'''simple docstring'''






import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__a										=					get_tests_dir('fixtures/test_sentencepiece.model')


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

__a										=					256_047
__a										=					256_145





@require_sentencepiece
@require_tokenizers
class     A__							(       __snake_case					,		unittest.TestCase   ):





   """simple docstring"""





   UpperCamelCase_       :						Dict    				=     NllbTokenizer
   UpperCamelCase_       :						List[str]    				=     NllbTokenizerFast
   UpperCamelCase_       :						Any    				=     True
   UpperCamelCase_       :						str    				=     True
   UpperCamelCase_       :						str    				=     {}




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







      """simple docstring"""




      super().setUp()

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




   def 			_lowerCAmelCase		( self     :		Any			)       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							:	List[str]							=   NllbTokenizer(A_						,     keep_accents=A_			)

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

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

      _UpperCAmelCase							:	str							=   tokenizer.tokenize("I was born in 92000, and this is falsé."			)
      self.assertListEqual(
          A_						,     [
              SPIECE_UNDERLINE + "I",
              SPIECE_UNDERLINE + "was",
              SPIECE_UNDERLINE + "b",
              "or",
              "n",
              SPIECE_UNDERLINE + "in",
              SPIECE_UNDERLINE + "",
              "9",
              "2",
              "0",
              "0",
              "0",
              ",",
              SPIECE_UNDERLINE + "and",
              SPIECE_UNDERLINE + "this",
              SPIECE_UNDERLINE + "is",
              SPIECE_UNDERLINE + "f",
              "al",
              "s",
              "é",
              ".",
          ]						,     )
      _UpperCAmelCase							:	Union[str, Any]							=   tokenizer.convert_tokens_to_ids(A_			)
      self.assertListEqual(
          A_						,     [
              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]
          ]						,     )

      _UpperCAmelCase							:	List[Any]							=   tokenizer.convert_ids_to_tokens(A_			)
      self.assertListEqual(
          A_						,     [
              SPIECE_UNDERLINE + "I",
              SPIECE_UNDERLINE + "was",
              SPIECE_UNDERLINE + "b",
              "or",
              "n",
              SPIECE_UNDERLINE + "in",
              SPIECE_UNDERLINE + "",
              "<unk>",
              "2",
              "0",
              "0",
              "0",
              ",",
              SPIECE_UNDERLINE + "and",
              SPIECE_UNDERLINE + "this",
              SPIECE_UNDERLINE + "is",
              SPIECE_UNDERLINE + "f",
              "al",
              "s",
              "<unk>",
              ".",
          ]						,     )




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







      """simple docstring"""




      _UpperCAmelCase							:	Optional[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})"""			):
            _UpperCAmelCase							:	Union[str, Any]							=   self.rust_tokenizer_class.from_pretrained(A_						,     **A_			)
            _UpperCAmelCase							:	Optional[Any]							=   self.tokenizer_class.from_pretrained(A_						,     **A_			)

            _UpperCAmelCase							:	Dict							=   tempfile.mkdtemp()

            _UpperCAmelCase							:	List[Any]							=   tokenizer_r.save_pretrained(A_			)
            _UpperCAmelCase							:	int							=   tokenizer_p.save_pretrained(A_			)

            # Checks it save with the same files + the tokenizer.json file for the fast one
            self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files			)			)
            _UpperCAmelCase							:	int							=   tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f			)
            self.assertSequenceEqual(A_						,     A_			)

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

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

            shutil.rmtree(A_			)

            # Save tokenizer rust, legacy_format=True
            _UpperCAmelCase							:	str							=   tempfile.mkdtemp()

            _UpperCAmelCase							:	Union[str, Any]							=   tokenizer_r.save_pretrained(A_						,     legacy_format=A_			)
            _UpperCAmelCase							:	Dict							=   tokenizer_p.save_pretrained(A_			)

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

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

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

            shutil.rmtree(A_			)

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

            _UpperCAmelCase							:	Optional[Any]							=   tokenizer_r.save_pretrained(A_						,     legacy_format=A_			)
            _UpperCAmelCase							:	List[Any]							=   tokenizer_p.save_pretrained(A_			)

            # Checks it saved the tokenizer.json file
            self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files			)			)

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

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

            shutil.rmtree(A_			)




   @require_torch
   def 			_lowerCAmelCase		( self     :		Dict			)       ->    int:







