




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A								=       {
    "configuration_groupvit": [
        "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "GroupViTConfig",
        "GroupViTOnnxConfig",
        "GroupViTTextConfig",
        "GroupViTVisionConfig",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A								=       [
					    "GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "GroupViTModel",
					    "GroupViTPreTrainedModel",
					    "GroupViTTextModel",
					    "GroupViTVisionModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A								=       [
					    "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFGroupViTModel",
					    "TFGroupViTPreTrainedModel",
					    "TFGroupViTTextModel",
					    "TFGroupViTVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_groupvit import (
					    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    GroupViTConfig,
					    GroupViTOnnxConfig,
					    GroupViTTextConfig,
					    GroupViTVisionConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_groupvit import (
										    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GroupViTModel,
										    GroupViTPreTrainedModel,
										    GroupViTTextModel,
										    GroupViTVisionModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_groupvit import (
										    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFGroupViTModel,
										    TFGroupViTPreTrainedModel,
										    TFGroupViTTextModel,
										    TFGroupViTVisionModel,
										)

else:
					import sys

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








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
__A								=       data_utils.TransfoXLTokenizer
__A								=       data_utils.TransfoXLCorpus
__A								=       data_utils
__A								=       data_utils







def      lowerCamelCase_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: str					,	UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: Tuple )	->			Tuple:





				"""simple docstring"""


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

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

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


