





'''simple docstring'''

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, 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


lowercase__		:     Tuple			=							get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
   from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowercase__		:     Dict			=							25_00_04
lowercase__		:     List[Any]			=							25_00_20
@require_sentencepiece
@require_tokenizers
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Dict									=       MBartTokenizer
       _snake_case      :	Tuple									=       MBartTokenizerFast
       _snake_case      :	Union[str, Any]									=       True
       _snake_case      :	Tuple									=       True





       def snake_case__		(				self  :						List[Any]	)       ->	Dict:

            '''simple docstring'''




            super().setUp()

            # We have a SentencePiece fixture for testing
            _UpperCamelCase							       =			MBartTokenizer(lowerCAmelCase__			,					keep_accents=lowerCAmelCase__	)
            tokenizer.save_pretrained(self.tmpdirname	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			MBartTokenizer(lowerCAmelCase__			,					keep_accents=lowerCAmelCase__	)

            _UpperCamelCase							       =			tokenizer.tokenize('''This is a test'''	)
            self.assertListEqual(lowerCAmelCase__			,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']	)

            self.assertListEqual(
                tokenizer.convert_tokens_to_ids(lowerCAmelCase__	)			,					[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,					)

            _UpperCamelCase							       =			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''	)
            self.assertListEqual(
                lowerCAmelCase__			,					[
                    SPIECE_UNDERLINE + '''I''',
                    SPIECE_UNDERLINE + '''was''',
                    SPIECE_UNDERLINE + '''b''',
                    '''or''',
                    '''n''',
                    SPIECE_UNDERLINE + '''in''',
                    SPIECE_UNDERLINE + '''''',
                    '''9''',
                    '''2''',
                    '''0''',
                    '''0''',
                    '''0''',
                    ''',''',
                    SPIECE_UNDERLINE + '''and''',
                    SPIECE_UNDERLINE + '''this''',
                    SPIECE_UNDERLINE + '''is''',
                    SPIECE_UNDERLINE + '''f''',
                    '''al''',
                    '''s''',
                    '''é''',
                    '''.''',
                ]			,					)
            _UpperCamelCase							       =			tokenizer.convert_tokens_to_ids(lowerCAmelCase__	)
            self.assertListEqual(
                lowerCAmelCase__			,					[
                    value + tokenizer.fairseq_offset
                    for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                    #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
                ]			,					)

            _UpperCamelCase							       =			tokenizer.convert_ids_to_tokens(lowerCAmelCase__	)
            self.assertListEqual(
                lowerCAmelCase__			,					[
                    SPIECE_UNDERLINE + '''I''',
                    SPIECE_UNDERLINE + '''was''',
                    SPIECE_UNDERLINE + '''b''',
                    '''or''',
                    '''n''',
                    SPIECE_UNDERLINE + '''in''',
                    SPIECE_UNDERLINE + '''''',
                    '''<unk>''',
                    '''2''',
                    '''0''',
                    '''0''',
                    '''0''',
                    ''',''',
                    SPIECE_UNDERLINE + '''and''',
                    SPIECE_UNDERLINE + '''this''',
                    SPIECE_UNDERLINE + '''is''',
                    SPIECE_UNDERLINE + '''f''',
                    '''al''',
                    '''s''',
                    '''<unk>''',
                    '''.''',
                ]			,					)







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

            '''simple docstring'''




            if not self.test_slow_tokenizer:
                 # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                 return

            _UpperCamelCase							       =			(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                      _UpperCamelCase							       =			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,					**lowerCAmelCase__	)
                      _UpperCamelCase							       =			self.tokenizer_class.from_pretrained(lowerCAmelCase__			,					**lowerCAmelCase__	)

                      _UpperCamelCase							       =			tempfile.mkdtemp()

                      _UpperCamelCase							       =			tokenizer_r.save_pretrained(lowerCAmelCase__	)
                      _UpperCamelCase							       =			tokenizer_p.save_pretrained(lowerCAmelCase__	)

