
"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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


_A			:       List[str]   		=       get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
   from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_A			:       Tuple   		=       25_60_47
_A			:       List[str]   		=       25_61_45

@require_sentencepiece
@require_tokenizers
class 		a__						(      a_,			unittest.TestCase						):
   __lowerCAmelCase            =				NllbTokenizer
   __lowerCAmelCase            =				NllbTokenizerFast
   __lowerCAmelCase            =				True
   __lowerCAmelCase            =				True
   __lowerCAmelCase            =				{}
   def 			__magic_name__			(			self				):
          super().setUp()

          # We have a SentencePiece fixture for testing
          lowercase    :				Optional[int]       		=							NllbTokenizer(_a	,    keep_accents=_a				)
          tokenizer.save_pretrained(self.tmpdirname				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							NllbTokenizer(_a	,    keep_accents=_a				)

          lowercase    :				List[Any]       		=							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]]	,    )

          lowercase    :				List[str]       		=							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",
                  ".",
              ]	,    )
          lowercase    :				Union[str, 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]
              ]	,    )

          lowercase    :				List[Any]       		=							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 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                        lowercase    :				str       		=							self.rust_tokenizer_class.from_pretrained(_a	,    **_a				)
                        lowercase    :				Optional[Any]       		=							self.tokenizer_class.from_pretrained(_a	,    **_a				)

                        lowercase    :				Any       		=							tempfile.mkdtemp()

                        lowercase    :				Dict       		=							tokenizer_r.save_pretrained(_a				)
                        lowercase    :				int       		=							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				)				)
                        lowercase    :				Tuple       		=							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
                        lowercase    :				List[Any]       		=							tokenizer_r.from_pretrained(_a				)
                        lowercase    :				Tuple       		=							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=True
                        lowercase    :				Any       		=							tempfile.mkdtemp()

                        lowercase    :				str       		=							tokenizer_r.save_pretrained(_a	,    legacy_format=_a				)
                        lowercase    :				Optional[Any]       		=							tokenizer_p.save_pretrained(_a				)

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

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

                        # Save tokenizer rust, legacy_format=False
                        lowercase    :				Union[str, Any]       		=							tempfile.mkdtemp()

                        lowercase    :				Dict       		=							tokenizer_r.save_pretrained(_a	,    legacy_format=_a				)
                        lowercase    :				int       		=							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
                        lowercase    :				str       		=							tokenizer_r.from_pretrained(_a				)
                        lowercase    :				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
   def 			__magic_name__			(			self				):
          if not self.test_seqaseq:
                 return

          lowercase    :				List[str]       		=							self.get_tokenizers()
          for tokenizer in tokenizers:
                 with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
                        # Longer text that will definitely require truncation.
                        lowercase    :				Union[str, 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.",
                        ]
                        lowercase    :				Dict       		=							[
                            "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.",
                        ]
                        try:
                               lowercase    :				List[Any]       		=							tokenizer.prepare_seqaseq_batch(
                                   src_texts=_a	,    tgt_texts=_a	,    max_length=3	,    max_target_length=10	,    return_tensors="pt"	,    src_lang="eng_Latn"	,    tgt_lang="ron_Latn"	,    )
                        except NotImplementedError:
                               return
                        self.assertEqual(batch.input_ids.shape[1]	,    3				)
                        self.assertEqual(batch.labels.shape[1]	,    10				)
                        # max_target_length will default to max_length if not specified
                        lowercase    :				Optional[Any]       		=							tokenizer.prepare_seqaseq_batch(
                            _a	,    tgt_texts=_a	,    max_length=3	,    return_tensors="pt"				)
                        self.assertEqual(batch.input_ids.shape[1]	,    3				)
                        self.assertEqual(batch.labels.shape[1]	,    3				)

                        lowercase    :				int       		=							tokenizer.prepare_seqaseq_batch(
                            src_texts=_a	,    max_length=3	,    max_target_length=10	,    return_tensors="pt"				)
                        self.assertEqual(batch_encoder_only.input_ids.shape[1]	,    3				)
                        self.assertEqual(batch_encoder_only.attention_mask.shape[1]	,    3				)
                        self.assertNotIn("decoder_input_ids"	,    _a				)
   @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."				)
   def 			__magic_name__			(			self				):
          pass

   def 			__magic_name__			(			self				):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                        lowercase    :				Any       		=							[AddedToken("<special>"	,    lstrip=_a				)]

                        lowercase    :				Any       		=							self.rust_tokenizer_class.from_pretrained(
                            _a	,    additional_special_tokens=_a	,    **_a				)
                        lowercase    :				List[Any]       		=							tokenizer_r.encode("Hey this is a <special> token"				)

                        lowercase    :				Dict       		=							tokenizer_r.encode("<special>"	,    add_special_tokens=_a				)[0]

                        self.assertTrue(special_token_id in r_output				)

                        if self.test_slow_tokenizer:
                               lowercase    :				Optional[Any]       		=							self.rust_tokenizer_class.from_pretrained(
                                   _a	,    additional_special_tokens=_a	,    **_a	,    )
                               lowercase    :				Union[str, Any]       		=							self.tokenizer_class.from_pretrained(
                                   _a	,    additional_special_tokens=_a	,    **_a				)

                               lowercase    :				List[Any]       		=							tokenizer_p.encode("Hey this is a <special> token"				)

                               lowercase    :				List[str]       		=							tokenizer_cr.encode("Hey this is a <special> token"				)

                               self.assertEqual(_a	,    _a				)
                               self.assertEqual(_a	,    _a				)
                               self.assertTrue(special_token_id in p_output				)
                               self.assertTrue(special_token_id in cr_output				)


