

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								=			logging.get_logger(__name__)

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

# fmt: off
__UpperCAmelCase								=			[
    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, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377,
    1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211,
    4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786,
    11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791,
    17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409,
    34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361
]
__UpperCAmelCase								=			[
    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, 1_350, 1_853, 1_982, 2_460, 2_627,
    3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647,
    7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793,
    14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675,
    22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865,
    42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362
]



class SCREAMING_SNAKE_CASE     (  a_			):
  lowerCamelCase   :      str										="whisper"
  lowerCamelCase   :      Tuple										=["past_key_values"]
  lowerCamelCase   :      Any										={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







  def __init__(      self			:   Tuple	,							lowerCAmelCase			:   List[Any]=5_18_65	,							lowerCAmelCase			:   Any=80	,							lowerCAmelCase			:   Dict=6	,							lowerCAmelCase			:   Optional[Any]=4	,							lowerCAmelCase			:   Optional[Any]=6	,							lowerCAmelCase			:   int=4	,							lowerCAmelCase			:   Optional[Any]=15_36	,							lowerCAmelCase			:   List[Any]=15_36	,							lowerCAmelCase			:   Tuple=0.0	,							lowerCAmelCase			:   Any=0.0	,							lowerCAmelCase			:   Dict=5_02_57	,							lowerCAmelCase			:   int=True	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   str="gelu"	,							lowerCAmelCase			:   str=2_56	,							lowerCAmelCase			:   int=0.0	,							lowerCAmelCase			:   Dict=0.0	,							lowerCAmelCase			:   Any=0.0	,							lowerCAmelCase			:   str=0.02	,							lowerCAmelCase			:   Union[str, Any]=False	,							lowerCAmelCase			:   Tuple=15_00	,							lowerCAmelCase			:   List[str]=4_48	,							lowerCAmelCase			:   Optional[int]=5_02_56	,							lowerCAmelCase			:   List[Any]=5_02_56	,							lowerCAmelCase			:   List[str]=5_02_56	,							lowerCAmelCase			:   Union[str, Any]=None	,							lowerCAmelCase			:   Union[str, Any]=[2_20, 5_02_56]	,							lowerCAmelCase			:   List[str]=False	,							lowerCAmelCase			:   int=2_56	,							lowerCAmelCase			:   int=False	,							lowerCAmelCase			:   List[str]=0.05	,							lowerCAmelCase			:   Any=10	,							lowerCAmelCase			:   Optional[Any]=2	,							lowerCAmelCase			:   List[str]=0.0	,							lowerCAmelCase			:   List[Any]=10	,							lowerCAmelCase			:   Optional[Any]=0	,							lowerCAmelCase			:   str=7	,							**lowerCAmelCase			:   Tuple	,							)							->     Union[str, Any]:






     """simple docstring"""







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

     # Audio Classification-specific parameters. Feel free to ignore for other classes.
     __lowerCAmelCase  :      Optional[Any]    =						classifier_proj_size
     __lowerCAmelCase  :      Union[str, Any]    =						use_weighted_layer_sum

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

     __lowerCAmelCase  :      List[str]    =						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 SCREAMING_SNAKE_CASE     (  a_			):







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






     """simple docstring"""







     __lowerCAmelCase  :      str    =						OrderedDict(
         [
             ("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
         ]   )
     if self.use_past:
        __lowerCAmelCase  :      str    =						{0: """batch"""}
     else:
        __lowerCAmelCase  :      Union[str, Any]    =						{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			:   Any	,							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	,							)							->     Mapping[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						OrderedDict()
     __lowerCAmelCase  :      int    =						OnnxConfig.generate_dummy_inputs(
         self	,							preprocessor=preprocessor.feature_extractor	,							batch_size=lowerCAmelCase	,							framework=lowerCAmelCase	,							sampling_rate=lowerCAmelCase	,							time_duration=lowerCAmelCase	,							frequency=lowerCAmelCase	,							)
     __lowerCAmelCase  :      Dict    =						encoder_inputs["""input_features"""].shape[2]
     __lowerCAmelCase  :      Tuple    =						encoder_sequence_length // 2 if self.use_past else seq_length

     __lowerCAmelCase  :      List[Any]    =						super().generate_dummy_inputs(
         preprocessor.tokenizer	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )

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

     if "past_key_values" in decoder_inputs:
        __lowerCAmelCase  :      Union[str, Any]    =						decoder_inputs.pop("""past_key_values"""   )

     return dummy_inputs







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     float:






     """simple docstring"""







     return 1e-3





import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate



@dataclass(frozen=a_			)
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      str										=field(default="audio-classification"       ,							metadata={"include_in_asdict_even_if_is_default": True}			)
  lowerCamelCase   :      ClassVar[Features]										=Features({"audio": Audio()}			)
  lowerCamelCase   :      ClassVar[Features]										=Features({"labels": ClassLabel}			)
  lowerCamelCase   :      str										="audio"
  lowerCamelCase   :      str										="labels"







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   Dict   )							->     Dict:






     """simple docstring"""







     if self.label_column not in features:
        raise ValueError(f'''Column {self.label_column} is not present in features.'''   )
     if not isinstance(features[self.label_column]	,							lowerCAmelCase   ):
        raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''   )
     __lowerCAmelCase  :      Tuple    =						copy.deepcopy(self   )
     __lowerCAmelCase  :      Tuple    =						self.label_schema.copy()
     __lowerCAmelCase  :      Union[str, Any]    =						features[self.label_column]
     __lowerCAmelCase  :      Any    =						label_schema
     return task_template







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Dict[str, str]:






     """simple docstring"""







     return {
         self.audio_column: "audio",
         self.label_column: "labels",
     }


