



import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase		:				int      		=get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
		from transformers.models.mbart.modeling_mbart import shift_tokens_right

_lowercase		:				List[str]      		=25_0004
_lowercase		:				Optional[int]      		=25_0020

@require_sentencepiece
@require_tokenizers
class        snake_case__    (A__ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:str      =					MBartTokenizer
						__lowerCAmelCase					:Union[str, Any]      =					MBartTokenizerFast
						__lowerCAmelCase					:Any      =					True
						__lowerCAmelCase					:Any      =					True

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									super().setUp()

									# We have a SentencePiece fixture for testing
									a__       :						Optional[int]							=  MBartTokenizer(__lowercase ,       keep_accents=__lowercase  )
									tokenizer.save_pretrained(self.tmpdirname  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  MBartTokenizer(__lowercase ,       keep_accents=__lowercase  )

									a__       :						Optional[int]							=  tokenizer.tokenize("""This is a test"""  )
									self.assertListEqual(__lowercase ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(__lowercase  ) ,       [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] ,       )

									a__       :						int							=  tokenizer.tokenize("""I was born in 92000, and this is falsé."""  )
									self.assertListEqual(
									    __lowercase ,       [
									        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""",
									        """.""",
									    ] ,       )
									a__       :						Optional[int]							=  tokenizer.convert_tokens_to_ids(__lowercase  )
									self.assertListEqual(
									    __lowercase ,       [
									        value + tokenizer.fairseq_offset
									        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
									        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
									    ] ,       )

									a__       :						List[str]							=  tokenizer.convert_ids_to_tokens(__lowercase  )
									self.assertListEqual(
									    __lowercase ,       [
									        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>""",
									        """.""",
									    ] ,       )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									if not self.test_slow_tokenizer:
												# as we don't have a slow version, we can't compare the outputs between slow and fast versions
												return

									a__       :						Optional[Any]							=  (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
															a__       :						Tuple							=  self.rust_tokenizer_class.from_pretrained(__lowercase ,       **__lowercase  )
															a__       :						int							=  self.tokenizer_class.from_pretrained(__lowercase ,       **__lowercase  )

															a__       :						Optional[int]							=  tempfile.mkdtemp()

															a__       :						Union[str, Any]							=  tokenizer_r.save_pretrained(__lowercase  )
															a__       :						int							=  tokenizer_p.save_pretrained(__lowercase  )

															# Checks it save with the same files + the tokenizer.json file for the fast one
															self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files  )  )
															a__       :						Optional[int]							=  tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f  )
															self.assertSequenceEqual(__lowercase ,       __lowercase  )

															# Checks everything loads correctly in the same way
															a__       :						List[Any]							=  tokenizer_r.from_pretrained(__lowercase  )
															a__       :						Any							=  tokenizer_p.from_pretrained(__lowercase  )

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__lowercase ,       __lowercase  )  )
																		# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																		# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

															shutil.rmtree(__lowercase  )

															# Save tokenizer rust, legacy_format=True
															a__       :						List[Any]							=  tempfile.mkdtemp()

															a__       :						Optional[Any]							=  tokenizer_r.save_pretrained(__lowercase ,       legacy_format=__lowercase  )
															a__       :						List[str]							=  tokenizer_p.save_pretrained(__lowercase  )

															# Checks it save with the same files
															self.assertSequenceEqual(__lowercase ,       __lowercase  )

															# Checks everything loads correctly in the same way
															a__       :						Union[str, Any]							=  tokenizer_r.from_pretrained(__lowercase  )
															a__       :						Dict							=  tokenizer_p.from_pretrained(__lowercase  )

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__lowercase ,       __lowercase  )  )

															shutil.rmtree(__lowercase  )

															# Save tokenizer rust, legacy_format=False
															a__       :						List[Any]							=  tempfile.mkdtemp()

															a__       :						List[str]							=  tokenizer_r.save_pretrained(__lowercase ,       legacy_format=__lowercase  )
															a__       :						List[Any]							=  tokenizer_p.save_pretrained(__lowercase  )

