



'''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

A_							: Tuple						 =	logging.get_logger(__name__)

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

# fmt: off
A_							: Tuple						 =	[
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
    7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
    1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
    4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
    1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
    1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
    3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
A_							: Optional[Any]						 =	[
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
    8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
    3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
    7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
    1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
    2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
    4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """whisper"""
 UpperCAmelCase							=  ["""past_key_values"""]
 UpperCAmelCase							=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







 def __init__(							self  ,a_=51_865  ,a_=80  ,a_=6  ,a_=4  ,a_=6  ,a_=4  ,a_=1_536  ,a_=1_536  ,a_=0.0  ,a_=0.0  ,a_=50_257  ,a_=True  ,a_=True  ,a_="gelu"  ,a_=256  ,a_=0.0  ,a_=0.0  ,a_=0.0  ,a_=0.02  ,a_=False  ,a_=1_500  ,a_=448  ,a_=50_256  ,a_=50_256  ,a_=50_256  ,a_=None  ,a_=[220, 50_256]  ,a_=False  ,a_=256  ,a_=False  ,a_=0.05  ,a_=10  ,a_=2  ,a_=0.0  ,a_=10  ,a_=0  ,a_=7  ,**a_  ,)		->   List[str]:
        _UpperCAmelCase  :						Optional[Any]			     = vocab_size
        _UpperCAmelCase  :						str			     = num_mel_bins
        _UpperCAmelCase  :						Any			     = d_model
        _UpperCAmelCase  :						Optional[Any]			     = encoder_layers
        _UpperCAmelCase  :						List[str]			     = encoder_attention_heads
        _UpperCAmelCase  :						Union[str, Any]			     = decoder_layers
        _UpperCAmelCase  :						Tuple			     = decoder_attention_heads
        _UpperCAmelCase  :						Dict			     = decoder_ffn_dim
        _UpperCAmelCase  :						Optional[Any]			     = encoder_ffn_dim
        _UpperCAmelCase  :						List[Any]			     = dropout
        _UpperCAmelCase  :						List[str]			     = attention_dropout
        _UpperCAmelCase  :						Optional[int]			     = activation_dropout
        _UpperCAmelCase  :						Optional[Any]			     = activation_function
        _UpperCAmelCase  :						Union[str, Any]			     = init_std
        _UpperCAmelCase  :						Optional[Any]			     = encoder_layerdrop
        _UpperCAmelCase  :						List[str]			     = decoder_layerdrop
        _UpperCAmelCase  :						Dict			     = use_cache
        _UpperCAmelCase  :						List[str]			     = encoder_layers
        _UpperCAmelCase  :						List[str]			     = scale_embedding  # scale factor will be sqrt(d_model) if True
        _UpperCAmelCase  :						Any			     = max_source_positions
        _UpperCAmelCase  :						List[str]			     = max_target_positions

        # Audio Classification-specific parameters. Feel free to ignore for other classes.
        _UpperCAmelCase  :						str			     = classifier_proj_size
        _UpperCAmelCase  :						Dict			     = use_weighted_layer_sum

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

        _UpperCAmelCase  :						Union[str, 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       (  _lowerCamelCase				):


 """simple docstring"""


 @property
 def _snake_case (							self							)		->   Mapping[str, Mapping[int, str]]:
        _UpperCAmelCase  :						Optional[Any]			     = OrderedDict(
            [
                ("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
            ]							)
        if self.use_past:
               _UpperCAmelCase  :						Any			     = {0: """batch"""}
        else:
               _UpperCAmelCase  :						List[str]			     = {0: """batch""", 1: """decoder_sequence"""}

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

        return common_inputs







 def _snake_case (							self  ,a_  ,a_ = -1  ,a_ = -1  ,a_ = False  ,a_ = None  ,a_ = 22_050  ,a_ = 5.0  ,a_ = 220  ,)		->   Mapping[str, Any]:
        _UpperCAmelCase  :						Optional[int]			     = OrderedDict()
        _UpperCAmelCase  :						Any			     = OnnxConfig.generate_dummy_inputs(
            self  ,preprocessor=preprocessor.feature_extractor  ,batch_size=a_  ,framework=a_  ,sampling_rate=a_  ,time_duration=a_  ,frequency=a_  ,)
        _UpperCAmelCase  :						Union[str, Any]			     = encoder_inputs["""input_features"""].shape[2]
        _UpperCAmelCase  :						Optional[Any]			     = encoder_sequence_length // 2 if self.use_past else seq_length

        _UpperCAmelCase  :						Dict			     = super().generate_dummy_inputs(
            preprocessor.tokenizer  ,a_  ,a_  ,a_  ,a_							)

        _UpperCAmelCase  :						Union[str, Any]			     = encoder_inputs.pop("""input_features"""							)
        _UpperCAmelCase  :						Union[str, Any]			     = decoder_inputs.pop("""decoder_input_ids"""							)

        if "past_key_values" in decoder_inputs:
               _UpperCAmelCase  :						Tuple			     = decoder_inputs.pop("""past_key_values"""							)

        return dummy_inputs







 @property
 def _snake_case (							self							)		->   float:
        return 1E-3








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


A_							: int						 =	{"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
   from .tokenization_bertweet import BertweetTokenizer

else:
   import sys

   A_							: int						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



