


'''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


__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		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					:				str									=		256047
__SCREAMING_SNAKE_CASE					:				int									=		256145






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

						"""simple docstring"""






						_UpperCAmelCase		:       List[Any]   						=NllbTokenizer
						_UpperCAmelCase		:       Optional[int]   						=NllbTokenizerFast
						_UpperCAmelCase		:       List[Any]   						=True
						_UpperCAmelCase		:       Dict   						=True
						_UpperCAmelCase		:       int   						={}






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													super().setUp()

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






						def 				_UpperCAmelCase       (  self							:      Dict	):
													A_			    =		NllbTokenizer(SCREAMING_SNAKE_CASE_ ,  keep_accents=SCREAMING_SNAKE_CASE_	)

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

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

													A_			    =		tokenizer.tokenize("I was born in 92000, and this is falsé."	)
													self.assertListEqual(
													    SCREAMING_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",
													        ".",
													    ] ,  )
													A_			    =		tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)
													self.assertListEqual(
													    SCREAMING_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]
													    ] ,  )

													A_			    =		tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	)
													self.assertListEqual(
													    SCREAMING_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 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		(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})"	):
																											A_			    =		self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ ,  **SCREAMING_SNAKE_CASE_	)
																											A_			    =		self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ ,  **SCREAMING_SNAKE_CASE_	)

																											A_			    =		tempfile.mkdtemp()

																											A_			    =		tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_	)
																											A_			    =		tokenizer_p.save_pretrained(SCREAMING_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	)	)
																											A_			    =		tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f	)
																											self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ ,  SCREAMING_SNAKE_CASE_	)

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

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

																											shutil.rmtree(SCREAMING_SNAKE_CASE_	)

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

																											A_			    =		tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ ,  legacy_format=SCREAMING_SNAKE_CASE_	)
																											A_			    =		tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_	)

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

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

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

																											shutil.rmtree(SCREAMING_SNAKE_CASE_	)

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

																											A_			    =		tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ ,  legacy_format=SCREAMING_SNAKE_CASE_	)
																											A_			    =		tokenizer_p.save_pretrained(SCREAMING_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
																											A_			    =		tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_	)
																											A_			    =		tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_	)

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

																											shutil.rmtree(SCREAMING_SNAKE_CASE_	)






						@require_torch
						def 				_UpperCAmelCase       (  self							:      Dict	):
													if not self.test_seqaseq:
																				return

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

																											A_			    =		tokenizer.prepare_seqaseq_batch(
																											    src_texts=SCREAMING_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" ,  SCREAMING_SNAKE_CASE_	)






						@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."	)
						def 				_UpperCAmelCase       (  self							:      Tuple	):
													pass






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

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

																											A_			    =		tokenizer_r.encode("<special>" ,  add_special_tokens=SCREAMING_SNAKE_CASE_	)[0]

																											self.assertTrue(special_token_id in r_output	)

																											if self.test_slow_tokenizer:
																																		A_			    =		self.rust_tokenizer_class.from_pretrained(
																																		    SCREAMING_SNAKE_CASE_ ,  additional_special_tokens=SCREAMING_SNAKE_CASE_ ,  **SCREAMING_SNAKE_CASE_ ,  )
																																		A_			    =		self.tokenizer_class.from_pretrained(
																																		    SCREAMING_SNAKE_CASE_ ,  additional_special_tokens=SCREAMING_SNAKE_CASE_ ,  **SCREAMING_SNAKE_CASE_	)

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

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

																																		self.assertEqual(SCREAMING_SNAKE_CASE_ ,  SCREAMING_SNAKE_CASE_	)
																																		self.assertEqual(SCREAMING_SNAKE_CASE_ ,  SCREAMING_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 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[int]   						='facebook/nllb-200-distilled-600M'
						_UpperCAmelCase		:       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.',
						]
						_UpperCAmelCase		:       List[str]   						=[
						    'Şeful ONU declară că nu există o soluţie militară în Siria',
						    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
						    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
						    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
						]
						_UpperCAmelCase		:       Tuple   						=[
						    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 				_UpperCAmelCase       (  cls							:      Union[str, Any]	):
													A_			    =		NllbTokenizer.from_pretrained(
													    cls.checkpoint_name ,  src_lang="eng_Latn" ,  tgt_lang="ron_Latn"	)
													A_			    =		1
													return cls