      """simple docstring"""




      if not self.test_seqaseq:
         return

      _UpperCAmelCase							:	Any							=   self.get_tokenizers()
      for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
            # Longer text that will definitely require truncation.
            _UpperCAmelCase							:	int							=   [
                " 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							:	Dict							=   [
                "Şeful ONU declară că nu există o soluţie militară în Siria",
                "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
                " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
                " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
            ]
            try:
               _UpperCAmelCase							:	List[str]							=   tokenizer.prepare_seqaseq_batch(
                   src_texts=A_						,     tgt_texts=A_						,     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
            _UpperCAmelCase							:	Union[str, Any]							=   tokenizer.prepare_seqaseq_batch(
                A_						,     tgt_texts=A_						,     max_length=3						,     return_tensors="pt"			)
            self.assertEqual(batch.input_ids.shape[1]						,     3			)
            self.assertEqual(batch.labels.shape[1]						,     3			)

            _UpperCAmelCase							:	str							=   tokenizer.prepare_seqaseq_batch(
                src_texts=A_						,     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"						,     A_			)




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







      """simple docstring"""




      pass




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







      """simple docstring"""




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

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

            _UpperCAmelCase							:	List[Any]							=   tokenizer_r.encode("<special>"						,     add_special_tokens=A_			)[0]

            self.assertTrue(special_token_id in r_output			)

            if self.test_slow_tokenizer:
               _UpperCAmelCase							:	List[Any]							=   self.rust_tokenizer_class.from_pretrained(
                   A_						,     additional_special_tokens=A_						,     **A_						,     )
               _UpperCAmelCase							:	Tuple							=   self.tokenizer_class.from_pretrained(
                   A_						,     additional_special_tokens=A_						,     **A_			)

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

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

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





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





   """simple docstring"""





   UpperCamelCase_       :						List[Any]    				=     'facebook/nllb-200-distilled-600M'
   UpperCamelCase_       :						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.',
   ]
   UpperCamelCase_       :						Optional[int]    				=     [
       '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_       :						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 			_lowerCAmelCase		( cls     :		List[str]			)       ->    Dict:







      """simple docstring"""




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




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







      """simple docstring"""




      _UpperCAmelCase							:	Tuple							=   self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
      self.assertListEqual(self.expected_src_tokens						,     A_			)




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







      """simple docstring"""




      self.assertIn(A_						,     self.tokenizer.all_special_ids			)
      # fmt: off
      _UpperCAmelCase							:	str							=   [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

      _UpperCAmelCase							:	Optional[Any]							=   self.tokenizer.decode(A_						,     skip_special_tokens=A_			)
      _UpperCAmelCase							:	Union[str, Any]							=   self.tokenizer.decode(generated_ids[1:]						,     skip_special_tokens=A_			)
      self.assertEqual(A_						,     A_			)
      self.assertNotIn(self.tokenizer.eos_token						,     A_			)




   def 			_lowerCAmelCase		( self     :		List[Any]			)       ->    int:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   ["this is gunna be a long sentence " * 2_0]
      assert isinstance(src_text[0]						,     A_			)
      _UpperCAmelCase							:	str							=   1_0
      _UpperCAmelCase							:	Dict							=   self.tokenizer(A_						,     max_length=A_						,     truncation=A_			).input_ids[0]
      self.assertEqual(ids[-1]						,     2			)
      self.assertEqual(ids[0]						,     A_			)
      self.assertEqual(len(A_			)						,     A_			)




   def 			_lowerCAmelCase		( self     :		Any			)       ->    Tuple:







      """simple docstring"""




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




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







      """simple docstring"""




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




   @require_torch
   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    Optional[int]:







      """simple docstring"""




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

      self.assertIsInstance(A_						,     A_			)

      self.assertEqual((2, 1_5)						,     batch.input_ids.shape			)
      self.assertEqual((2, 1_5)						,     batch.attention_mask.shape			)
      _UpperCAmelCase							:	List[Any]							=   batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens						,     A_			)
      self.assertEqual(A_						,     batch.decoder_input_ids[0, 0]			)  # EOS
      # Test that special tokens are reset
      self.assertEqual(self.tokenizer.prefix_tokens						,     [EN_CODE]			)
      self.assertEqual(self.tokenizer.suffix_tokens						,     [self.tokenizer.eos_token_id]			)




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







      """simple docstring"""




