
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


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


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

__lowerCamelCase							=       25_60_47
__lowerCamelCase							=       25_61_45






@require_sentencepiece
@require_tokenizers
class   UpperCAmelCase		(						A_       ,unittest.TestCase ):
   A__					:		str						     =     NllbTokenizer
   A__					:		str						     =     NllbTokenizerFast
   A__					:		List[Any]						     =     True
   A__					:		int						     =     True
   A__					:		Union[str, Any]						     =     {}



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Dict:


          '''simple docstring'''


          super().setUp()

          # We have a SentencePiece fixture for testing
          snake_case       :   Any							=      NllbTokenizer(snake_case__    ,						keep_accents=snake_case__ )
          tokenizer.save_pretrained(self.tmpdirname )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Dict:


          '''simple docstring'''


          snake_case       :   Any							=      NllbTokenizer(snake_case__    ,						keep_accents=snake_case__ )

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

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(snake_case__ )    ,						[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]    ,						)

          snake_case       :   Optional[int]							=      tokenizer.tokenize("I was born in 92000, and this is falsé." )
          self.assertListEqual(
              snake_case__    ,						[
                  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       :   int							=      tokenizer.convert_tokens_to_ids(snake_case__ )
          self.assertListEqual(
              snake_case__    ,						[
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              ]    ,						)

          snake_case       :   Optional[Any]							=      tokenizer.convert_ids_to_tokens(snake_case__ )
          self.assertListEqual(
              snake_case__    ,						[
                  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		:					Union[str, Any] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   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})""" ):
                        snake_case       :   Tuple							=      self.rust_tokenizer_class.from_pretrained(snake_case__    ,						**snake_case__ )
                        snake_case       :   Any							=      self.tokenizer_class.from_pretrained(snake_case__    ,						**snake_case__ )

                        snake_case       :   List[Any]							=      tempfile.mkdtemp()

                        snake_case       :   Union[str, Any]							=      tokenizer_r.save_pretrained(snake_case__ )
                        snake_case       :   int							=      tokenizer_p.save_pretrained(snake_case__ )

                        # 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       :   Optional[Any]							=      tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
                        self.assertSequenceEqual(snake_case__    ,						snake_case__ )

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

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

                        shutil.rmtree(snake_case__ )

                        # Save tokenizer rust, legacy_format=True
                        snake_case       :   Optional[int]							=      tempfile.mkdtemp()

                        snake_case       :   int							=      tokenizer_r.save_pretrained(snake_case__    ,						legacy_format=snake_case__ )
                        snake_case       :   str							=      tokenizer_p.save_pretrained(snake_case__ )

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

                        # Checks everything loads correctly in the same way
                        snake_case       :   Optional[int]							=      tokenizer_r.from_pretrained(snake_case__ )
                        snake_case       :   int							=      tokenizer_p.from_pretrained(snake_case__ )

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

                        shutil.rmtree(snake_case__ )

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

                        snake_case       :   Optional[int]							=      tokenizer_r.save_pretrained(snake_case__    ,						legacy_format=snake_case__ )
                        snake_case       :   List[str]							=      tokenizer_p.save_pretrained(snake_case__ )

                        # 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       :   Union[str, Any]							=      tokenizer_r.from_pretrained(snake_case__ )
                        snake_case       :   Tuple							=      tokenizer_p.from_pretrained(snake_case__ )

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

                        shutil.rmtree(snake_case__ )



   @require_torch
   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Union[str, Any]:


          '''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       :   List[str]							=      [
                            " 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=snake_case__    ,						tgt_texts=snake_case__    ,						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       :   List[Any]							=      tokenizer.prepare_seqaseq_batch(
                            snake_case__    ,						tgt_texts=snake_case__    ,						max_length=3    ,						return_tensors="pt" )
                        self.assertEqual(batch.input_ids.shape[1]    ,						3 )
                        self.assertEqual(batch.labels.shape[1]    ,						3 )

                        snake_case       :   int							=      tokenizer.prepare_seqaseq_batch(
                            src_texts=snake_case__    ,						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"    ,						snake_case__ )



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


          '''simple docstring'''


          pass



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


          '''simple docstring'''


          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                        snake_case       :   int							=      [AddedToken("<special>"    ,						lstrip=snake_case__ )]

                        snake_case       :   Tuple							=      self.rust_tokenizer_class.from_pretrained(
                            snake_case__    ,						additional_special_tokens=snake_case__    ,						**snake_case__ )
                        snake_case       :   str							=      tokenizer_r.encode("Hey this is a <special> token" )

