





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       :   Any					= data_utils.TransfoXLTokenizer
A       :   Tuple					= data_utils.TransfoXLCorpus
A       :   int					= data_utils
A       :   Union[str, Any]					= data_utils







def   __lowerCamelCase   (				__a  :List[str]       ,	__a  :List[Any]       ,	__a  :List[str]       ,	__a  :str       )						->							Any:






		"""simple docstring"""

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

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

				A__     =  load_tf_weights_in_transfo_xl(__a       ,	__a       ,	__a       )
				# Save pytorch-model
				A__     =  os.path.join(__a       ,	__a       )
				A__     =  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__":
					A       :   List[str]					= 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       :   List[str]					= 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_torch_available, is_vision_available


A       :   Optional[int]					= {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Optional[int]					= ['''Pix2StructImageProcessor''']


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= [
					    '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Pix2StructPreTrainedModel''',
					    '''Pix2StructForConditionalGeneration''',
					    '''Pix2StructVisionModel''',
					    '''Pix2StructTextModel''',
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

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