      _UpperCAmelCase							:	Any							=   self.tokenizer(self.src_text						,     padding=A_						,     truncation=A_						,     max_length=3						,     return_tensors="pt"			)
      _UpperCAmelCase							:	List[Any]							=   self.tokenizer(
          text_target=self.tgt_text						,     padding=A_						,     truncation=A_						,     max_length=1_0						,     return_tensors="pt"			)
      _UpperCAmelCase							:	str							=   targets["input_ids"]
      _UpperCAmelCase							:	List[Any]							=   shift_tokens_right(
          A_						,     self.tokenizer.pad_token_id						,     decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]						,     )

      self.assertEqual(batch.input_ids.shape[1]						,     3			)
      self.assertEqual(batch.decoder_input_ids.shape[1]						,     1_0			)




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







      """simple docstring"""




      _UpperCAmelCase							:	List[str]							=   self.tokenizer._build_translation_inputs(
          "A test"						,     return_tensors="pt"						,     src_lang="eng_Latn"						,     tgt_lang="fra_Latn"			)

      self.assertEqual(
          nested_simplify(A_			)						,     {
              # A, test, EOS, en_XX
              "input_ids": [[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[int]			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							:	Any							=   True
      _UpperCAmelCase							:	List[Any]							=   self.tokenizer(
          "UN Chief says there is no military solution in Syria"						,     src_lang="eng_Latn"						,     tgt_lang="fra_Latn"			)
      self.assertEqual(
          inputs.input_ids						,     [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]			)

      _UpperCAmelCase							:	Optional[Any]							=   False
      _UpperCAmelCase							:	List[Any]							=   self.tokenizer(
          "UN Chief says there is no military solution in Syria"						,     src_lang="eng_Latn"						,     tgt_lang="fra_Latn"			)
      self.assertEqual(
          inputs.input_ids						,     [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]			)






import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowerCamelCase       :     Union[str, Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     Any   =     {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Dict						    =   'bart'
      _UpperCAmelCase	:str						    =   ['past_key_values']
      _UpperCAmelCase	:Any						    =   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__( self       ,							A_=5_0265       ,							A_=1024       ,							A_=12       ,							A_=4096       ,							A_=16       ,							A_=12       ,							A_=4096       ,							A_=16       ,							A_=0.0       ,							A_=0.0       ,							A_="gelu"       ,							A_=1024       ,							A_=0.1       ,							A_=0.0       ,							A_=0.0       ,							A_=0.02       ,							A_=0.0       ,							A_=False       ,							A_=True       ,							A_=3       ,							A_=1       ,							A_=0       ,							A_=2       ,							A_=True       ,							A_=2       ,							A_=2       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   vocab_size
       UpperCamelCase	:					List[Any]       	=   max_position_embeddings
       UpperCamelCase	:					Any       	=   d_model
       UpperCamelCase	:					Optional[Any]       	=   encoder_ffn_dim
       UpperCamelCase	:					List[Any]       	=   encoder_layers
       UpperCamelCase	:					int       	=   encoder_attention_heads
       UpperCamelCase	:					Optional[int]       	=   decoder_ffn_dim
       UpperCamelCase	:					List[str]       	=   decoder_layers
       UpperCamelCase	:					Optional[int]       	=   decoder_attention_heads
       UpperCamelCase	:					int       	=   dropout
       UpperCamelCase	:					int       	=   attention_dropout
       UpperCamelCase	:					Tuple       	=   activation_dropout
       UpperCamelCase	:					Tuple       	=   activation_function
       UpperCamelCase	:					int       	=   init_std
       UpperCamelCase	:					List[Any]       	=   encoder_layerdrop
       UpperCamelCase	:					List[str]       	=   decoder_layerdrop
       UpperCamelCase	:					Dict       	=   classifier_dropout
       UpperCamelCase	:					Optional[int]       	=   use_cache
       UpperCamelCase	:					List[Any]       	=   encoder_layers
       UpperCamelCase	:					int       	=   scale_embedding  # scale factor will be sqrt(d_model) if True

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

       # ensure backward compatibility for BART CNN models
       if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"       ,							A_   ):
        UpperCamelCase	:					int       	=   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."   )



class 					A__   (       __snake_case			):





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[int]       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]   )

        if self.use_past:
         UpperCamelCase	:					List[str]       	=   {0: "batch"}
         UpperCamelCase	:					Dict       	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
         UpperCamelCase	:					Dict       	=   {0: "batch", 1: "decoder_sequence"}
         UpperCamelCase	:					Union[str, Any]       	=   {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
         self.fill_with_past_key_values_(A_       ,							direction="inputs"   )
       elif self.task == "causal-lm":
        # TODO: figure this case out.
        UpperCamelCase	:					Any       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]   )
        if self.use_past:
         UpperCamelCase	,    UpperCamelCase	:					Optional[int]       	=   self.num_layers
         for i in range(A_   ):
          UpperCamelCase	:					Optional[Any]       	=   {0: "batch", 2: "past_sequence + sequence"}
          UpperCamelCase	:					Union[str, Any]       	=   {0: "batch", 2: "past_sequence + sequence"}
       else:
        UpperCamelCase	:					Optional[Any]       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
            ]   )