                        snake_case       :   str							=      tokenizer_r.encode("<special>"    ,						add_special_tokens=snake_case__ )[0]

                        self.assertTrue(special_token_id in r_output )

                        if self.test_slow_tokenizer:
                               snake_case       :   int							=      self.rust_tokenizer_class.from_pretrained(
                                   snake_case__    ,						additional_special_tokens=snake_case__    ,						**snake_case__    ,						)
                               snake_case       :   str							=      self.tokenizer_class.from_pretrained(
                                   snake_case__    ,						additional_special_tokens=snake_case__    ,						**snake_case__ )

                               snake_case       :   Any							=      tokenizer_p.encode("Hey this is a <special> token" )

                               snake_case       :   Optional[Any]							=      tokenizer_cr.encode("Hey this is a <special> token" )

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






@require_torch
@require_sentencepiece
@require_tokenizers
class   UpperCAmelCase		(						unittest.TestCase ):
   A__					:		int						     =     "facebook/nllb-200-distilled-600M"
   A__					:		List[str]						     =     [
       " 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.",
   ]
   A__					:		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.",
   ]
   A__					:		Union[str, Any]						     =     [
       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 				_SCREAMING_SNAKE_CASE   (cls		:					List[Any] )		->	Tuple:


          '''simple docstring'''


          snake_case       :   NllbTokenizer							=      NllbTokenizer.from_pretrained(
              cls.checkpoint_name    ,						src_lang="eng_Latn"    ,						tgt_lang="ron_Latn" )
          snake_case       :   str							=      1
          return cls



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Tuple:


          '''simple docstring'''


          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]    ,						25_60_01 )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]    ,						25_60_02 )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]    ,						25_60_57 )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Dict:


          '''simple docstring'''


          snake_case       :   int							=      self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens    ,						snake_case__ )



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


          '''simple docstring'''


          self.assertIn(snake_case__    ,						self.tokenizer.all_special_ids )
          # fmt: off
          snake_case       :   int							=      [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
          # fmt: on

          snake_case       :   Tuple							=      self.tokenizer.decode(snake_case__    ,						skip_special_tokens=snake_case__ )
          snake_case       :   Tuple							=      self.tokenizer.decode(generated_ids[1:]    ,						skip_special_tokens=snake_case__ )
          self.assertEqual(snake_case__    ,						snake_case__ )
          self.assertNotIn(self.tokenizer.eos_token    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Tuple:


          '''simple docstring'''


          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] )    ,						[25_62_03, 3] )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      tempfile.mkdtemp()
          snake_case       :   Optional[int]							=      self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(snake_case__ )
          snake_case       :   int							=      NllbTokenizer.from_pretrained(snake_case__ )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,						snake_case__ )



   @require_torch
   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Optional[int]:


          '''simple docstring'''


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

          self.assertIsInstance(snake_case__    ,						snake_case__ )

          self.assertEqual((2, 15)    ,						batch.input_ids.shape )
          self.assertEqual((2, 15)    ,						batch.attention_mask.shape )
          snake_case       :   Optional[int]							=      batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens    ,						snake_case__ )
          self.assertEqual(snake_case__    ,						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 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.tokenizer(self.src_text    ,						padding=snake_case__    ,						truncation=snake_case__    ,						max_length=3    ,						return_tensors="pt" )
          snake_case       :   List[str]							=      self.tokenizer(
              text_target=self.tgt_text    ,						padding=snake_case__    ,						truncation=snake_case__    ,						max_length=10    ,						return_tensors="pt" )
          snake_case       :   List[Any]							=      targets["input_ids"]
          snake_case       :   str							=      shift_tokens_right(
              snake_case__    ,						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 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Optional[int]:


          '''simple docstring'''


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

          self.assertEqual(
              nested_simplify(snake_case__ )    ,						{
                  # A, test, EOS, en_XX
                  "input_ids": [[25_60_47, 70, 73_56, 2]],
                  "attention_mask": [[1, 1, 1, 1]],
                  # ar_AR
                  "forced_bos_token_id": 25_60_57,
              }    ,						)



   @require_torch
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      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_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47] )

          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_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2] )












import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCamelCase__(       UpperCamelCase__			:       Any ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       int  )->int:
       # Initialise PyTorch model
       A__											=						BertConfig.from_json_file(UpperCamelCase__  )
       print(f"Building PyTorch model from configuration: {config}"  )
       A__											=						BertForPreTraining(UpperCamelCase__  )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       # Save pytorch-model
       print(f"Save PyTorch model to {pytorch_dump_path}"  )
       torch.save(model.state_dict() ,     UpperCamelCase__  )


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(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT 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__:      Any									=		parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





