


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


SCREAMING_SNAKE_CASE		: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

SCREAMING_SNAKE_CASE		:Dict     						=   25_60_47
SCREAMING_SNAKE_CASE		:Optional[int]     						=   25_61_45


@require_sentencepiece
@require_tokenizers
class 			__lowerCAmelCase   (       snake_case_       ,     unittest.TestCase    ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE									=				NllbTokenizer
  _SCREAMING_SNAKE_CASE									=				NllbTokenizerFast
  _SCREAMING_SNAKE_CASE									=				True
  _SCREAMING_SNAKE_CASE									=				True
  _SCREAMING_SNAKE_CASE									=				{}



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




     """simple docstring"""
     super().setUp()

     # We have a SentencePiece fixture for testing
     snake_case_    			=    NllbTokenizer(UpperCAmelCase__			, keep_accents=UpperCAmelCase__     )
     tokenizer.save_pretrained(self.tmpdirname     )



  def     lowerCAmelCase__     (   self				:				List[str]     )      ->       Optional[int]:




     """simple docstring"""
     snake_case_    			=    NllbTokenizer(UpperCAmelCase__			, keep_accents=UpperCAmelCase__     )

     snake_case_    			=    tokenizer.tokenize("This is a test"     )
     self.assertListEqual(UpperCAmelCase__			, ["▁This", "▁is", "▁a", "▁t", "est"]     )

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

     snake_case_    			=    tokenizer.tokenize("I was born in 92000, and this is falsé."     )
     self.assertListEqual(
         UpperCAmelCase__			, [
             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_    			=    tokenizer.convert_tokens_to_ids(UpperCAmelCase__     )
     self.assertListEqual(
         UpperCAmelCase__			, [
             value + tokenizer.fairseq_offset
             for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
         ]			, )

     snake_case_    			=    tokenizer.convert_ids_to_tokens(UpperCAmelCase__     )
     self.assertListEqual(
         UpperCAmelCase__			, [
             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     lowerCAmelCase__     (   self				:				Any     )      ->       List[Any]:




     """simple docstring"""
     snake_case_    			=    (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_    			=    self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__			, **UpperCAmelCase__     )
           snake_case_    			=    self.tokenizer_class.from_pretrained(UpperCAmelCase__			, **UpperCAmelCase__     )

           snake_case_    			=    tempfile.mkdtemp()

           snake_case_    			=    tokenizer_r.save_pretrained(UpperCAmelCase__     )
           snake_case_    			=    tokenizer_p.save_pretrained(UpperCAmelCase__     )

           # 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_    			=    tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f     )
           self.assertSequenceEqual(UpperCAmelCase__			, UpperCAmelCase__     )

           # Checks everything loads correctly in the same way
           snake_case_    			=    tokenizer_r.from_pretrained(UpperCAmelCase__     )
           snake_case_    			=    tokenizer_p.from_pretrained(UpperCAmelCase__     )

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

           shutil.rmtree(UpperCAmelCase__     )

           # Save tokenizer rust, legacy_format=True
           snake_case_    			=    tempfile.mkdtemp()

           snake_case_    			=    tokenizer_r.save_pretrained(UpperCAmelCase__			, legacy_format=UpperCAmelCase__     )
           snake_case_    			=    tokenizer_p.save_pretrained(UpperCAmelCase__     )

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

           # Checks everything loads correctly in the same way
           snake_case_    			=    tokenizer_r.from_pretrained(UpperCAmelCase__     )
           snake_case_    			=    tokenizer_p.from_pretrained(UpperCAmelCase__     )

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

           shutil.rmtree(UpperCAmelCase__     )

           # Save tokenizer rust, legacy_format=False
           snake_case_    			=    tempfile.mkdtemp()

           snake_case_    			=    tokenizer_r.save_pretrained(UpperCAmelCase__			, legacy_format=UpperCAmelCase__     )
           snake_case_    			=    tokenizer_p.save_pretrained(UpperCAmelCase__     )