       return common_inputs





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Tuple       	=   super().outputs
       else:
        UpperCamelCase	:					Dict       	=   super(A_       ,							self   ).outputs
        if self.use_past:
         UpperCamelCase	,    UpperCamelCase	:					int       	=   self.num_layers
         for i in range(A_   ):
          UpperCamelCase	:					int       	=   {0: "batch", 2: "past_sequence + sequence"}
          UpperCamelCase	:					Tuple       	=   {0: "batch", 2: "past_sequence + sequence"}
       return common_outputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )

       # Generate decoder inputs
       UpperCamelCase	:					List[Any]       	=   seq_length if not self.use_past else 1
       UpperCamelCase	:					Tuple       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )
       UpperCamelCase	:					Optional[int]       	=   {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase	:					List[Any]       	=   dict(**A_       ,							**A_   )

       if self.use_past:
        if not is_torch_available():
         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
        else:
         import torch
        UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   common_inputs["input_ids"].shape
        UpperCamelCase	:					List[Any]       	=   common_inputs["decoder_input_ids"].shape[1]
        UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   self.num_attention_heads
        UpperCamelCase	:					int       	=   (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        UpperCamelCase	:					List[Any]       	=   decoder_seq_length + 3
        UpperCamelCase	:					str       	=   (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        UpperCamelCase	:					int       	=   torch.cat(
            [common_inputs["decoder_attention_mask"], torch.ones(A_       ,							A_   )]       ,							dim=1   )

        UpperCamelCase	:					int       	=   []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   self.num_layers
        UpperCamelCase	:					Any       	=   min(A_       ,							A_   )
        UpperCamelCase	:					List[str]       	=   max(A_       ,							A_   ) - min_num_layers
        UpperCamelCase	:					Dict       	=   "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

        for _ in range(A_   ):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
             )   )
        # TODO: test this.
        UpperCamelCase	:					Optional[Any]       	=   encoder_shape if remaining_side_name == "encoder" else decoder_shape
        for _ in range(A_       ,							A_   ):
         common_inputs["past_key_values"].append((torch.zeros(A_   ), torch.zeros(A_   ))   )
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )

       if self.use_past:
        if not is_torch_available():
         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
        else:
         import torch
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   common_inputs["input_ids"].shape
        # Not using the same length for past_key_values
        UpperCamelCase	:					Optional[Any]       	=   seqlen + 2
        UpperCamelCase	,    UpperCamelCase	:					List[Any]       	=   self.num_layers
        UpperCamelCase	,    UpperCamelCase	:					Optional[int]       	=   self.num_attention_heads
        UpperCamelCase	:					str       	=   (
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        UpperCamelCase	:					Optional[Any]       	=   common_inputs["attention_mask"].dtype
        UpperCamelCase	:					int       	=   torch.cat(
            [common_inputs["attention_mask"], torch.ones(A_       ,							A_       ,							dtype=A_   )]       ,							dim=1   )
        UpperCamelCase	:					Optional[Any]       	=   [
            (torch.zeros(A_   ), torch.zeros(A_   )) for _ in range(A_   )
        ]
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   compute_effective_axis_dimension(
           A_       ,							fixed_dimension=OnnxConfig.default_fixed_batch       ,							num_token_to_add=0   )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase	:					Union[str, Any]       	=   tokenizer.num_special_tokens_to_add(A_   )
       UpperCamelCase	:					int       	=   compute_effective_axis_dimension(
           A_       ,							fixed_dimension=OnnxConfig.default_fixed_sequence       ,							num_token_to_add=A_   )

       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase	:					int       	=   [" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
       UpperCamelCase	:					Dict       	=   dict(tokenizer(A_       ,							return_tensors=A_   )   )
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[int]       	=   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )

       elif self.task == "causal-lm":
        UpperCamelCase	:					List[str]       	=   self._generate_dummy_inputs_for_causal_lm(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )
       else:
        UpperCamelCase	:					List[str]       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )

       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[Any]       	=   super()._flatten_past_key_values_(A_       ,							A_       ,							A_       ,							A_   )
       else:
        UpperCamelCase	:					Optional[Any]       	=   super(A_       ,							self   )._flatten_past_key_values_(
            A_       ,							A_       ,							A_       ,							A_   )
