




import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin




class       __lowerCAmelCase				(							lowerCAmelCase):
       # to overwrite at feature extractactor specific tests
       _a           =  None
       _a           =  None






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         return self.feat_extract_tester.prepare_feat_extract_dict()






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:int							=  self.feature_extraction_class(**self.feat_extract_dict )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "feature_size" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "sampling_rate" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "padding_value" ) )






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Any							=  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase			:List[Any]							=  self.feature_extraction_class(**self.feat_extract_dict )
         lowercase			:Optional[int]							=  feat_extract.model_input_names[0]

         lowercase			:Tuple							=  BatchFeature({input_name: speech_inputs} )

         self.assertTrue(all(len(_lowerCAmelCase ) == len(_lowerCAmelCase ) for x, y in zip(_lowerCAmelCase				,    processed_features[input_name] ) ) )

         lowercase			:int							=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
         lowercase			:int							=  BatchFeature({input_name: speech_inputs}				,    tensor_type="np" )

         lowercase			:List[str]							=  processed_features[input_name]

         if len(batch_features_input.shape ) < 3:
           lowercase			:List[str]							=  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )






       @require_torch
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         lowercase			:Union[str, Any]							=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
         lowercase			:Union[str, Any]							=  self.feature_extraction_class(**self.feat_extract_dict )
         lowercase			:Any							=  feat_extract.model_input_names[0]

         lowercase			:int							=  BatchFeature({input_name: speech_inputs}				,    tensor_type="pt" )

         lowercase			:Optional[Any]							=  processed_features[input_name]

         if len(batch_features_input.shape ) < 3:
           lowercase			:Any							=  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )






       @require_tf
       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:str							=  self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
         lowercase			:Optional[Any]							=  self.feature_extraction_class(**self.feat_extract_dict )
         lowercase			:int							=  feat_extract.model_input_names[0]

         lowercase			:List[str]							=  BatchFeature({input_name: speech_inputs}				,    tensor_type="tf" )

         lowercase			:Union[str, Any]							=  processed_features[input_name]

         if len(batch_features_input.shape ) < 3:
           lowercase			:Dict							=  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    str=False ):
         def _inputs_have_equal_length(_lowerCAmelCase:    int ):
           lowercase			:Tuple							=  len(input[0] )
           for input_slice in input[1:]:
             if len(_lowerCAmelCase ) != length:
               return False
           return True

         def _inputs_are_equal(_lowerCAmelCase:    int				,    _lowerCAmelCase:    Tuple ):
           if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
             return False

           for input_slice_a, input_slice_a in zip(_lowerCAmelCase				,    _lowerCAmelCase ):
             if not np.allclose(np.asarray(_lowerCAmelCase )				,    np.asarray(_lowerCAmelCase )				,    atol=1e-3 ):
               return False
           return True

         lowercase			:List[str]							=  self.feature_extraction_class(**self.feat_extract_dict )
         lowercase			:Any							=  self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
         lowercase			:Optional[Any]							=  feat_extract.model_input_names[0]

         lowercase			:Any							=  BatchFeature({input_name: speech_inputs} )

         lowercase			:List[str]							=  self.feat_extract_tester.seq_length_diff
         lowercase			:Any							=  self.feat_extract_tester.max_seq_length + pad_diff
         lowercase			:Any							=  self.feat_extract_tester.min_seq_length
         lowercase			:Dict							=  self.feat_extract_tester.batch_size
         lowercase			:Tuple							=  self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         lowercase			:Optional[int]							=  feat_extract.pad(_lowerCAmelCase				,    padding=_lowerCAmelCase )
         lowercase			:int							=  input_a[input_name]

         lowercase			:List[Any]							=  feat_extract.pad(_lowerCAmelCase				,    padding="longest" )
         lowercase			:str							=  input_a[input_name]

         lowercase			:Union[str, Any]							=  feat_extract.pad(_lowerCAmelCase				,    padding="max_length"				,    max_length=len(speech_inputs[-1] ) )
         lowercase			:Optional[int]							=  input_a[input_name]