@require_torch
@require_sentencepiece
@require_tokenizers
class 		a__						(      unittest.TestCase						):
   __lowerCAmelCase            =				"""facebook/nllb-200-distilled-600M"""
   __lowerCAmelCase            =				[
       """ 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            =				[
       """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            =				[
       25_6047,
       1_6297,
       13_4408,
       8165,
       24_8066,
       1_4734,
       950,
       1135,
       10_5721,
       3573,
       83,
       2_7352,
       108,
       4_9486,
       2,
   ]
   @classmethod
   def 			__magic_name__			(			cls				):
          lowercase    :				NllbTokenizer       		=							NllbTokenizer.from_pretrained(
              cls.checkpoint_name	,    src_lang="eng_Latn"	,    tgt_lang="ron_Latn"				)
          lowercase    :				Optional[int]       		=							1
          return cls
   def 			__magic_name__			(			self				):
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]	,    256_001				)
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]	,    256_002				)
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]	,    256_057				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
          self.assertListEqual(self.expected_src_tokens	,    _a				)
   def 			__magic_name__			(			self				):
          self.assertIn(_a	,    self.tokenizer.all_special_ids				)
          # fmt: off
          lowercase    :				int       		=							[RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
          # fmt: on

          lowercase    :				Union[str, Any]       		=							self.tokenizer.decode(_a	,    skip_special_tokens=_a				)
          lowercase    :				str       		=							self.tokenizer.decode(generated_ids[1:]	,    skip_special_tokens=_a				)
          self.assertEqual(_a	,    _a				)
          self.assertNotIn(self.tokenizer.eos_token	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							["this is gunna be a long sentence " * 20]
          assert isinstance(src_text[0]	,    _a				)
          lowercase    :				Optional[Any]       		=							10
          lowercase    :				List[str]       		=							self.tokenizer(_a	,    max_length=_a	,    truncation=_a				).input_ids[0]
          self.assertEqual(ids[-1]	,    2				)
          self.assertEqual(ids[0]	,    _a				)
          self.assertEqual(len(_a				)	,    _a				)
   def 			__magic_name__			(			self				):
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]				)	,    [256_203, 3]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							tempfile.mkdtemp()
          lowercase    :				Optional[int]       		=							self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(_a				)
          lowercase    :				Tuple       		=							NllbTokenizer.from_pretrained(_a				)
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids	,    _a				)
   @require_torch
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.tokenizer(
              self.src_text	,    text_target=self.tgt_text	,    padding=_a	,    truncation=_a	,    max_length=len(self.expected_src_tokens				)	,    return_tensors="pt"	,    )
          lowercase    :				str       		=							shift_tokens_right(
              batch["labels"]	,    self.tokenizer.pad_token_id	,    self.tokenizer.lang_code_to_id["ron_Latn"]				)

          self.assertIsInstance(_a	,    _a				)

          self.assertEqual((2, 15)	,    batch.input_ids.shape				)
          self.assertEqual((2, 15)	,    batch.attention_mask.shape				)
          lowercase    :				Any       		=							batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens	,    _a				)
          self.assertEqual(_a	,    batch.decoder_input_ids[0, 0]				)  # EOS
          # Test that special tokens are reset
          self.assertEqual(self.tokenizer.prefix_tokens	,    [EN_CODE]				)
          self.assertEqual(self.tokenizer.suffix_tokens	,    [self.tokenizer.eos_token_id]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.tokenizer(self.src_text	,    padding=_a	,    truncation=_a	,    max_length=3	,    return_tensors="pt"				)
          lowercase    :				Optional[int]       		=							self.tokenizer(
              text_target=self.tgt_text	,    padding=_a	,    truncation=_a	,    max_length=10	,    return_tensors="pt"				)
          lowercase    :				Optional[int]       		=							targets["input_ids"]
          lowercase    :				Union[str, Any]       		=							shift_tokens_right(
              _a	,    self.tokenizer.pad_token_id	,    decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]	,    )

          self.assertEqual(batch.input_ids.shape[1]	,    3				)
          self.assertEqual(batch.decoder_input_ids.shape[1]	,    10				)
   @require_torch
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.tokenizer._build_translation_inputs(
              "A test"	,    return_tensors="pt"	,    src_lang="eng_Latn"	,    tgt_lang="fra_Latn"				)

          self.assertEqual(
              nested_simplify(_a				)	,    {
                  # A, test, EOS, en_XX
                  "input_ids": [[256_047, 70, 7_356, 2]],
                  "attention_mask": [[1, 1, 1, 1]],
                  # ar_AR
                  "forced_bos_token_id": 256_057,
              }	,    )

   @require_torch
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							True
          lowercase    :				Any       		=							self.tokenizer(
              "UN Chief says there is no military solution in Syria"	,    src_lang="eng_Latn"	,    tgt_lang="fra_Latn"				)
          self.assertEqual(
              inputs.input_ids	,    [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]				)

          lowercase    :				Any       		=							False
          lowercase    :				Dict       		=							self.tokenizer(
              "UN Chief says there is no military solution in Syria"	,    src_lang="eng_Latn"	,    tgt_lang="fra_Latn"				)
          self.assertEqual(
              inputs.input_ids	,    [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]				)




"""simple docstring"""




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       Any  ) ->				Any:
       # Initialise PyTorch model
       lowercase    :				Union[str, Any]       		=							RemBertConfig.from_json_file(__snake_case  )
       print("Building PyTorch model from configuration: {}".format(str(__snake_case  )  )  )
       lowercase    :				str       		=							RemBertModel(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(__snake_case     , __snake_case     , __snake_case  )

       # Save pytorch-model
       print("Save PyTorch model to {}".format(__snake_case  )  )
       torch.save(model.state_dict()     , __snake_case  )


if __name__ == "__main__":
   _A			:       str   		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   _A			:       List[str]   		=       parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)