                      # Checks it save with the same files + the tokenizer.json file for the fast one
                      self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files	)	)
                      _UpperCamelCase							       =			tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f	)
                      self.assertSequenceEqual(lowerCAmelCase__			,					lowerCAmelCase__	)

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

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                           self.assertTrue(hasattr(lowerCAmelCase__			,					lowerCAmelCase__	)	)
                           # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                           # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                      shutil.rmtree(lowerCAmelCase__	)

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

                      _UpperCamelCase							       =			tokenizer_r.save_pretrained(lowerCAmelCase__			,					legacy_format=lowerCAmelCase__	)
                      _UpperCamelCase							       =			tokenizer_p.save_pretrained(lowerCAmelCase__	)

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

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

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

                      shutil.rmtree(lowerCAmelCase__	)

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

                      _UpperCamelCase							       =			tokenizer_r.save_pretrained(lowerCAmelCase__			,					legacy_format=lowerCAmelCase__	)
                      _UpperCamelCase							       =			tokenizer_p.save_pretrained(lowerCAmelCase__	)

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

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

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

                      shutil.rmtree(lowerCAmelCase__	)

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






       """simple docstring"""
       _snake_case      :	Tuple									=       'facebook/mbart-large-en-ro'
       _snake_case      :	List[Any]									=       [
           ' UN Chief Says There Is No Military Solution in Syria',
           ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
       ]
       _snake_case      :	Any									=       [
           'Şeful ONU declară că nu există o soluţie militară în Siria',
           'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
           ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
           ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
       ]
       _snake_case      :	Any									=       [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]





       @classmethod
       def snake_case__		(				cls  :						str	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			MBartTokenizer.from_pretrained(
                cls.checkpoint_name			,					src_lang='''en_XX'''			,					tgt_lang='''ro_RO'''	)
            _UpperCamelCase							       =			1
            return cls





       def snake_case__		(				self  :						Any	)       ->	str:

            '''simple docstring'''




            self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']			,					250001	)
            self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']			,					250004	)
            self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']			,					250020	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
            self.assertListEqual(self.expected_src_tokens			,					lowerCAmelCase__	)





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

            '''simple docstring'''




            self.assertIn(lowerCAmelCase__			,					self.tokenizer.all_special_ids	)
            _UpperCamelCase							       =			[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
            _UpperCamelCase							       =			self.tokenizer.decode(lowerCAmelCase__			,					skip_special_tokens=lowerCAmelCase__	)
            _UpperCamelCase							       =			self.tokenizer.decode(generated_ids[1:]			,					skip_special_tokens=lowerCAmelCase__	)
            self.assertEqual(lowerCAmelCase__			,					lowerCAmelCase__	)
            self.assertNotIn(self.tokenizer.eos_token			,					lowerCAmelCase__	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			['''this is gunna be a long sentence ''' * 20]
            assert isinstance(src_text[0]			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			10
            _UpperCamelCase							       =			self.tokenizer(lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					truncation=lowerCAmelCase__	).input_ids[0]
            self.assertEqual(ids[-2]			,					2	)
            self.assertEqual(ids[-1]			,					lowerCAmelCase__	)
            self.assertEqual(len(lowerCAmelCase__	)			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Dict	)       ->	List[Any]:

            '''simple docstring'''




            self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']	)			,					[250026, 250001]	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			tempfile.mkdtemp()
            _UpperCamelCase							       =			self.tokenizer.fairseq_tokens_to_ids
            self.tokenizer.save_pretrained(lowerCAmelCase__	)
            _UpperCamelCase							       =			MBartTokenizer.from_pretrained(lowerCAmelCase__	)
            self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,					lowerCAmelCase__	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer(self.src_text			,					text_target=self.tgt_text			,					padding=lowerCAmelCase__			,					return_tensors='''pt'''	)
            _UpperCamelCase							       =			shift_tokens_right(batch['''labels''']			,					self.tokenizer.pad_token_id	)

            # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
            assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
            assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
            assert batch.decoder_input_ids[1][-1] == 2
            assert batch.labels[1][-2:].tolist() == [2, RO_CODE]





       @require_torch
       def snake_case__		(				self  :						Tuple	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer(
                self.src_text			,					text_target=self.tgt_text			,					padding=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					max_length=len(self.expected_src_tokens	)			,					return_tensors='''pt'''			,					)

            _UpperCamelCase							       =			shift_tokens_right(batch['''labels''']			,					self.tokenizer.pad_token_id	)

            self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

            self.assertEqual((2, 14)			,					batch.input_ids.shape	)
            self.assertEqual((2, 14)			,					batch.attention_mask.shape	)
            _UpperCamelCase							       =			batch.input_ids.tolist()[0]
            self.assertListEqual(self.expected_src_tokens			,					lowerCAmelCase__	)
            self.assertEqual(2			,					batch.decoder_input_ids[0, -1]	)  # EOS
            # Test that special tokens are reset
            self.assertEqual(self.tokenizer.prefix_tokens			,					[]	)
            self.assertEqual(self.tokenizer.suffix_tokens			,					[self.tokenizer.eos_token_id, EN_CODE]	)





       def snake_case__		(				self  :						Dict	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer(self.src_text			,					padding=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					max_length=3			,					return_tensors='''pt'''	)
            _UpperCamelCase							       =			self.tokenizer(
                text_target=self.tgt_text			,					padding=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					max_length=10			,					return_tensors='''pt'''	)
            _UpperCamelCase							       =			targets['''input_ids''']
            _UpperCamelCase							       =			shift_tokens_right(lowerCAmelCase__			,					self.tokenizer.pad_token_id	)

            self.assertEqual(batch.input_ids.shape[1]			,					3	)
            self.assertEqual(batch.decoder_input_ids.shape[1]			,					10	)







       @require_torch
       def snake_case__		(				self  :						str	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer._build_translation_inputs(
                '''A test'''			,					return_tensors='''pt'''			,					src_lang='''en_XX'''			,					tgt_lang='''ar_AR'''	)

            self.assertEqual(
                nested_simplify(lowerCAmelCase__	)			,					{
                    # A, test, EOS, en_XX
                    '''input_ids''': [[62, 3034, 2, 250004]],
                    '''attention_mask''': [[1, 1, 1, 1]],
                    # ar_AR
                    '''forced_bos_token_id''': 250001,
                }			,					)










'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
   from ...feature_extraction_utils import FeatureExtractionMixin
   from ...tokenization_utils_base import PreTrainedTokenizerBase
   from ...utils import TensorType

lowercase__		:     Optional[Any]			=							logging.get_logger(__name__)

lowercase__		:     Union[str, Any]			=							{
    'openai/whisper-base': 'https://huggingface.co/openai/whisper-base/resolve/main/config.json',
}