															# Checks it saved the tokenizer.json file
															self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files  )  )

															# Checks everything loads correctly in the same way
															a__       :						Tuple							=  tokenizer_r.from_pretrained(__lowercase  )
															a__       :						Tuple							=  tokenizer_p.from_pretrained(__lowercase  )

															# Check special tokens are set accordingly on Rust and Python
															for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__lowercase ,       __lowercase  )  )

															shutil.rmtree(__lowercase  )




@require_torch
@require_sentencepiece
@require_tokenizers
class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[Any]      =					"facebook/mbart-large-en-ro"
						__lowerCAmelCase					:Any      =					[
						    " UN Chief Says There Is No Military Solution in Syria",
						    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
						]
						__lowerCAmelCase					:Tuple      =					[
						    "Şeful ONU declară că nu există o soluţie militară în Siria",
						    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
						    " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
						    " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
						]
						__lowerCAmelCase					:str      =					[8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]

						@classmethod
						def 							SCREAMING_SNAKE_CASE__(       cls  ) ->				str:
									"""simple docstring"""




									a__       :						MBartTokenizer							=  MBartTokenizer.from_pretrained(
									    cls.checkpoint_name ,       src_lang="""en_XX""" ,       tgt_lang="""ro_RO"""  )
									a__       :						Tuple							=  1
									return cls

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""] ,       2_5_0_0_0_1  )
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""] ,       2_5_0_0_0_4  )
									self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""] ,       2_5_0_0_2_0  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									a__       :						Dict							=  self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
									self.assertListEqual(self.expected_src_tokens ,       __lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									self.assertIn(__lowercase ,       self.tokenizer.all_special_ids  )
									a__       :						str							=  [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
									a__       :						List[Any]							=  self.tokenizer.decode(__lowercase ,       skip_special_tokens=__lowercase  )
									a__       :						Union[str, Any]							=  self.tokenizer.decode(generated_ids[1:] ,       skip_special_tokens=__lowercase  )
									self.assertEqual(__lowercase ,       __lowercase  )
									self.assertNotIn(self.tokenizer.eos_token ,       __lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						Any							=  ["""this is gunna be a long sentence """ * 2_0]
									assert isinstance(src_text[0] ,       __lowercase  )
									a__       :						List[str]							=  1_0
									a__       :						Dict							=  self.tokenizer(__lowercase ,       max_length=__lowercase ,       truncation=__lowercase  ).input_ids[0]
									self.assertEqual(ids[-2] ,       2  )
									self.assertEqual(ids[-1] ,       __lowercase  )
									self.assertEqual(len(__lowercase  ) ,       __lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]  ) ,       [2_5_0_0_2_6, 2_5_0_0_0_1]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									a__       :						Any							=  tempfile.mkdtemp()
									a__       :						Any							=  self.tokenizer.fairseq_tokens_to_ids
									self.tokenizer.save_pretrained(__lowercase  )
									a__       :						str							=  MBartTokenizer.from_pretrained(__lowercase  )
									self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,       __lowercase  )

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Tuple							=  self.tokenizer(self.src_text ,       text_target=self.tgt_text ,       padding=__lowercase ,       return_tensors="""pt"""  )
									a__       :						str							=  shift_tokens_right(batch["""labels"""] ,       self.tokenizer.pad_token_id  )

									# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
									assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
									assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
									assert batch.decoder_input_ids[1][-1] == 2
									assert batch.labels[1][-2:].tolist() == [2, RO_CODE]

