
"""simple docstring"""







import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam




class 				A__ (			datasets.BeamBasedBuilder):

		def 							__lowerCamelCase							(   self				):
			return datasets.DatasetInfo(
			    features=datasets.Features({'content': datasets.Value('string'				)}				) ,	supervised_keys=_SCREAMING_SNAKE_CASE ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,	gen_kwargs={'examples': get_test_dummy_examples()}				)]







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			import apache_beam as beam

			return pipeline | "Load Examples" >> beam.Create(_SCREAMING_SNAKE_CASE				)




class 				A__ (			datasets.BeamBasedBuilder):

		def 							__lowerCamelCase							(   self				):
			return datasets.DatasetInfo(
			    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				) ,	supervised_keys=_SCREAMING_SNAKE_CASE ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			return [
			    datasets.SplitGenerator(name=datasets.Split.TRAIN ,	gen_kwargs={'examples': get_test_nested_examples()}				)
			]







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			import apache_beam as beam

			return pipeline | "Load Examples" >> beam.Create(_SCREAMING_SNAKE_CASE				)



def 					__lowerCAmelCase       ():
	return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar']	)]
def 					__lowerCAmelCase       ():
	return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar']	)]




class 				A__ (			_lowerCamelCase):

		@require_beam
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   len(get_test_dummy_examples()				)
			with tempfile.TemporaryDirectory() as tmp_cache_dir:
				__lowerCAmelCase    :		Dict		    =   DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE ,	beam_runner='DirectRunner'				)
				builder.download_and_prepare()
				self.assertTrue(
				    os.path.exists(
				        os.path.join(_SCREAMING_SNAKE_CASE ,	builder.name ,	'default' ,	'0.0.0' ,	f"{builder.name}-train.arrow"				)				)				)
				self.assertDictEqual(builder.info.features ,	datasets.Features({'content': datasets.Value('string'				)}				)				)
				__lowerCAmelCase    :		Union[str, Any]		    =   builder.as_dataset()
				self.assertEqual(dset['train'].num_rows ,	_SCREAMING_SNAKE_CASE				)
				self.assertEqual(dset['train'].info.splits['train'].num_examples ,	_SCREAMING_SNAKE_CASE				)
				self.assertDictEqual(dset['train'][0] ,	get_test_dummy_examples()[0][1]				)
				self.assertDictEqual(
				    dset['train'][expected_num_examples - 1] ,	get_test_dummy_examples()[expected_num_examples - 1][1]				)
				self.assertTrue(
				    os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,	builder.name ,	'default' ,	'0.0.0' ,	'dataset_info.json'				)				)				)
				del dset

		@require_beam
		def 							__lowerCamelCase							(   self				):
			import apache_beam as beam

			__lowerCAmelCase    :		Any		    =   beam.io.parquetio.WriteToParquet

			__lowerCAmelCase    :		Optional[int]		    =   len(get_test_dummy_examples()				)
			with tempfile.TemporaryDirectory() as tmp_cache_dir:
				__lowerCAmelCase    :		Optional[int]		    =   DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE ,	beam_runner='DirectRunner'				)
				with patch('apache_beam.io.parquetio.WriteToParquet'				) as write_parquet_mock:
					__lowerCAmelCase    :		int		    =   partial(_SCREAMING_SNAKE_CASE ,	num_shards=2				)
					builder.download_and_prepare()
				self.assertTrue(
				    os.path.exists(
				        os.path.join(
				            _SCREAMING_SNAKE_CASE ,	builder.name ,	'default' ,	'0.0.0' ,	f"{builder.name}-train-00000-of-00002.arrow"				)				)				)
				self.assertTrue(
				    os.path.exists(
				        os.path.join(
				            _SCREAMING_SNAKE_CASE ,	builder.name ,	'default' ,	'0.0.0' ,	f"{builder.name}-train-00000-of-00002.arrow"				)				)				)
				self.assertDictEqual(builder.info.features ,	datasets.Features({'content': datasets.Value('string'				)}				)				)
				__lowerCAmelCase    :		List[Any]		    =   builder.as_dataset()
				self.assertEqual(dset['train'].num_rows ,	_SCREAMING_SNAKE_CASE				)
				self.assertEqual(dset['train'].info.splits['train'].num_examples ,	_SCREAMING_SNAKE_CASE				)
				# Order is not preserved when sharding, so we just check that all the elements are there
				self.assertListEqual(sorted(dset['train']['content']				) ,	sorted(['foo', 'bar', 'foobar']				)				)
				self.assertTrue(
				    os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,	builder.name ,	'default' ,	'0.0.0' ,	'dataset_info.json'				)				)				)
				del dset

		@require_beam
		def 							__lowerCamelCase							(   self				):
			with tempfile.TemporaryDirectory() as tmp_cache_dir:
				__lowerCAmelCase    :		List[Any]		    =   DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE				)
				self.assertRaises(datasets.builder.MissingBeamOptions ,	builder.download_and_prepare				)







		@require_beam
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   len(get_test_nested_examples()				)
			with tempfile.TemporaryDirectory() as tmp_cache_dir:
				__lowerCAmelCase    :		Dict		    =   NestedBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE ,	beam_runner='DirectRunner'				)
				builder.download_and_prepare()
				self.assertTrue(
				    os.path.exists(
				        os.path.join(_SCREAMING_SNAKE_CASE ,	builder.name ,	'default' ,	'0.0.0' ,	f"{builder.name}-train.arrow"				)				)				)
				self.assertDictEqual(
				    builder.info.features ,	datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				)				)
				__lowerCAmelCase    :		List[str]		    =   builder.as_dataset()
				self.assertEqual(dset['train'].num_rows ,	_SCREAMING_SNAKE_CASE				)
				self.assertEqual(dset['train'].info.splits['train'].num_examples ,	_SCREAMING_SNAKE_CASE				)
				self.assertDictEqual(dset['train'][0] ,	get_test_nested_examples()[0][1]				)
				self.assertDictEqual(
				    dset['train'][expected_num_examples - 1] ,	get_test_nested_examples()[expected_num_examples - 1][1]				)
				self.assertTrue(
				    os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE ,	builder.name ,	'default' ,	'0.0.0' ,	'dataset_info.json'				)				)				)
				del dset

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

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

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