






from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCamelCase	:		Union[str, Any]										=   {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
       from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
       import sys

       __lowerCamelCase	:		Union[str, Any]										=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













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

__lowerCamelCase	:		Optional[int]										=   logging.get_logger(__name__)

__lowerCamelCase	:		Optional[int]										=   {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
__lowerCamelCase	:		int										=   [
    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
]
__lowerCamelCase	:		Dict										=   [
    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  _lowercase   (   _A						):
      _a							:						List[Any]										=							'whisper'
      _a							:						Any										=							['past_key_values']
      _a							:						Dict										=							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
      def __init__(     self				,			a=5_1_8_6_5				,			a=8_0				,			a=6				,			a=4				,			a=6				,			a=4				,			a=1_5_3_6				,			a=1_5_3_6				,			a=0.0				,			a=0.0				,			a=5_0_2_5_7				,			a=True				,			a=True				,			a="gelu"				,			a=2_5_6				,			a=0.0				,			a=0.0				,			a=0.0				,			a=0.02				,			a=False				,			a=1_5_0_0				,			a=4_4_8				,			a=5_0_2_5_6				,			a=5_0_2_5_6				,			a=5_0_2_5_6				,			a=None				,			a=[2_2_0, 5_0_2_5_6]				,			a=False				,			a=2_5_6				,			a=False				,			a=0.05				,			a=1_0				,			a=2				,			a=0.0				,			a=1_0				,			a=0				,			a=7				,			**a				,			):
       snake_case__  :    Union[str, Any]							       =vocab_size
       snake_case__  :    int							       =num_mel_bins
       snake_case__  :    Tuple							       =d_model
       snake_case__  :    Optional[Any]							       =encoder_layers
       snake_case__  :    List[Any]							       =encoder_attention_heads
       snake_case__  :    Dict							       =decoder_layers
       snake_case__  :    Optional[Any]							       =decoder_attention_heads
       snake_case__  :    str							       =decoder_ffn_dim
       snake_case__  :    str							       =encoder_ffn_dim
       snake_case__  :    List[Any]							       =dropout
       snake_case__  :    Optional[Any]							       =attention_dropout
       snake_case__  :    Tuple							       =activation_dropout
       snake_case__  :    int							       =activation_function
       snake_case__  :    List[str]							       =init_std
       snake_case__  :    List[str]							       =encoder_layerdrop
       snake_case__  :    int							       =decoder_layerdrop
       snake_case__  :    Union[str, Any]							       =use_cache
       snake_case__  :    Tuple							       =encoder_layers
       snake_case__  :    int							       =scale_embedding  # scale factor will be sqrt(d_model) if True
       snake_case__  :    Tuple							       =max_source_positions
       snake_case__  :    Union[str, Any]							       =max_target_positions

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

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

       snake_case__  :    Optional[Any]							       =median_filter_width

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







class  _lowercase   (   _A						):
      @property
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =OrderedDict(
           [
               ("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
           ]   )
       if self.use_past:
        snake_case__  :    List[Any]							       ={0: """batch"""}
       else:
        snake_case__  :    int							       ={0: """batch""", 1: """decoder_sequence"""}

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

       return common_inputs
      def 						lowercase__					(     self				,			a				,			a = -1				,			a = -1				,			a = False				,			a = None				,			a = 2_2_0_5_0				,			a = 5.0				,			a = 2_2_0				,			):
       snake_case__  :    Union[str, Any]							       =OrderedDict()
       snake_case__  :    Optional[int]							       =OnnxConfig.generate_dummy_inputs(
           self				,			preprocessor=preprocessor.feature_extractor				,			batch_size=a				,			framework=a				,			sampling_rate=a				,			time_duration=a				,			frequency=a				,			)
       snake_case__  :    Any							       =encoder_inputs["""input_features"""].shape[2]
       snake_case__  :    Dict							       =encoder_sequence_length // 2 if self.use_past else seq_length

       snake_case__  :    List[Any]							       =super().generate_dummy_inputs(
           preprocessor.tokenizer				,			a				,			a				,			a				,			a   )

       snake_case__  :    int							       =encoder_inputs.pop("""input_features"""   )
       snake_case__  :    List[Any]							       =decoder_inputs.pop("""decoder_input_ids"""   )

       if "past_key_values" in decoder_inputs:
        snake_case__  :    int							       =decoder_inputs.pop("""past_key_values"""   )

       return dummy_inputs



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