           # 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_    			=    tokenizer_r.from_pretrained(UpperCAmelCase__     )
           snake_case_    			=    tokenizer_p.from_pretrained(UpperCAmelCase__     )

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

           shutil.rmtree(UpperCAmelCase__     )



  @require_torch
  def     lowerCAmelCase__     (   self				:				str     )      ->       List[str]:




     """simple docstring"""
     if not self.test_seqaseq:
        return

     snake_case_    			=    self.get_tokenizers()
     for tokenizer in tokenizers:
        with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
           # Longer text that will definitely require truncation.
           snake_case_    			=    [
               """ 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_    			=    [
               """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_    			=    tokenizer.prepare_seqaseq_batch(
                  src_texts=UpperCAmelCase__			, tgt_texts=UpperCAmelCase__			, max_length=3			, max_target_length=1_0			, 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]			, 1_0     )
           # max_target_length will default to max_length if not specified
           snake_case_    			=    tokenizer.prepare_seqaseq_batch(
               UpperCAmelCase__			, tgt_texts=UpperCAmelCase__			, max_length=3			, return_tensors="pt"     )
           self.assertEqual(batch.input_ids.shape[1]			, 3     )
           self.assertEqual(batch.labels.shape[1]			, 3     )

           snake_case_    			=    tokenizer.prepare_seqaseq_batch(
               src_texts=UpperCAmelCase__			, max_length=3			, max_target_length=1_0			, 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"			, UpperCAmelCase__     )



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




     """simple docstring"""
     pass



  def     lowerCAmelCase__     (   self				:				Any     )      ->       Optional[Any]:




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

           snake_case_    			=    self.rust_tokenizer_class.from_pretrained(
               UpperCAmelCase__			, additional_special_tokens=UpperCAmelCase__			, **UpperCAmelCase__     )
           snake_case_    			=    tokenizer_r.encode("Hey this is a <special> token"     )

           snake_case_    			=    tokenizer_r.encode("<special>"			, add_special_tokens=UpperCAmelCase__     )[0]

           self.assertTrue(special_token_id in r_output     )

           if self.test_slow_tokenizer:
              snake_case_    			=    self.rust_tokenizer_class.from_pretrained(
                  UpperCAmelCase__			, additional_special_tokens=UpperCAmelCase__			, **UpperCAmelCase__			, )
              snake_case_    			=    self.tokenizer_class.from_pretrained(
                  UpperCAmelCase__			, additional_special_tokens=UpperCAmelCase__			, **UpperCAmelCase__     )

              snake_case_    			=    tokenizer_p.encode("Hey this is a <special> token"     )

              snake_case_    			=    tokenizer_cr.encode("Hey this is a <special> token"     )

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






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






  """simple docstring"""





  _SCREAMING_SNAKE_CASE									=				'''facebook/nllb-200-distilled-600M'''
  _SCREAMING_SNAKE_CASE									=				[
      ''' 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.''',
  ]
  _SCREAMING_SNAKE_CASE									=				[
      '''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.''',
  ]
  _SCREAMING_SNAKE_CASE									=				[
      256047,
      16297,
      134408,
      8165,
      248066,
      14734,
      950,
      1135,
      105721,
      3573,
      83,
      27352,
      108,
      49486,
      2,
  ]



  @classmethod
  def     lowerCAmelCase__     (   cls				:				List[Any]     )      ->       List[Any]:




     """simple docstring"""
     snake_case_    			=    NllbTokenizer.from_pretrained(
         cls.checkpoint_name			, src_lang="eng_Latn"			, tgt_lang="ron_Latn"     )
     snake_case_    			=    1
     return cls



  def     lowerCAmelCase__     (   self				:				Dict     )      ->       Any:




     """simple docstring"""
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]			, 2_5_6_0_0_1     )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]			, 2_5_6_0_0_2     )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]			, 2_5_6_0_5_7     )



  def     lowerCAmelCase__     (   self				:				str     )      ->       str:




     """simple docstring"""
     snake_case_    			=    self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
     self.assertListEqual(self.expected_src_tokens			, UpperCAmelCase__     )