# fmt: off
lowercase__		:     Any			=							[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 3_57, 3_66, 4_38, 5_32, 6_85,
    7_05, 7_96, 9_30, 10_58, 12_20, 12_67, 12_79, 13_03, 13_43, 13_77,
    13_91, 16_35, 17_82, 18_75, 21_62, 23_61, 24_88, 34_67, 40_08, 42_11,
    46_00, 48_08, 52_99, 58_55, 63_29, 72_03, 96_09, 99_59, 1_05_63, 1_07_86,
    1_14_20, 1_17_09, 1_19_07, 1_31_63, 1_36_97, 1_37_00, 1_48_08, 1_53_06, 1_64_10, 1_67_91,
    1_79_92, 1_92_03, 1_95_10, 2_07_24, 2_23_05, 2_29_35, 2_70_07, 3_01_09, 3_04_20, 3_34_09,
    3_49_49, 4_02_83, 4_04_93, 4_05_49, 4_72_82, 4_91_46, 5_02_57, 5_03_59, 5_03_60, 5_03_61
]
lowercase__		:     str			=							[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 3_59, 5_03, 5_22, 5_42, 8_73,
    8_93, 9_02, 9_18, 9_22, 9_31, 13_50, 18_53, 19_82, 24_60, 26_27,
    32_46, 32_53, 32_68, 35_36, 38_46, 39_61, 41_83, 46_67, 65_85, 66_47,
    72_73, 90_61, 93_83, 1_04_28, 1_09_29, 1_19_38, 1_20_33, 1_23_31, 1_25_62, 1_37_93,
    1_41_57, 1_46_35, 1_52_65, 1_56_18, 1_65_53, 1_66_04, 1_83_62, 1_89_56, 2_00_75, 2_16_75,
    2_25_20, 2_61_30, 2_61_61, 2_64_35, 2_82_79, 2_94_64, 3_16_50, 3_23_02, 3_24_70, 3_68_65,
    4_28_63, 4_74_25, 4_98_70, 5_02_54, 5_02_58, 5_03_60, 5_03_61, 5_03_62
]
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[int]									=       'whisper'
       _snake_case      :	Union[str, Any]									=       ['past_key_values']
       _snake_case      :	Union[str, Any]									=       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





       def __init__(				self  :						Tuple			,					lowerCAmelCase__  :						List[str]=51865			,					lowerCAmelCase__  :						Optional[Any]=80			,					lowerCAmelCase__  :						Optional[int]=6			,					lowerCAmelCase__  :						List[Any]=4			,					lowerCAmelCase__  :						Any=6			,					lowerCAmelCase__  :						List[Any]=4			,					lowerCAmelCase__  :						List[Any]=1536			,					lowerCAmelCase__  :						Dict=1536			,					lowerCAmelCase__  :						int=0.0			,					lowerCAmelCase__  :						Optional[Any]=0.0			,					lowerCAmelCase__  :						int=50257			,					lowerCAmelCase__  :						Tuple=True			,					lowerCAmelCase__  :						Optional[Any]=True			,					lowerCAmelCase__  :						Optional[int]="gelu"			,					lowerCAmelCase__  :						Optional[int]=256			,					lowerCAmelCase__  :						Any=0.0			,					lowerCAmelCase__  :						Tuple=0.0			,					lowerCAmelCase__  :						Optional[Any]=0.0			,					lowerCAmelCase__  :						str=0.02			,					lowerCAmelCase__  :						Dict=False			,					lowerCAmelCase__  :						int=1500			,					lowerCAmelCase__  :						int=448			,					lowerCAmelCase__  :						Union[str, Any]=50256			,					lowerCAmelCase__  :						str=50256			,					lowerCAmelCase__  :						Optional[int]=50256			,					lowerCAmelCase__  :						List[str]=None			,					lowerCAmelCase__  :						Optional[int]=[220, 50256]			,					lowerCAmelCase__  :						List[Any]=False			,					lowerCAmelCase__  :						str=256			,					lowerCAmelCase__  :						Optional[Any]=False			,					lowerCAmelCase__  :						Any=0.05			,					lowerCAmelCase__  :						Optional[int]=10			,					lowerCAmelCase__  :						Dict=2			,					lowerCAmelCase__  :						int=0.0			,					lowerCAmelCase__  :						Dict=10			,					lowerCAmelCase__  :						List[str]=0			,					lowerCAmelCase__  :						List[Any]=7			,					**lowerCAmelCase__  :						List[str]			,					)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			vocab_size
            _UpperCamelCase							       =			num_mel_bins
            _UpperCamelCase							       =			d_model
            _UpperCamelCase							       =			encoder_layers
            _UpperCamelCase							       =			encoder_attention_heads
            _UpperCamelCase							       =			decoder_layers
            _UpperCamelCase							       =			decoder_attention_heads
            _UpperCamelCase							       =			decoder_ffn_dim
            _UpperCamelCase							       =			encoder_ffn_dim
            _UpperCamelCase							       =			dropout
            _UpperCamelCase							       =			attention_dropout
            _UpperCamelCase							       =			activation_dropout
            _UpperCamelCase							       =			activation_function
            _UpperCamelCase							       =			init_std
            _UpperCamelCase							       =			encoder_layerdrop
            _UpperCamelCase							       =			decoder_layerdrop
            _UpperCamelCase							       =			use_cache
            _UpperCamelCase							       =			encoder_layers
            _UpperCamelCase							       =			scale_embedding  # scale factor will be sqrt(d_model) if True
            _UpperCamelCase							       =			max_source_positions
            _UpperCamelCase							       =			max_target_positions

