





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


__lowerCamelCase       :     Optional[int]   =     get_tests_dir("""fixtures/test_sentencepiece.model""")


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

__lowerCamelCase       :     Optional[Any]   =     25_0004
__lowerCamelCase       :     Optional[Any]   =     25_0020



@require_sentencepiece
@require_tokenizers
class 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Optional[int]						    =   MBartTokenizer
      _UpperCAmelCase	:int						    =   MBartTokenizerFast
      _UpperCAmelCase	:Dict						    =   True
      _UpperCAmelCase	:Any						    =   True





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase	:					List[Any]       	=   MBartTokenizer(A_       ,							keep_accents=A_   )
       tokenizer.save_pretrained(self.tmpdirname   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   MBartTokenizer(A_       ,							keep_accents=A_   )

       UpperCamelCase	:					str       	=   tokenizer.tokenize("This is a test"   )
       self.assertListEqual(A_       ,							["▁This", "▁is", "▁a", "▁t", "est"]   )

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

       UpperCamelCase	:					Optional[Any]       	=   tokenizer.tokenize("I was born in 92000, and this is falsé."   )
       self.assertListEqual(
           A_       ,							[
               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",
               ".",
           ]       ,							)
       UpperCamelCase	:					Any       	=   tokenizer.convert_tokens_to_ids(A_   )
       self.assertListEqual(
           A_       ,							[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
           ]       ,							)

       UpperCamelCase	:					str       	=   tokenizer.convert_ids_to_tokens(A_   )
       self.assertListEqual(
           A_       ,							[
               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        __UpperCamelCase( self   ):
       '''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

       UpperCamelCase	:					Union[str, 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})"""   ):
         UpperCamelCase	:					Any       	=   self.rust_tokenizer_class.from_pretrained(A_       ,							**A_   )
         UpperCamelCase	:					Any       	=   self.tokenizer_class.from_pretrained(A_       ,							**A_   )

         UpperCamelCase	:					Union[str, Any]       	=   tempfile.mkdtemp()

         UpperCamelCase	:					Any       	=   tokenizer_r.save_pretrained(A_   )
         UpperCamelCase	:					str       	=   tokenizer_p.save_pretrained(A_   )

         # 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   )   )
         UpperCamelCase	:					Dict       	=   tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f   )
         self.assertSequenceEqual(A_       ,							A_   )

         # Checks everything loads correctly in the same way
         UpperCamelCase	:					Dict       	=   tokenizer_r.from_pretrained(A_   )
         UpperCamelCase	:					List[str]       	=   tokenizer_p.from_pretrained(A_   )

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

         shutil.rmtree(A_   )

         # Save tokenizer rust, legacy_format=True
         UpperCamelCase	:					Any       	=   tempfile.mkdtemp()

         UpperCamelCase	:					List[str]       	=   tokenizer_r.save_pretrained(A_       ,							legacy_format=A_   )
         UpperCamelCase	:					Dict       	=   tokenizer_p.save_pretrained(A_   )

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

         # Checks everything loads correctly in the same way
         UpperCamelCase	:					Any       	=   tokenizer_r.from_pretrained(A_   )
         UpperCamelCase	:					Optional[Any]       	=   tokenizer_p.from_pretrained(A_   )

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

         shutil.rmtree(A_   )

         # Save tokenizer rust, legacy_format=False
         UpperCamelCase	:					str       	=   tempfile.mkdtemp()

         UpperCamelCase	:					int       	=   tokenizer_r.save_pretrained(A_       ,							legacy_format=A_   )
         UpperCamelCase	:					List[Any]       	=   tokenizer_p.save_pretrained(A_   )

         # 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
         UpperCamelCase	:					Union[str, Any]       	=   tokenizer_r.from_pretrained(A_   )
         UpperCamelCase	:					Dict       	=   tokenizer_p.from_pretrained(A_   )

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

         shutil.rmtree(A_   )



