



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


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

a__    =	25_60_47
a__    =	25_61_45







@require_sentencepiece
@require_tokenizers
class 		snake_case						(	SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		NllbTokenizer
   snake_case_					: str          =		NllbTokenizerFast
   snake_case_					: Tuple          =		True
   snake_case_					: Dict          =		True
   snake_case_					: Tuple          =		{}

   def      UpperCamelCase_  (					self							:			Optional[Any])				->				List[str]:

    """simple docstring"""







    super().setUp()

    # We have a SentencePiece fixture for testing
    _snake_case    :  List[str]					=					NllbTokenizer(lowerCAmelCase				,		keep_accents=lowerCAmelCase)
    tokenizer.save_pretrained(self.tmpdirname)

   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  int					=					NllbTokenizer(lowerCAmelCase				,		keep_accents=lowerCAmelCase)

    _snake_case    :  Tuple					=					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]]				,		)

    _snake_case    :  Union[str, Any]					=					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""",
            """.""",
        ]				,		)
    _snake_case    :  Optional[Any]					=					tokenizer.convert_tokens_to_ids(lowerCAmelCase)
    self.assertListEqual(
        lowerCAmelCase				,		[
            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]
        ]				,		)

    _snake_case    :  Optional[Any]					=					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>""",
            """.""",
        ]				,		)

   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				int:

    """simple docstring"""







    _snake_case    :  int					=					(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})'''):
      _snake_case    :  Optional[int]					=					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase				,		**lowerCAmelCase)
      _snake_case    :  str					=					self.tokenizer_class.from_pretrained(lowerCAmelCase				,		**lowerCAmelCase)

      _snake_case    :  Any					=					tempfile.mkdtemp()

      _snake_case    :  List[str]					=					tokenizer_r.save_pretrained(lowerCAmelCase)
      _snake_case    :  int					=					tokenizer_p.save_pretrained(lowerCAmelCase)

      # 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))
      _snake_case    :  int					=					tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f)
      self.assertSequenceEqual(lowerCAmelCase				,		lowerCAmelCase)

      # Checks everything loads correctly in the same way
      _snake_case    :  Any					=					tokenizer_r.from_pretrained(lowerCAmelCase)
      _snake_case    :  Tuple					=					tokenizer_p.from_pretrained(lowerCAmelCase)

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

      shutil.rmtree(lowerCAmelCase)

      # Save tokenizer rust, legacy_format=True
      _snake_case    :  Tuple					=					tempfile.mkdtemp()

      _snake_case    :  Dict					=					tokenizer_r.save_pretrained(lowerCAmelCase				,		legacy_format=lowerCAmelCase)
      _snake_case    :  Any					=					tokenizer_p.save_pretrained(lowerCAmelCase)

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

      # Checks everything loads correctly in the same way
      _snake_case    :  Dict					=					tokenizer_r.from_pretrained(lowerCAmelCase)
      _snake_case    :  int					=					tokenizer_p.from_pretrained(lowerCAmelCase)

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

      shutil.rmtree(lowerCAmelCase)

      # Save tokenizer rust, legacy_format=False
      _snake_case    :  int					=					tempfile.mkdtemp()

      _snake_case    :  str					=					tokenizer_r.save_pretrained(lowerCAmelCase				,		legacy_format=lowerCAmelCase)
      _snake_case    :  Optional[Any]					=					tokenizer_p.save_pretrained(lowerCAmelCase)

      # 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
      _snake_case    :  str					=					tokenizer_r.from_pretrained(lowerCAmelCase)
      _snake_case    :  List[Any]					=					tokenizer_p.from_pretrained(lowerCAmelCase)

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

      shutil.rmtree(lowerCAmelCase)

   @require_torch
   def      UpperCamelCase_  (					self							:			str)				->				int:

    """simple docstring"""







    if not self.test_seqaseq:
     return

    _snake_case    :  Dict					=					self.get_tokenizers()
    for tokenizer in tokenizers:
     with self.subTest(F'''{tokenizer.__class__.__name__}'''):
      # Longer text that will definitely require truncation.
      _snake_case    :  Tuple					=					[
          """ 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.""",
      ]
      _snake_case    :  Union[str, Any]					=					[
          """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:
       _snake_case    :  str					=					tokenizer.prepare_seqaseq_batch(
           src_texts=lowerCAmelCase				,		tgt_texts=lowerCAmelCase				,		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
      _snake_case    :  Tuple					=					tokenizer.prepare_seqaseq_batch(
          lowerCAmelCase				,		tgt_texts=lowerCAmelCase				,		max_length=3				,		return_tensors="""pt""")
      self.assertEqual(batch.input_ids.shape[1]				,		3)
      self.assertEqual(batch.labels.shape[1]				,		3)

      _snake_case    :  Any					=					tokenizer.prepare_seqaseq_batch(
          src_texts=lowerCAmelCase				,		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"""				,		lowerCAmelCase)

   @unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece.""")
   def      UpperCamelCase_  (					self							:			str)				->				Optional[Any]:

    """simple docstring"""







    pass


   def      UpperCamelCase_  (					self							:			Dict)				->				List[str]:

    """simple docstring"""







    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
      _snake_case    :  Any					=					[AddedToken("""<special>"""				,		lstrip=lowerCAmelCase)]

      _snake_case    :  int					=					self.rust_tokenizer_class.from_pretrained(
          lowerCAmelCase				,		additional_special_tokens=lowerCAmelCase				,		**lowerCAmelCase)
      _snake_case    :  Union[str, Any]					=					tokenizer_r.encode("""Hey this is a <special> token""")

      _snake_case    :  Optional[Any]					=					tokenizer_r.encode("""<special>"""				,		add_special_tokens=lowerCAmelCase)[0]

      self.assertTrue(special_token_id in r_output)

      if self.test_slow_tokenizer:
       _snake_case    :  Tuple					=					self.rust_tokenizer_class.from_pretrained(
           lowerCAmelCase				,		additional_special_tokens=lowerCAmelCase				,		**lowerCAmelCase				,		)
       _snake_case    :  Optional[Any]					=					self.tokenizer_class.from_pretrained(
           lowerCAmelCase				,		additional_special_tokens=lowerCAmelCase				,		**lowerCAmelCase)

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

       _snake_case    :  str					=					tokenizer_cr.encode("""Hey this is a <special> token""")

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







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



   '''simple docstring'''






   snake_case_					: Union[str, Any]          =		"""facebook/nllb-200-distilled-600M"""
   snake_case_					: 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.""",
   ]
   snake_case_					: Any          =		[
       """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.""",
   ]
   snake_case_					: Optional[int]          =		[
       25_60_47,
       1_62_97,
       13_44_08,
       81_65,
       24_80_66,
       1_47_34,
       9_50,
       11_35,
       10_57_21,
       35_73,
       83,
       2_73_52,
       1_08,
       4_94_86,
       2,
   ]

   @classmethod
   def      UpperCamelCase_  (					cls							:			Union[str, Any])				->				List[str]:

    """simple docstring"""







    _snake_case    :  NllbTokenizer					=					NllbTokenizer.from_pretrained(
        cls.checkpoint_name				,		src_lang="""eng_Latn"""				,		tgt_lang="""ron_Latn""")
    _snake_case    :  Optional[Any]					=					1
    return cls

   def      UpperCamelCase_  (					self							:			Tuple)				->				int:

    """simple docstring"""







    self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]				,		25_6001)
    self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]				,		25_6002)
    self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]				,		25_6057)

   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  Any					=					self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
    self.assertListEqual(self.expected_src_tokens				,		lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			int)				->				int:

    """simple docstring"""







    self.assertIn(lowerCAmelCase				,		self.tokenizer.all_special_ids)
    # fmt: off
    _snake_case    :  Union[str, Any]					=					[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
    # fmt: on

    _snake_case    :  Dict					=					self.tokenizer.decode(lowerCAmelCase				,		skip_special_tokens=lowerCAmelCase)
    _snake_case    :  Optional[Any]					=					self.tokenizer.decode(generated_ids[1:]				,		skip_special_tokens=lowerCAmelCase)
    self.assertEqual(lowerCAmelCase				,		lowerCAmelCase)
    self.assertNotIn(self.tokenizer.eos_token				,		lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			int)				->				str:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Tuple:

    """simple docstring"""







    self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""])				,		[25_6203, 3])

   def      UpperCamelCase_  (					self							:			int)				->				Tuple:

    """simple docstring"""







    _snake_case    :  int					=					tempfile.mkdtemp()
    _snake_case    :  Tuple					=					self.tokenizer.fairseq_tokens_to_ids
    self.tokenizer.save_pretrained(lowerCAmelCase)
    _snake_case    :  int					=					NllbTokenizer.from_pretrained(lowerCAmelCase)
    self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,		lowerCAmelCase)

   @require_torch
   def      UpperCamelCase_  (					self							:			Dict)				->				str:

    """simple docstring"""







    _snake_case    :  int					=					self.tokenizer(
        self.src_text				,		text_target=self.tgt_text				,		padding=lowerCAmelCase				,		truncation=lowerCAmelCase				,		max_length=len(self.expected_src_tokens)				,		return_tensors="""pt"""				,		)
    _snake_case    :  Optional[int]					=					shift_tokens_right(
        batch["""labels"""]				,		self.tokenizer.pad_token_id				,		self.tokenizer.lang_code_to_id["""ron_Latn"""])

    self.assertIsInstance(lowerCAmelCase				,		lowerCAmelCase)

    self.assertEqual((2, 15)				,		batch.input_ids.shape)
    self.assertEqual((2, 15)				,		batch.attention_mask.shape)
    _snake_case    :  Dict					=					batch.input_ids.tolist()[0]
    self.assertListEqual(self.expected_src_tokens				,		lowerCAmelCase)
    self.assertEqual(lowerCAmelCase				,		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      UpperCamelCase_  (					self							:			List[Any])				->				str:

    """simple docstring"""







    _snake_case    :  Any					=					self.tokenizer(self.src_text				,		padding=lowerCAmelCase				,		truncation=lowerCAmelCase				,		max_length=3				,		return_tensors="""pt""")
    _snake_case    :  List[str]					=					self.tokenizer(
        text_target=self.tgt_text				,		padding=lowerCAmelCase				,		truncation=lowerCAmelCase				,		max_length=10				,		return_tensors="""pt""")
    _snake_case    :  List[str]					=					targets["""input_ids"""]
    _snake_case    :  Union[str, Any]					=					shift_tokens_right(
        lowerCAmelCase				,		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      UpperCamelCase_  (					self							:			str)				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  Tuple					=					self.tokenizer._build_translation_inputs(
        """A test"""				,		return_tensors="""pt"""				,		src_lang="""eng_Latn"""				,		tgt_lang="""fra_Latn""")

    self.assertEqual(
        nested_simplify(lowerCAmelCase)				,		{
            # A, test, EOS, en_XX
            """input_ids""": [[25_6047, 70, 7356, 2]],
            """attention_mask""": [[1, 1, 1, 1]],
            # ar_AR
            """forced_bos_token_id""": 25_6057,
        }				,		)


   @require_torch
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  Dict					=					True
    _snake_case    :  int					=					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				,		[1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047])

    _snake_case    :  Optional[Any]					=					False
    _snake_case    :  Tuple					=					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				,		[25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2])





# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

a__    =	2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a__    =	{
    # fairseq:
    """wmt19-ru-en""": {"""length_penalty""": 1.1},
    """wmt19-en-ru""": {"""length_penalty""": 1.15},
    """wmt19-en-de""": {"""length_penalty""": 1.0},
    """wmt19-de-en""": {"""length_penalty""": 1.1},
    # allenai:
    """wmt16-en-de-dist-12-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-dist-6-1""": {"""length_penalty""": 0.6},
    """wmt16-en-de-12-1""": {"""length_penalty""": 0.8},
    """wmt19-de-en-6-6-base""": {"""length_penalty""": 0.6},
    """wmt19-de-en-6-6-big""": {"""length_penalty""": 0.6},
}

# this remaps the different models to their organization names
a__    =	{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  a__    =	"""facebook"""
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
  a__    =	"""allenai"""