         lowercase			:List[Any]							=  feat_extract.pad(_lowerCAmelCase				,    padding="longest"				,    return_tensors="np" )
         lowercase			:str							=  input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(_lowerCAmelCase ):
           feat_extract.pad(_lowerCAmelCase				,    padding="max_length" )[input_name]

         lowercase			:Any							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    max_length=_lowerCAmelCase				,    return_tensors="np" )
         lowercase			:int							=  input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
         self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
         self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
         self.assertTrue(_inputs_are_equal(_lowerCAmelCase				,    _lowerCAmelCase ) )
         self.assertTrue(len(input_a[0] ) == pad_min_length )
         self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )

         if feature_size > 1:
           self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         lowercase			:List[Any]							=  feat_extract.pad(_lowerCAmelCase				,    pad_to_multiple_of=10 )
         lowercase			:Optional[Any]							=  input_a[input_name]

         lowercase			:Any							=  feat_extract.pad(_lowerCAmelCase				,    padding="longest"				,    pad_to_multiple_of=10 )
         lowercase			:List[str]							=  input_a[input_name]

         lowercase			:List[Any]							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    pad_to_multiple_of=10				,    max_length=_lowerCAmelCase )
         lowercase			:Tuple							=  input_a[input_name]

         lowercase			:Dict							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    pad_to_multiple_of=10				,    max_length=_lowerCAmelCase				,    return_tensors="np"				,    )
         lowercase			:Union[str, Any]							=  input_a[input_name]

         self.assertTrue(all(len(_lowerCAmelCase ) % 10 == 0 for x in input_a ) )
         self.assertTrue(_inputs_are_equal(_lowerCAmelCase				,    _lowerCAmelCase ) )

         lowercase			:Optional[Any]							=  pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
         self.assertTrue(all(len(_lowerCAmelCase ) == expected_mult_pad_length for x in input_a ) )
         self.assertEqual(input_a.shape[:2]				,    (batch_size, expected_mult_pad_length) )

         if feature_size > 1:
           self.assertTrue(input_a.shape[2] == feature_size )

         # Check padding value is correct
         lowercase			:Union[str, Any]							=  (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
             < 1e-3 )
         self.assertTrue(
             abs(
                 np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
             < 1e-3 )
         self.assertTrue(
             abs(
                 np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
             < 1e-3 )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
             < 1e-3 )






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    int=False ):
         def _inputs_have_equal_length(_lowerCAmelCase:    Union[str, Any] ):
           lowercase			:str							=  len(input[0] )
           for input_slice in input[1:]:
             if len(_lowerCAmelCase ) != length:
               return False
           return True

         def _inputs_are_equal(_lowerCAmelCase:    int				,    _lowerCAmelCase:    Optional[Any] ):
           if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
             return False

           for input_slice_a, input_slice_a in zip(_lowerCAmelCase				,    _lowerCAmelCase ):
             if not np.allclose(np.asarray(_lowerCAmelCase )				,    np.asarray(_lowerCAmelCase )				,    atol=1e-3 ):
               return False
           return True

         lowercase			:Dict							=  self.feature_extraction_class(**self.feat_extract_dict )
         lowercase			:Optional[Any]							=  self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
         lowercase			:Tuple							=  feat_extract.model_input_names[0]

         lowercase			:List[Any]							=  BatchFeature({input_name: speech_inputs} )

         # truncate to smallest
         lowercase			:Tuple							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    max_length=len(speech_inputs[0] )				,    truncation=_lowerCAmelCase )
         lowercase			:List[str]							=  input_a[input_name]

         lowercase			:Union[str, Any]							=  feat_extract.pad(_lowerCAmelCase				,    padding="max_length"				,    max_length=len(speech_inputs[0] ) )
         lowercase			:Union[str, Any]							=  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
         self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )

         # truncate to smallest with np
         lowercase			:Tuple							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    max_length=len(speech_inputs[0] )				,    return_tensors="np"				,    truncation=_lowerCAmelCase				,    )
         lowercase			:Optional[Any]							=  input_a[input_name]

         lowercase			:int							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    max_length=len(speech_inputs[0] )				,    return_tensors="np" )
         lowercase			:Tuple							=  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )

         # truncate to middle
         lowercase			:int							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    max_length=len(speech_inputs[1] )				,    truncation=_lowerCAmelCase				,    return_tensors="np"				,    )
         lowercase			:Union[str, Any]							=  input_a[input_name]

         lowercase			:List[Any]							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    max_length=len(speech_inputs[1] )				,    truncation=_lowerCAmelCase )
         lowercase			:Tuple							=  input_a[input_name]

         lowercase			:Tuple							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    max_length=len(speech_inputs[1] )				,    return_tensors="np" )
         lowercase			:Tuple							=  input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
         self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
         self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
         self.assertTrue(_inputs_are_equal(_lowerCAmelCase				,    _lowerCAmelCase ) )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
         self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(_lowerCAmelCase ):
           feat_extract.pad(_lowerCAmelCase				,    truncation=_lowerCAmelCase )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(_lowerCAmelCase ):
           feat_extract.pad(_lowerCAmelCase				,    padding="longest"				,    truncation=_lowerCAmelCase )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(_lowerCAmelCase ):
           feat_extract.pad(_lowerCAmelCase				,    padding="longest"				,    truncation=_lowerCAmelCase )[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(_lowerCAmelCase ):
           feat_extract.pad(_lowerCAmelCase				,    padding="max_length"				,    truncation=_lowerCAmelCase )[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         lowercase			:int							=  12
         lowercase			:Tuple							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    max_length=len(speech_inputs[0] )				,    pad_to_multiple_of=_lowerCAmelCase				,    truncation=_lowerCAmelCase				,    )
         lowercase			:Union[str, Any]							=  input_a[input_name]

         lowercase			:Any							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    max_length=len(speech_inputs[0] )				,    pad_to_multiple_of=_lowerCAmelCase				,    )
         lowercase			:Optional[Any]							=  input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         lowercase			:List[Any]							=  len(speech_inputs[0] )
         if expected_length % pad_to_multiple_of != 0:
           lowercase			:Dict							=  ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0] ) == expected_length )
         self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
         self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )






       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         self._check_padding(numpify=_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         self._check_padding(numpify=_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         self._check_truncation(numpify=_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         self._check_truncation(numpify=_lowerCAmelCase )






       @require_torch
       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         lowercase			:Dict							=  self.feature_extraction_class(**self.feat_extract_dict )
         lowercase			:int							=  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase			:str							=  feat_extract.model_input_names[0]

         lowercase			:Optional[Any]							=  BatchFeature({input_name: speech_inputs} )

         lowercase			:Tuple							=  feat_extract.pad(_lowerCAmelCase				,    padding="longest"				,    return_tensors="np" )[input_name]
         lowercase			:Tuple							=  feat_extract.pad(_lowerCAmelCase				,    padding="longest"				,    return_tensors="pt" )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )






       @require_tf
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         lowercase			:List[Any]							=  self.feature_extraction_class(**self.feat_extract_dict )
         lowercase			:Optional[int]							=  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase			:List[Any]							=  feat_extract.model_input_names[0]

         lowercase			:List[Any]							=  BatchFeature({input_name: speech_inputs} )

         lowercase			:Union[str, Any]							=  feat_extract.pad(_lowerCAmelCase				,    padding="longest"				,    return_tensors="np" )[input_name]
         lowercase			:Dict							=  feat_extract.pad(_lowerCAmelCase				,    padding="longest"				,    return_tensors="tf" )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:str							=  self.feat_extract_dict
         lowercase			:Optional[int]							=  True
         lowercase			:Union[str, Any]							=  self.feature_extraction_class(**_lowerCAmelCase )
         lowercase			:int							=  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase			:int							=  [len(_lowerCAmelCase ) for x in speech_inputs]
         lowercase			:List[str]							=  feat_extract.model_input_names[0]

