

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

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

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










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase__ :   Union[str, Any]					=							{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Any					=							['LayoutLMv2TokenizerFast']

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Dict					=							['LayoutLMv2FeatureExtractor']
   lowerCamelCase__ :   Dict					=							['LayoutLMv2ImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Union[str, Any]					=							[
       'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'LayoutLMv2ForQuestionAnswering',
       'LayoutLMv2ForSequenceClassification',
       'LayoutLMv2ForTokenClassification',
       'LayoutLMv2Layer',
       'LayoutLMv2Model',
       'LayoutLMv2PreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
   from .processing_layoutlmva import LayoutLMvaProcessor
   from .tokenization_layoutlmva import LayoutLMvaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlmva import (
          LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaLayer,
          LayoutLMvaModel,
          LayoutLMvaPreTrainedModel,
      )
else:
   import sys

   lowerCamelCase__ :   Any					=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)