            # Audio Classification-specific parameters. Feel free to ignore for other classes.
            _UpperCamelCase							       =			classifier_proj_size
            _UpperCamelCase							       =			use_weighted_layer_sum

            # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
            _UpperCamelCase							       =			apply_spec_augment
            _UpperCamelCase							       =			mask_time_prob
            _UpperCamelCase							       =			mask_time_length
            _UpperCamelCase							       =			mask_time_min_masks
            _UpperCamelCase							       =			mask_feature_prob
            _UpperCamelCase							       =			mask_feature_length
            _UpperCamelCase							       =			mask_feature_min_masks

            _UpperCamelCase							       =			median_filter_width

            super().__init__(
                pad_token_id=lowerCAmelCase__			,					bos_token_id=lowerCAmelCase__			,					eos_token_id=lowerCAmelCase__			,					is_encoder_decoder=lowerCAmelCase__			,					decoder_start_token_id=lowerCAmelCase__			,					suppress_tokens=lowerCAmelCase__			,					begin_suppress_tokens=lowerCAmelCase__			,					**lowerCAmelCase__			,					)

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       @property
       def snake_case__		(				self  :						Optional[Any]	)       ->	Mapping[str, Mapping[int, str]]:

            '''simple docstring'''




            _UpperCamelCase							       =			OrderedDict(
                [
                    ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
                ]	)
            if self.use_past:
                 _UpperCamelCase							       =			{0: '''batch'''}
            else:
                 _UpperCamelCase							       =			{0: '''batch''', 1: '''decoder_sequence'''}

            if self.use_past:
                 self.fill_with_past_key_values_(lowerCAmelCase__			,					direction='''inputs'''	)

            return common_inputs





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]			,					lowerCAmelCase__  :						int = -1			,					lowerCAmelCase__  :						int = -1			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						Optional["TensorType"] = None			,					lowerCAmelCase__  :						int = 22050			,					lowerCAmelCase__  :						float = 5.0			,					lowerCAmelCase__  :						int = 220			,					)       ->	Mapping[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			OrderedDict()
            _UpperCamelCase							       =			OnnxConfig.generate_dummy_inputs(
                self			,					preprocessor=preprocessor.feature_extractor			,					batch_size=lowerCAmelCase__			,					framework=lowerCAmelCase__			,					sampling_rate=lowerCAmelCase__			,					time_duration=lowerCAmelCase__			,					frequency=lowerCAmelCase__			,					)
            _UpperCamelCase							       =			encoder_inputs['''input_features'''].shape[2]
            _UpperCamelCase							       =			encoder_sequence_length // 2 if self.use_past else seq_length

            _UpperCamelCase							       =			super().generate_dummy_inputs(
                preprocessor.tokenizer			,					lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			encoder_inputs.pop('''input_features'''	)
            _UpperCamelCase							       =			decoder_inputs.pop('''decoder_input_ids'''	)

            if "past_key_values" in decoder_inputs:
                 _UpperCamelCase							       =			decoder_inputs.pop('''past_key_values'''	)

            return dummy_inputs







       @property
       def snake_case__		(				self  :						List[Any]	)       ->	float:

            '''simple docstring'''




            return 1e-3