@require_torch
@require_sentencepiece
@require_tokenizers
class 					A__   (       unittest.TestCase			):
      _UpperCAmelCase	:Optional[Any]						    =   'facebook/mbart-large-en-ro'
      _UpperCAmelCase	:Optional[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.',
      ]
      _UpperCAmelCase	:int						    =   [
          '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.',
      ]
      _UpperCAmelCase	:Dict						    =   [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]





      @classmethod
      def        __UpperCamelCase( cls   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]       ,							25_0001   )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]       ,							25_0004   )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]       ,							25_0020   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.assertIn(A_       ,							self.tokenizer.all_special_ids   )
       UpperCamelCase	:					int       	=   [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
       UpperCamelCase	:					Optional[Any]       	=   self.tokenizer.decode(A_       ,							skip_special_tokens=A_   )
       UpperCamelCase	:					str       	=   self.tokenizer.decode(generated_ids[1:]       ,							skip_special_tokens=A_   )
       self.assertEqual(A_       ,							A_   )
       self.assertNotIn(self.tokenizer.eos_token       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   ["this is gunna be a long sentence " * 20]
       assert isinstance(src_text[0]       ,							A_   )
       UpperCamelCase	:					Union[str, Any]       	=   10
       UpperCamelCase	:					List[Any]       	=   self.tokenizer(A_       ,							max_length=A_       ,							truncation=A_   ).input_ids[0]
       self.assertEqual(ids[-2]       ,							2   )
       self.assertEqual(ids[-1]       ,							A_   )
       self.assertEqual(len(A_   )       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]   )       ,							[25_0026, 25_0001]   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   tempfile.mkdtemp()
       UpperCamelCase	:					List[str]       	=   self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(A_   )
       UpperCamelCase	:					List[Any]       	=   MBartTokenizer.from_pretrained(A_   )
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,							A_   )





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.tokenizer(self.src_text       ,							text_target=self.tgt_text       ,							padding=A_       ,							return_tensors="pt"   )
       UpperCamelCase	:					int       	=   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        __UpperCamelCase( self   ):
       '''simple docstring'''




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

       UpperCamelCase	:					List[str]       	=   shift_tokens_right(batch["labels"]       ,							self.tokenizer.pad_token_id   )

       self.assertIsInstance(A_       ,							A_   )

       self.assertEqual((2, 14)       ,							batch.input_ids.shape   )
       self.assertEqual((2, 14)       ,							batch.attention_mask.shape   )
       UpperCamelCase	:					Union[str, Any]       	=   batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens       ,							A_   )
       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        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.tokenizer(self.src_text       ,							padding=A_       ,							truncation=A_       ,							max_length=3       ,							return_tensors="pt"   )
       UpperCamelCase	:					str       	=   self.tokenizer(
           text_target=self.tgt_text       ,							padding=A_       ,							truncation=A_       ,							max_length=10       ,							return_tensors="pt"   )
       UpperCamelCase	:					Optional[int]       	=   targets["input_ids"]
       UpperCamelCase	:					Any       	=   shift_tokens_right(A_       ,							self.tokenizer.pad_token_id   )

       self.assertEqual(batch.input_ids.shape[1]       ,							3   )
       self.assertEqual(batch.decoder_input_ids.shape[1]       ,							10   )





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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

       self.assertEqual(
           nested_simplify(A_   )       ,							{
               # A, test, EOS, en_XX
               "input_ids": [[62, 3034, 2, 25_0004]],
               "attention_mask": [[1, 1, 1, 1]],
               # ar_AR
               "forced_bos_token_id": 25_0001,
           }       ,							)





'''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
_UpperCamelCase											=						data_utils.TransfoXLTokenizer
_UpperCamelCase											=						data_utils.TransfoXLCorpus
_UpperCamelCase											=						data_utils
_UpperCamelCase											=						data_utils

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

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

    __lowerCamelCase       :       Dict           = load_tf_weights_in_transfo_xl(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
    # Save pytorch-model
    __lowerCamelCase       :       List[str]           = os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )
    __lowerCamelCase       :       int           = 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__":
   _UpperCamelCase											=						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.',
   )
   _UpperCamelCase											=						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,
   )