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													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 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
													self.assertListEqual(self.expected_src_tokens ,  SCREAMING_SNAKE_CASE_	)






						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													self.assertIn(SCREAMING_SNAKE_CASE_ ,  self.tokenizer.all_special_ids	)
													# fmt: off
													A_			    =		[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

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






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		['this is gunna be a long sentence ' * 20]
													assert isinstance(src_text[0] ,  SCREAMING_SNAKE_CASE_	)
													A_			    =		10
													A_			    =		self.tokenizer(SCREAMING_SNAKE_CASE_ ,  max_length=SCREAMING_SNAKE_CASE_ ,  truncation=SCREAMING_SNAKE_CASE_	).input_ids[0]
													self.assertEqual(ids[-1] ,  2	)
													self.assertEqual(ids[0] ,  SCREAMING_SNAKE_CASE_	)
													self.assertEqual(len(SCREAMING_SNAKE_CASE_	) ,  SCREAMING_SNAKE_CASE_	)






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






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		tempfile.mkdtemp()
													A_			    =		self.tokenizer.fairseq_tokens_to_ids
													self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_	)
													A_			    =		NllbTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_	)
													self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,  SCREAMING_SNAKE_CASE_	)






						@require_torch
						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		self.tokenizer(
													    self.src_text ,  text_target=self.tgt_text ,  padding=SCREAMING_SNAKE_CASE_ ,  truncation=SCREAMING_SNAKE_CASE_ ,  max_length=len(self.expected_src_tokens	) ,  return_tensors="pt" ,  )
													A_			    =		shift_tokens_right(
													    batch["labels"] ,  self.tokenizer.pad_token_id ,  self.tokenizer.lang_code_to_id["ron_Latn"]	)

													self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,  SCREAMING_SNAKE_CASE_	)

													self.assertEqual((2, 15) ,  batch.input_ids.shape	)
													self.assertEqual((2, 15) ,  batch.attention_mask.shape	)
													A_			    =		batch.input_ids.tolist()[0]
													self.assertListEqual(self.expected_src_tokens ,  SCREAMING_SNAKE_CASE_	)
													self.assertEqual(SCREAMING_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 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		self.tokenizer(self.src_text ,  padding=SCREAMING_SNAKE_CASE_ ,  truncation=SCREAMING_SNAKE_CASE_ ,  max_length=3 ,  return_tensors="pt"	)
													A_			    =		self.tokenizer(
													    text_target=self.tgt_text ,  padding=SCREAMING_SNAKE_CASE_ ,  truncation=SCREAMING_SNAKE_CASE_ ,  max_length=10 ,  return_tensors="pt"	)
													A_			    =		targets['input_ids']
													A_			    =		shift_tokens_right(
													    SCREAMING_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 				_UpperCAmelCase       (  self							:      Any	):
													A_			    =		self.tokenizer._build_translation_inputs(
													    "A test" ,  return_tensors="pt" ,  src_lang="eng_Latn" ,  tgt_lang="fra_Latn"	)

													self.assertEqual(
													    nested_simplify(SCREAMING_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 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		True
													A_			    =		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]	)

													A_			    =		False
													A_			    =		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]	)









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
      from ..ta.tokenization_ta import TaTokenizer
else:
      from ...utils.dummy_sentencepiece_objects import TaTokenizer

A__						:   int				=    TaTokenizer

if is_tokenizers_available():
      from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
      from ...utils.dummy_tokenizers_objects import TaTokenizerFast

A__						:   Dict				=    TaTokenizerFast

A__						:   Dict				=    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   Any				=    [
          """MT5EncoderModel""",
          """MT5ForConditionalGeneration""",
          """MT5ForQuestionAnswering""",
          """MT5Model""",
          """MT5PreTrainedModel""",
          """MT5Stack""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
      from .configuration_mta import MTaConfig, MTaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mta import (
                MTaEncoderModel,
                MTaForConditionalGeneration,
                MTaForQuestionAnswering,
                MTaModel,
                MTaPreTrainedModel,
                MTaStack,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
      import sys

      A__						:   Union[str, Any]				=    _LazyModule(
          __name__,
          globals()["""__file__"""],
          _import_structure,
          extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
          module_spec=__spec__,
      )