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  self.tokenizer(
									    self.src_text ,       text_target=self.tgt_text ,       padding=__lowercase ,       truncation=__lowercase ,       max_length=len(self.expected_src_tokens  ) ,       return_tensors="""pt""" ,       )

									a__       :						Optional[Any]							=  shift_tokens_right(batch["""labels"""] ,       self.tokenizer.pad_token_id  )

									self.assertIsInstance(__lowercase ,       __lowercase  )

									self.assertEqual((2, 1_4) ,       batch.input_ids.shape  )
									self.assertEqual((2, 1_4) ,       batch.attention_mask.shape  )
									a__       :						List[str]							=  batch.input_ids.tolist()[0]
									self.assertListEqual(self.expected_src_tokens ,       __lowercase  )
									self.assertEqual(2 ,       batch.decoder_input_ids[0, -1]  )  # EOS
									# Test that special tokens are reset
									self.assertEqual(self.tokenizer.prefix_tokens ,       []  )
									self.assertEqual(self.tokenizer.suffix_tokens ,       [self.tokenizer.eos_token_id, EN_CODE]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						str							=  self.tokenizer(self.src_text ,       padding=__lowercase ,       truncation=__lowercase ,       max_length=3 ,       return_tensors="""pt"""  )
									a__       :						str							=  self.tokenizer(
									    text_target=self.tgt_text ,       padding=__lowercase ,       truncation=__lowercase ,       max_length=1_0 ,       return_tensors="""pt"""  )
									a__       :						List[Any]							=  targets["""input_ids"""]
									a__       :						Optional[Any]							=  shift_tokens_right(__lowercase ,       self.tokenizer.pad_token_id  )

									self.assertEqual(batch.input_ids.shape[1] ,       3  )
									self.assertEqual(batch.decoder_input_ids.shape[1] ,       1_0  )

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									a__       :						Optional[int]							=  self.tokenizer._build_translation_inputs(
									    """A test""" ,       return_tensors="""pt""" ,       src_lang="""en_XX""" ,       tgt_lang="""ar_AR"""  )

									self.assertEqual(
									    nested_simplify(__lowercase  ) ,       {
									        # A, test, EOS, en_XX
									        """input_ids""": [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
									        """attention_mask""": [[1, 1, 1, 1]],
									        # ar_AR
									        """forced_bos_token_id""": 2_5_0_0_0_1,
									    } ,       )









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

def      lowerCAmelCase_					(  _lowercase    :       Optional[Any]						,       _lowercase    :       List[Any]						,       _lowercase    :       Optional[Any]						,       _lowercase    :       Optional[Any])							->	List[Any]:







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

						a__       :						Optional[int]							=  corpus.__dict__
						corpus_dict_no_vocab.pop("""vocab"""						,       _lowercase)
						a__       :						Tuple							=  pytorch_dump_folder_path + """/""" + CORPUS_NAME
						print(F'''Save dataset to {pytorch_dataset_dump_path}''')
						torch.save(_lowercase						,       _lowercase)

			if tf_checkpoint_path:
						# Convert a pre-trained TensorFlow model
						a__       :						Optional[Any]							=  os.path.abspath(_lowercase)
						a__       :						str							=  os.path.abspath(_lowercase)

						print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''')
						# Initialise PyTorch model
						if transfo_xl_config_file == "":
									a__       :						List[str]							=  TransfoXLConfig()
						else:
									a__       :						Any							=  TransfoXLConfig.from_json_file(_lowercase)
						print(F'''Building PyTorch model from configuration: {config}''')
						a__       :						Any							=  TransfoXLLMHeadModel(_lowercase)

						a__       :						Optional[Any]							=  load_tf_weights_in_transfo_xl(_lowercase						,       _lowercase						,       _lowercase)
						# Save pytorch-model
						a__       :						Tuple							=  os.path.join(_lowercase						,       _lowercase)
						a__       :						Optional[int]							=  os.path.join(_lowercase						,       _lowercase)
						print(F'''Save PyTorch model to {os.path.abspath(_lowercase)}''')
						torch.save(model.state_dict()						,       _lowercase)
						print(F'''Save configuration file to {os.path.abspath(_lowercase)}''')
						with open(_lowercase						,       """w"""						,       encoding="""utf-8""") as f:
									f.write(config.to_json_string())


if __name__ == "__main__":
		_lowercase		:				Tuple      		=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.",
		)
		_lowercase		:				Dict      		=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,
		)




