"""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

__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, 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, 1_0563, 1_0786,
    1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791,
    1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409,
    3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361
]
__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, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793,
    1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675,
    2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865,
    4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362
]




class  UpperCamelCase					(				snake_case__   ):
       SCREAMING_SNAKE_CASE_			=      "whisper"
       SCREAMING_SNAKE_CASE_			=      ["past_key_values"]
       SCREAMING_SNAKE_CASE_			=      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







       def __init__( self,							lowerCAmelCase__=5_1865,							lowerCAmelCase__=80,							lowerCAmelCase__=6,							lowerCAmelCase__=4,							lowerCAmelCase__=6,							lowerCAmelCase__=4,							lowerCAmelCase__=1536,							lowerCAmelCase__=1536,							lowerCAmelCase__=0.0,							lowerCAmelCase__=0.0,							lowerCAmelCase__=5_0257,							lowerCAmelCase__=True,							lowerCAmelCase__=True,							lowerCAmelCase__="gelu",							lowerCAmelCase__=256,							lowerCAmelCase__=0.0,							lowerCAmelCase__=0.0,							lowerCAmelCase__=0.0,							lowerCAmelCase__=0.02,							lowerCAmelCase__=False,							lowerCAmelCase__=1500,							lowerCAmelCase__=448,							lowerCAmelCase__=5_0256,							lowerCAmelCase__=5_0256,							lowerCAmelCase__=5_0256,							lowerCAmelCase__=None,							lowerCAmelCase__=[220, 5_0256],							lowerCAmelCase__=False,							lowerCAmelCase__=256,							lowerCAmelCase__=False,							lowerCAmelCase__=0.05,							lowerCAmelCase__=10,							lowerCAmelCase__=2,							lowerCAmelCase__=0.0,							lowerCAmelCase__=10,							lowerCAmelCase__=0,							lowerCAmelCase__=7,							**lowerCAmelCase__,							)      ->      Union[str, Any]:
            snake_case_					       =					vocab_size
            snake_case_					       =					num_mel_bins
            snake_case_					       =					d_model
            snake_case_					       =					encoder_layers
            snake_case_					       =					encoder_attention_heads
            snake_case_					       =					decoder_layers
            snake_case_					       =					decoder_attention_heads
            snake_case_					       =					decoder_ffn_dim
            snake_case_					       =					encoder_ffn_dim
            snake_case_					       =					dropout
            snake_case_					       =					attention_dropout
            snake_case_					       =					activation_dropout
            snake_case_					       =					activation_function
            snake_case_					       =					init_std
            snake_case_					       =					encoder_layerdrop
            snake_case_					       =					decoder_layerdrop
            snake_case_					       =					use_cache
            snake_case_					       =					encoder_layers
            snake_case_					       =					scale_embedding  # scale factor will be sqrt(d_model) if True
            snake_case_					       =					max_source_positions
            snake_case_					       =					max_target_positions

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

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

            snake_case_					       =					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  UpperCamelCase					(				snake_case__   ):







       @property
       def 		a_						( self)      ->      str:
            snake_case_					       =					OrderedDict(
                [
                    ('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
                ])
            if self.use_past:
                 snake_case_					       =					{0: "batch"}
            else:
                 snake_case_					       =					{0: "batch", 1: "decoder_sequence"}

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

            return common_inputs







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = -1,							lowerCAmelCase__ = -1,							lowerCAmelCase__ = False,							lowerCAmelCase__ = None,							lowerCAmelCase__ = 2_2050,							lowerCAmelCase__ = 5.0,							lowerCAmelCase__ = 220,							)      ->      Optional[int]:
            snake_case_					       =					OrderedDict()
            snake_case_					       =					OnnxConfig.generate_dummy_inputs(
                self,							preprocessor=preprocessor.feature_extractor,							batch_size=lowerCAmelCase__,							framework=lowerCAmelCase__,							sampling_rate=lowerCAmelCase__,							time_duration=lowerCAmelCase__,							frequency=lowerCAmelCase__,							)
            snake_case_					       =					encoder_inputs["input_features"].shape[2]
            snake_case_					       =					encoder_sequence_length // 2 if self.use_past else seq_length

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

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

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

            return dummy_inputs







       @property
       def 		a_						( self)      ->      Tuple:
            return 1e-3


'''simple docstring'''




__SCREAMING_SNAKE_CASE       :     Dict     =  8.3_1_4_4_6_2  # Unit - J mol-1 K-1

def 	UpperCamelCase_							(					_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float    )    ->    float:


       """simple docstring"""







       if moles < 0 or kelvin < 0 or volume < 0:
              raise ValueError("Invalid inputs. Enter positive value."    )
       return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume

def 	UpperCamelCase_							(					_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float			,	_UpperCAmelCase  :      float    )    ->    float:


       """simple docstring"""







       if moles < 0 or kelvin < 0 or pressure < 0:
              raise ValueError("Invalid inputs. Enter positive value."    )
       return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
  from doctest import testmod

  testmod()