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				)  ->     Union[str, Any]:
 # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
 # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
 _snake_case    :  Union[str, Any]					=					dict((re.sub(R"""@@$"""				,       """"""				,       SCREAMING_SNAKE_CASE__				), v) if k.endswith("""@@"""				) else (re.sub(R"""$"""				,       """</w>"""				,       SCREAMING_SNAKE_CASE__				), v) for k, v in d.items()				)
 _snake_case    :  int					=					"""<s> <pad> </s> <unk>""".split()
 # restore the special tokens
 for k in keep_keys:
  del da[F'''{k}</w>''']
  _snake_case    :  Tuple					=					d[k]  # restore
 return da



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	str				)  ->     str:
 # prep
 assert os.path.exists(SCREAMING_SNAKE_CASE__				)
 os.makedirs(SCREAMING_SNAKE_CASE__				,       exist_ok=SCREAMING_SNAKE_CASE__				)
 print(F'''Writing results to {pytorch_dump_folder_path}'''				)

 # handle various types of models

 _snake_case    :  Optional[Any]					=					basename(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					dirname(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
 _snake_case    :  List[str]					=					cls.hub_models()
 _snake_case    :  Tuple					=					{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
 _snake_case    :  Dict					=					"""."""
 # note: since the model dump is old, fairseq has upgraded its model some
 # time later, and it does a whole lot of rewrites and splits on the saved
 # weights, therefore we can't use torch.load() directly on the model file.
 # see: upgrade_state_dict(state_dict) in fairseq_model.py
 print(F'''using checkpoint {checkpoint_file}'''				)
 _snake_case    :  List[Any]					=					hub_utils.from_pretrained(
     SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       archive_map=SCREAMING_SNAKE_CASE__				,       **SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Dict					=					vars(chkpt["""args"""]["""model"""]				)

 _snake_case    :  Union[str, Any]					=					args["""source_lang"""]
 _snake_case    :  Tuple					=					args["""target_lang"""]

 _snake_case    :  Any					=					dirname(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					basename(SCREAMING_SNAKE_CASE__				)

 # dicts
 _snake_case    :  List[str]					=					os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dict.{src_lang}.txt'''				)
 _snake_case    :  Any					=					os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dict.{tgt_lang}.txt'''				)

 _snake_case    :  List[Any]					=					Dictionary.load(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[Any]					=					rewrite_dict_keys(src_dict.indices				)
 _snake_case    :  Dict					=					len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  List[Any]					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """vocab-src.json"""				)
 print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''				)
 with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as f:
  f.write(json.dumps(SCREAMING_SNAKE_CASE__				,       ensure_ascii=SCREAMING_SNAKE_CASE__				,       indent=SCREAMING_SNAKE_CASE__				)				)

 # detect whether this is a do_lower_case situation, which can be derived by checking whether we
 # have at least one uppercase letter in the source vocab
 _snake_case    :  str					=					True
 for k in src_vocab.keys():
  if not k.islower():
   _snake_case    :  Any					=					False
   break

 _snake_case    :  Union[str, Any]					=					Dictionary.load(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Union[str, Any]					=					rewrite_dict_keys(tgt_dict.indices				)
 _snake_case    :  Union[str, Any]					=					len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Tuple					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """vocab-tgt.json"""				)
 print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''				)
 with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as f:
  f.write(json.dumps(SCREAMING_SNAKE_CASE__				,       ensure_ascii=SCREAMING_SNAKE_CASE__				,       indent=SCREAMING_SNAKE_CASE__				)				)

 # merges_file (bpecodes)
 _snake_case    :  str					=					os.path.join(SCREAMING_SNAKE_CASE__				,       VOCAB_FILES_NAMES["""merges_file"""]				)
 for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
  _snake_case    :  Union[str, Any]					=					os.path.join(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  if os.path.exists(SCREAMING_SNAKE_CASE__				):
   break
 with open(SCREAMING_SNAKE_CASE__				,       encoding="""utf-8"""				) as fin:
  _snake_case    :  Dict					=					fin.read()
 _snake_case    :  Optional[Any]					=					re.sub(R""" \d+$"""				,       """"""				,       SCREAMING_SNAKE_CASE__				,       0				,       re.M				)  # remove frequency number
 print(F'''Generating {merges_file}'''				)
 with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as fout:
  fout.write(SCREAMING_SNAKE_CASE__				)

 # model config
 _snake_case    :  List[str]					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """config.json"""				)

 # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
 # may have to modify the tokenizer if a different type is used by a future model
 assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
 assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''

 _snake_case    :  Optional[int]					=					{
     """architectures""": ["""FSMTForConditionalGeneration"""],
     """model_type""": """fsmt""",
     """activation_dropout""": args["""activation_dropout"""],
     """activation_function""": """relu""",
     """attention_dropout""": args["""attention_dropout"""],
     """d_model""": args["""decoder_embed_dim"""],
     """dropout""": args["""dropout"""],
     """init_std""": 0.0_2,
     """max_position_embeddings""": args["""max_source_positions"""],
     """num_hidden_layers""": args["""encoder_layers"""],
     """src_vocab_size""": src_vocab_size,
     """tgt_vocab_size""": tgt_vocab_size,
     """langs""": [src_lang, tgt_lang],
     """encoder_attention_heads""": args["""encoder_attention_heads"""],
     """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
     """encoder_layerdrop""": args["""encoder_layerdrop"""],
     """encoder_layers""": args["""encoder_layers"""],
     """decoder_attention_heads""": args["""decoder_attention_heads"""],
     """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
     """decoder_layerdrop""": args["""decoder_layerdrop"""],
     """decoder_layers""": args["""decoder_layers"""],
     """bos_token_id""": 0,
     """pad_token_id""": 1,
     """eos_token_id""": 2,
     """is_encoder_decoder""": True,
     """scale_embedding""": not args["""no_scale_embedding"""],
     """tie_word_embeddings""": args["""share_all_embeddings"""],
 }

 # good hparam defaults to start with
 _snake_case    :  Tuple					=					5
 _snake_case    :  int					=					False
 if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
  _snake_case    :  List[str]					=					best_score_hparams[model_dir]["""length_penalty"""]
 else:
  _snake_case    :  Optional[Any]					=					1.0

 print(F'''Generating {fsmt_model_config_file}'''				)
 with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as f:
  f.write(json.dumps(SCREAMING_SNAKE_CASE__				,       ensure_ascii=SCREAMING_SNAKE_CASE__				,       indent=SCREAMING_SNAKE_CASE__				)				)

 # tokenizer config
 _snake_case    :  List[Any]					=					os.path.join(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  str					=					{
     """langs""": [src_lang, tgt_lang],
     """model_max_length""": 1_024,
     """do_lower_case""": do_lower_case,
 }

 print(F'''Generating {fsmt_tokenizer_config_file}'''				)
 with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as f:
  f.write(json.dumps(SCREAMING_SNAKE_CASE__				,       ensure_ascii=SCREAMING_SNAKE_CASE__				,       indent=SCREAMING_SNAKE_CASE__				)				)

 # model
 _snake_case    :  Optional[Any]					=					chkpt["""models"""][0]
 _snake_case    :  List[str]					=					model.state_dict()

 # rename keys to start with 'model.'
 _snake_case    :  Any					=					OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()				)

 # remove unneeded keys
 _snake_case    :  Union[str, Any]					=					[
     """model.model""",
     """model.encoder.version""",
     """model.decoder.version""",
     """model.encoder_embed_tokens.weight""",
     """model.decoder_embed_tokens.weight""",
     """model.encoder.embed_positions._float_tensor""",
     """model.decoder.embed_positions._float_tensor""",
 ]
 for k in ignore_keys:
  model_state_dict.pop(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Optional[int]					=					FSMTConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  List[Any]					=					FSMTForConditionalGeneration(SCREAMING_SNAKE_CASE__				)

 # check that it loads ok
 model_new.load_state_dict(SCREAMING_SNAKE_CASE__				,       strict=SCREAMING_SNAKE_CASE__				)

 # save
 _snake_case    :  int					=					os.path.join(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 print(F'''Generating {pytorch_weights_dump_path}'''				)
 torch.save(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 print("""Conversion is done!"""				)
 print("""\nLast step is to upload the files to s3"""				)
 print(F'''cd {data_root}'''				)
 print(F'''transformers-cli upload {model_dir}'''				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--fsmt_checkpoint_path""",
      default=None,
      type=str,
      required=True,
      help=(
          """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
          """ bpecodes, etc."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  a__    =	parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
