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


if __name__ == "__main__":
							__lowerCamelCase   :						Optional[Any]				=			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,
							)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase   :						List[str]				=			{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Dict				=			[
	    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RobertaPreLayerNormForCausalLM''',
	    '''RobertaPreLayerNormForMaskedLM''',
	    '''RobertaPreLayerNormForMultipleChoice''',
	    '''RobertaPreLayerNormForQuestionAnswering''',
	    '''RobertaPreLayerNormForSequenceClassification''',
	    '''RobertaPreLayerNormForTokenClassification''',
	    '''RobertaPreLayerNormModel''',
	    '''RobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRobertaPreLayerNormForCausalLM''',
	    '''TFRobertaPreLayerNormForMaskedLM''',
	    '''TFRobertaPreLayerNormForMultipleChoice''',
	    '''TFRobertaPreLayerNormForQuestionAnswering''',
	    '''TFRobertaPreLayerNormForSequenceClassification''',
	    '''TFRobertaPreLayerNormForTokenClassification''',
	    '''TFRobertaPreLayerNormMainLayer''',
	    '''TFRobertaPreLayerNormModel''',
	    '''TFRobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''FlaxRobertaPreLayerNormForCausalLM''',
	    '''FlaxRobertaPreLayerNormForMaskedLM''',
	    '''FlaxRobertaPreLayerNormForMultipleChoice''',
	    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
	    '''FlaxRobertaPreLayerNormForSequenceClassification''',
	    '''FlaxRobertaPreLayerNormForTokenClassification''',
	    '''FlaxRobertaPreLayerNormModel''',
	    '''FlaxRobertaPreLayerNormPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Dict				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



