"""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
UpperCAmelCase__  		=						data_utils.TransfoXLTokenizer
UpperCAmelCase__  		=						data_utils.TransfoXLCorpus
UpperCAmelCase__  		=						data_utils
UpperCAmelCase__  		=						data_utils






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase		):







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

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

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

				print(f'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''		)
				# Initialise PyTorch model
				if transfo_xl_config_file == "":
						_UpperCAmelCase						 =      TransfoXLConfig()
				else:
						_UpperCAmelCase						 =      TransfoXLConfig.from_json_file(lowercase		)
				print(f'''Building PyTorch model from configuration: {config}'''		)
				_UpperCAmelCase						 =      TransfoXLLMHeadModel(lowercase		)

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


if __name__ == "__main__":
				UpperCAmelCase__  		=						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.""",
				)
				UpperCAmelCase__  		=						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,
				)

"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__  		=						{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """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:
    UpperCAmelCase__  		=						[
        """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:
    UpperCAmelCase__  		=						[
        """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

    UpperCAmelCase__  		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