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




     """simple docstring"""
     self.assertIn(UpperCAmelCase__			, self.tokenizer.all_special_ids     )
     # fmt: off
     snake_case_    			=    [RO_CODE, 4_2_5_4, 9_8_0_6_8, 1_1_2_9_2_3, 3_9_0_7_2, 3_9_0_9, 7_1_3, 1_0_2_7_6_7, 2_6, 1_7_3_1_4, 3_5_6_4_2, 1_4_6_8_3, 3_3_1_1_8, 2_0_2_2, 6_6_9_8_7, 2, 2_5_6_0_4_7]
     # fmt: on

     snake_case_    			=    self.tokenizer.decode(UpperCAmelCase__			, skip_special_tokens=UpperCAmelCase__     )
     snake_case_    			=    self.tokenizer.decode(generated_ids[1:]			, skip_special_tokens=UpperCAmelCase__     )
     self.assertEqual(UpperCAmelCase__			, UpperCAmelCase__     )
     self.assertNotIn(self.tokenizer.eos_token			, UpperCAmelCase__     )



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




     """simple docstring"""
     snake_case_    			=    ["""this is gunna be a long sentence """ * 2_0]
     assert isinstance(src_text[0]			, UpperCAmelCase__     )
     snake_case_    			=    1_0
     snake_case_    			=    self.tokenizer(UpperCAmelCase__			, max_length=UpperCAmelCase__			, truncation=UpperCAmelCase__     ).input_ids[0]
     self.assertEqual(ids[-1]			, 2     )
     self.assertEqual(ids[0]			, UpperCAmelCase__     )
     self.assertEqual(len(UpperCAmelCase__     )			, UpperCAmelCase__     )



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




     """simple docstring"""
     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]     )			, [2_5_6_2_0_3, 3]     )



  def     lowerCAmelCase__     (   self				:				int     )      ->       int:




     """simple docstring"""
     snake_case_    			=    tempfile.mkdtemp()
     snake_case_    			=    self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(UpperCAmelCase__     )
     snake_case_    			=    NllbTokenizer.from_pretrained(UpperCAmelCase__     )
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids			, UpperCAmelCase__     )



  @require_torch
  def     lowerCAmelCase__     (   self				:				List[Any]     )      ->       Tuple:




     """simple docstring"""
     snake_case_    			=    self.tokenizer(
         self.src_text			, text_target=self.tgt_text			, padding=UpperCAmelCase__			, truncation=UpperCAmelCase__			, max_length=len(self.expected_src_tokens     )			, return_tensors="pt"			, )
     snake_case_    			=    shift_tokens_right(
         batch["labels"]			, self.tokenizer.pad_token_id			, self.tokenizer.lang_code_to_id["ron_Latn"]     )

     self.assertIsInstance(UpperCAmelCase__			, UpperCAmelCase__     )

     self.assertEqual((2, 1_5)			, batch.input_ids.shape     )
     self.assertEqual((2, 1_5)			, batch.attention_mask.shape     )
     snake_case_    			=    batch.input_ids.tolist()[0]
     self.assertListEqual(self.expected_src_tokens			, UpperCAmelCase__     )
     self.assertEqual(UpperCAmelCase__			, 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     lowerCAmelCase__     (   self				:				Optional[Any]     )      ->       Optional[Any]:




     """simple docstring"""
     snake_case_    			=    self.tokenizer(self.src_text			, padding=UpperCAmelCase__			, truncation=UpperCAmelCase__			, max_length=3			, return_tensors="pt"     )
     snake_case_    			=    self.tokenizer(
         text_target=self.tgt_text			, padding=UpperCAmelCase__			, truncation=UpperCAmelCase__			, max_length=1_0			, return_tensors="pt"     )
     snake_case_    			=    targets["""input_ids"""]
     snake_case_    			=    shift_tokens_right(
         UpperCAmelCase__			, 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]			, 1_0     )



  @require_torch
  def     lowerCAmelCase__     (   self				:				List[str]     )      ->       Any:




     """simple docstring"""
     snake_case_    			=    self.tokenizer._build_translation_inputs(
         "A test"			, return_tensors="pt"			, src_lang="eng_Latn"			, tgt_lang="fra_Latn"     )

     self.assertEqual(
         nested_simplify(UpperCAmelCase__     )			, {
             # A, test, EOS, en_XX
             "input_ids": [[2_5_6_0_4_7, 7_0, 7_3_5_6, 2]],
             "attention_mask": [[1, 1, 1, 1]],
             # ar_AR
             "forced_bos_token_id": 2_5_6_0_5_7,
         }			, )



  @require_torch
  def     lowerCAmelCase__     (   self				:				Optional[Any]     )      ->       Dict:




     """simple docstring"""
     snake_case_    			=    True
     snake_case_    			=    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_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2, 2_5_6_0_4_7]     )

     snake_case_    			=    False
     snake_case_    			=    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			, [2_5_6_0_4_7, 1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2]     )












"""simple docstring"""




import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					'''mvp'''
 UpperCamelCase					:       Union[str, Any]			=					['''past_key_values''']
 UpperCamelCase					:       Any			=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


 def __init__(     self		:						List[str]							,						UpperCAmelCase__		:						List[str]=50267							,						UpperCAmelCase__		:						Optional[Any]=1024							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						Optional[Any]=4096							,						UpperCAmelCase__		:						int=16							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						int=4096							,						UpperCAmelCase__		:						List[Any]=16							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple="gelu"							,						UpperCAmelCase__		:						Union[str, Any]=1024							,						UpperCAmelCase__		:						List[str]=0.1							,						UpperCAmelCase__		:						Any=0.0							,						UpperCAmelCase__		:						Dict=0.0							,						UpperCAmelCase__		:						Tuple=0.0_2							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						int=True							,						UpperCAmelCase__		:						Tuple=1							,						UpperCAmelCase__		:						Dict=0							,						UpperCAmelCase__		:						Union[str, Any]=2							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=2							,						UpperCAmelCase__		:						Any=2							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						Dict=100							,						UpperCAmelCase__		:						Union[str, Any]=800							,						**UpperCAmelCase__		:						Dict							,						)					->       List[Any]:
   _a  :       Any             =	vocab_size
   _a  :       Any             =	max_position_embeddings
   _a  :       Union[str, Any]             =	d_model
   _a  :       List[str]             =	encoder_ffn_dim
   _a  :       List[Any]             =	encoder_layers
   _a  :       Dict             =	encoder_attention_heads
   _a  :       Tuple             =	decoder_ffn_dim
   _a  :       List[Any]             =	decoder_layers
   _a  :       Optional[Any]             =	decoder_attention_heads
   _a  :       Optional[Any]             =	dropout
   _a  :       str             =	attention_dropout
   _a  :       Dict             =	activation_dropout
   _a  :       Any             =	activation_function
   _a  :       Tuple             =	init_std
   _a  :       Dict             =	encoder_layerdrop
   _a  :       Optional[int]             =	decoder_layerdrop
   _a  :       Optional[Any]             =	classifier_dropout
   _a  :       List[Any]             =	use_cache
   _a  :       Dict             =	encoder_layers
   _a  :       str             =	scale_embedding  # scale factor will be sqrt(d_model) if True
   _a  :       int             =	use_prompt
   _a  :       Dict             =	prompt_length
   _a  :       Dict             =	prompt_mid_dim

   super().__init__(
       pad_token_id=UpperCAmelCase__							,						bos_token_id=UpperCAmelCase__							,						eos_token_id=UpperCAmelCase__							,						is_encoder_decoder=UpperCAmelCase__							,						decoder_start_token_id=UpperCAmelCase__							,						forced_eos_token_id=UpperCAmelCase__							,						**UpperCAmelCase__							,						)

   if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,						UpperCAmelCase__							):
     _a  :       List[str]             =	self.bos_token_id
     warnings.warn(
         f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
         """The config can simply be saved and uploaded again to be fixed."""							)