         lowercase			:int							=  BatchFeature({input_name: speech_inputs} )

         lowercase			:Dict							=  feat_extract.pad(_lowerCAmelCase				,    padding="longest"				,    return_tensors="np" )
         self.assertIn("attention_mask"				,    _lowerCAmelCase )
         self.assertListEqual(list(processed.attention_mask.shape )				,    list(processed[input_name].shape[:2] ) )
         self.assertListEqual(processed.attention_mask.sum(-1 ).tolist()				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:Union[str, Any]							=  self.feat_extract_dict
         lowercase			:Any							=  True
         lowercase			:str							=  self.feature_extraction_class(**_lowerCAmelCase )
         lowercase			:str							=  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase			:int							=  [len(_lowerCAmelCase ) for x in speech_inputs]
         lowercase			:Dict							=  feat_extract.model_input_names[0]

         lowercase			:Tuple							=  BatchFeature({input_name: speech_inputs} )
         lowercase			:str							=  min(_lowerCAmelCase )

         lowercase			:Optional[Any]							=  feat_extract.pad(
             _lowerCAmelCase				,    padding="max_length"				,    max_length=_lowerCAmelCase				,    truncation=_lowerCAmelCase				,    return_tensors="np" )
         self.assertIn("attention_mask"				,    _lowerCAmelCase )
         self.assertListEqual(
             list(processed_pad.attention_mask.shape )				,    [processed_pad[input_name].shape[0], max_length] )
         self.assertListEqual(
             processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist()				,    [max_length for x in speech_inputs] )









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

_UpperCAmelCase		:  Tuple	=   logging.get_logger(__name__)

_UpperCAmelCase		:  Dict	=   {
    "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}

# fmt: off
_UpperCAmelCase		:  Union[str, Any]	=   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
_UpperCAmelCase		:  List[Any]	=   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]




class       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  '''whisper'''
       _a           =  ['''past_key_values''']
       _a           =  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






       def __init__(      self:    int				,    _lowerCAmelCase:    str=5_18_65				,    _lowerCAmelCase:    str=80				,    _lowerCAmelCase:    int=6				,    _lowerCAmelCase:    Tuple=4				,    _lowerCAmelCase:    Union[str, Any]=6				,    _lowerCAmelCase:    List[Any]=4				,    _lowerCAmelCase:    Any=15_36				,    _lowerCAmelCase:    Union[str, Any]=15_36				,    _lowerCAmelCase:    str=0.0				,    _lowerCAmelCase:    str=0.0				,    _lowerCAmelCase:    List[Any]=5_02_57				,    _lowerCAmelCase:    Optional[Any]=True				,    _lowerCAmelCase:    Tuple=True				,    _lowerCAmelCase:    str="gelu"				,    _lowerCAmelCase:    Dict=2_56				,    _lowerCAmelCase:    Union[str, Any]=0.0				,    _lowerCAmelCase:    Any=0.0				,    _lowerCAmelCase:    Dict=0.0				,    _lowerCAmelCase:    Union[str, Any]=0.02				,    _lowerCAmelCase:    Any=False				,    _lowerCAmelCase:    List[str]=15_00				,    _lowerCAmelCase:    Tuple=4_48				,    _lowerCAmelCase:    Optional[Any]=5_02_56				,    _lowerCAmelCase:    Dict=5_02_56				,    _lowerCAmelCase:    List[Any]=5_02_56				,    _lowerCAmelCase:    Union[str, Any]=None				,    _lowerCAmelCase:    str=[2_20, 5_02_56]				,    _lowerCAmelCase:    Optional[int]=False				,    _lowerCAmelCase:    Optional[int]=2_56				,    _lowerCAmelCase:    int=False				,    _lowerCAmelCase:    Dict=0.05				,    _lowerCAmelCase:    Optional[Any]=10				,    _lowerCAmelCase:    List[str]=2				,    _lowerCAmelCase:    Tuple=0.0				,    _lowerCAmelCase:    str=10				,    _lowerCAmelCase:    Union[str, Any]=0				,    _lowerCAmelCase:    List[Any]=7				,    **_lowerCAmelCase:    Union[str, Any]				,    ):
         lowercase			:Optional[Any]							=  vocab_size
         lowercase			:Optional[int]							=  num_mel_bins
         lowercase			:Union[str, Any]							=  d_model
         lowercase			:List[Any]							=  encoder_layers
         lowercase			:Optional[Any]							=  encoder_attention_heads
         lowercase			:Union[str, Any]							=  decoder_layers
         lowercase			:List[str]							=  decoder_attention_heads
         lowercase			:Optional[int]							=  decoder_ffn_dim
         lowercase			:List[Any]							=  encoder_ffn_dim
         lowercase			:Optional[Any]							=  dropout
         lowercase			:Tuple							=  attention_dropout
         lowercase			:Tuple							=  activation_dropout
         lowercase			:Optional[Any]							=  activation_function
         lowercase			:Any							=  init_std
         lowercase			:Optional[int]							=  encoder_layerdrop
         lowercase			:Optional[int]							=  decoder_layerdrop
         lowercase			:str							=  use_cache
         lowercase			:Optional[Any]							=  encoder_layers
         lowercase			:List[Any]							=  scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase			:Any							=  max_source_positions
         lowercase			:Optional[Any]							=  max_target_positions

