




import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__A								=       get_tests_dir("fixtures/test_sentencepiece.model")







@require_sentencepiece
class        __lowerCAmelCase					(			__magic_name__     ,							unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			XLMProphetNetTokenizer
			snake_case_  						=			False
			snake_case_  						=			True


			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							super().setUp()

							# We have a SentencePiece fixture for testing
							__lowerCamelCase									=       XLMProphetNetTokenizer(lowerCamelCase__     ,		keep_accents=lowerCamelCase__				)
							tokenizer.save_pretrained(self.tmpdirname				)


			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       '[PAD]'
							__lowerCamelCase									=       0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__				)     ,		lowerCamelCase__				)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__				)     ,		lowerCamelCase__				)


			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       list(self.get_tokenizer().get_vocab().keys()				)

							self.assertEqual(vocab_keys[0]     ,		'[PAD]'				)
							self.assertEqual(vocab_keys[1]     ,		'[CLS]'				)
							self.assertEqual(vocab_keys[-1]     ,		'j'				)
							self.assertEqual(len(lowerCamelCase__				)     ,		1_012				)


			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							self.assertEqual(self.get_tokenizer().vocab_size     ,		1_012				)


			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       XLMProphetNetTokenizer(lowerCamelCase__     ,		keep_accents=lowerCamelCase__				)

							__lowerCamelCase									=       tokenizer.tokenize('This is a test'				)
							self.assertListEqual(lowerCamelCase__     ,		['▁This', '▁is', '▁a', '▁t', 'est']				)

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(lowerCamelCase__				)     ,		[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]     ,		)

							__lowerCamelCase									=       tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
							self.assertListEqual(
							    lowerCamelCase__     ,		[
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '9',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        'é',
							        '.',
							    ]     ,		)
							__lowerCamelCase									=       tokenizer.convert_tokens_to_ids(lowerCamelCase__				)
							self.assertListEqual(
							    lowerCamelCase__     ,		[
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
							    ]     ,		)

							__lowerCamelCase									=       tokenizer.convert_ids_to_tokens(lowerCamelCase__				)
							self.assertListEqual(
							    lowerCamelCase__     ,		[
							        SPIECE_UNDERLINE + 'I',
							        SPIECE_UNDERLINE + 'was',
							        SPIECE_UNDERLINE + 'b',
							        'or',
							        'n',
							        SPIECE_UNDERLINE + 'in',
							        SPIECE_UNDERLINE + '',
							        '[UNK]',
							        '2',
							        '0',
							        '0',
							        '0',
							        ',',
							        SPIECE_UNDERLINE + 'and',
							        SPIECE_UNDERLINE + 'this',
							        SPIECE_UNDERLINE + 'is',
							        SPIECE_UNDERLINE + 'f',
							        'al',
							        's',
							        '[UNK]',
							        '.',
							    ]     ,		)


			@cached_property
			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'				)


			@slow
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       'Hello World!'
							__lowerCamelCase									=       [35_389, 6_672, 49, 2]
							self.assertListEqual(lowerCamelCase__     ,		self.big_tokenizer.encode(lowerCamelCase__				)				)






			@slow
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							# fmt: off
							__lowerCamelCase									=       {'input_ids': [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=lowerCamelCase__     ,		model_name='microsoft/xprophetnet-large-wiki100-cased'     ,		revision='1acad1643ddd54a44df6a1b797ada8373685d90e'     ,		)








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,
					)