         # Audio Classification-specific parameters. Feel free to ignore for other classes.
         lowercase			:int							=  classifier_proj_size
         lowercase			:List[str]							=  use_weighted_layer_sum

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         lowercase			:Tuple							=  apply_spec_augment
         lowercase			:int							=  mask_time_prob
         lowercase			:Union[str, Any]							=  mask_time_length
         lowercase			:Dict							=  mask_time_min_masks
         lowercase			:Tuple							=  mask_feature_prob
         lowercase			:List[Any]							=  mask_feature_length
         lowercase			:List[Any]							=  mask_feature_min_masks

         lowercase			:Any							=  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				(							lowerCAmelCase):






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Tuple							=  OrderedDict(
             [
                 ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
             ] )
         if self.use_past:
           lowercase			:List[Any]							=  {0: "batch"}
         else:
           lowercase			:str							=  {0: "batch", 1: "decoder_sequence"}

         if self.use_past:
           self.fill_with_past_key_values_(_lowerCAmelCase				,    direction="inputs" )

         return common_inputs






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]				,    _lowerCAmelCase:    int = -1				,    _lowerCAmelCase:    int = -1				,    _lowerCAmelCase:    bool = False				,    _lowerCAmelCase:    Optional["TensorType"] = None				,    _lowerCAmelCase:    int = 2_20_50				,    _lowerCAmelCase:    float = 5.0				,    _lowerCAmelCase:    int = 2_20				,    ):
         lowercase			:List[str]							=  OrderedDict()
         lowercase			:str							=  OnnxConfig.generate_dummy_inputs(
             self				,    preprocessor=preprocessor.feature_extractor				,    batch_size=_lowerCAmelCase				,    framework=_lowerCAmelCase				,    sampling_rate=_lowerCAmelCase				,    time_duration=_lowerCAmelCase				,    frequency=_lowerCAmelCase				,    )
         lowercase			:Optional[Any]							=  encoder_inputs["input_features"].shape[2]
         lowercase			:List[str]							=  encoder_sequence_length // 2 if self.use_past else seq_length

         lowercase			:Dict							=  super().generate_dummy_inputs(
             preprocessor.tokenizer				,    _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )

         lowercase			:str							=  encoder_inputs.pop("input_features" )
         lowercase			:Optional[int]							=  decoder_inputs.pop("decoder_input_ids" )

         if "past_key_values" in decoder_inputs:
           lowercase			:List[str]							=  decoder_inputs.pop("past_key_values" )

         return dummy_inputs






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         return 1e-3



