





'''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__			:					List[Any]       						=						logging.get_logger(__name__)

a__			:					int       						=						{
    'openai/whisper-base': 'https://huggingface.co/openai/whisper-base/resolve/main/config.json',
}

# fmt: off
a__			:					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, 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
]
a__			:					Tuple       						=						[
    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     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''
       _lowerCamelCase								="whisper"
       _lowerCamelCase								=["past_key_values"]
       _lowerCamelCase								={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

       def __init__( self					:				List[Any]       ,     a__					:				Any=51865       ,     a__					:				Any=80       ,     a__					:				Dict=6       ,     a__					:				int=4       ,     a__					:				int=6       ,     a__					:				str=4       ,     a__					:				int=1536       ,     a__					:				Optional[Any]=1536       ,     a__					:				str=0.0       ,     a__					:				Optional[int]=0.0       ,     a__					:				Optional[int]=50257       ,     a__					:				int=True       ,     a__					:				Optional[int]=True       ,     a__					:				str="gelu"       ,     a__					:				List[str]=256       ,     a__					:				Union[str, Any]=0.0       ,     a__					:				List[str]=0.0       ,     a__					:				Any=0.0       ,     a__					:				str=0.02       ,     a__					:				str=False       ,     a__					:				int=1500       ,     a__					:				Tuple=448       ,     a__					:				Optional[Any]=50256       ,     a__					:				Optional[Any]=50256       ,     a__					:				Optional[Any]=50256       ,     a__					:				Tuple=None       ,     a__					:				List[Any]=[220, 50256]       ,     a__					:				Optional[int]=False       ,     a__					:				Optional[Any]=256       ,     a__					:				Any=False       ,     a__					:				int=0.05       ,     a__					:				Optional[Any]=10       ,     a__					:				Dict=2       ,     a__					:				Optional[Any]=0.0       ,     a__					:				Tuple=10       ,     a__					:				Any=0       ,     a__					:				str=7       ,     **a__					:				Any       ,     ):
              UpperCAmelCase  =						vocab_size
              UpperCAmelCase  =						num_mel_bins
              UpperCAmelCase  =						d_model
              UpperCAmelCase  =						encoder_layers
              UpperCAmelCase  =						encoder_attention_heads
              UpperCAmelCase  =						decoder_layers
              UpperCAmelCase  =						decoder_attention_heads
              UpperCAmelCase  =						decoder_ffn_dim
              UpperCAmelCase  =						encoder_ffn_dim
              UpperCAmelCase  =						dropout
              UpperCAmelCase  =						attention_dropout
              UpperCAmelCase  =						activation_dropout
              UpperCAmelCase  =						activation_function
              UpperCAmelCase  =						init_std
              UpperCAmelCase  =						encoder_layerdrop
              UpperCAmelCase  =						decoder_layerdrop
              UpperCAmelCase  =						use_cache
              UpperCAmelCase  =						encoder_layers
              UpperCAmelCase  =						scale_embedding  # scale factor will be sqrt(d_model) if True
              UpperCAmelCase  =						max_source_positions
              UpperCAmelCase  =						max_target_positions

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

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

              UpperCAmelCase  =						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     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''

       @property
       def 			__snake_case      ( self					:				List[str]						):
              UpperCAmelCase  =						OrderedDict(
                  [
                      ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
                  ]						)
              if self.use_past:
                     UpperCAmelCase  =						{0: '''batch'''}
              else:
                     UpperCAmelCase  =						{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					:				str       ,     a__					:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,     a__					:				int = -1       ,     a__					:				int = -1       ,     a__					:				bool = False       ,     a__					:				Optional["TensorType"] = None       ,     a__					:				int = 22050       ,     a__					:				float = 5.0       ,     a__					:				int = 220       ,     ):
              UpperCAmelCase  =						OrderedDict()
              UpperCAmelCase  =						OnnxConfig.generate_dummy_inputs(
                  self       ,     preprocessor=preprocessor.feature_extractor       ,     batch_size=a__       ,     framework=a__       ,     sampling_rate=a__       ,     time_duration=a__       ,     frequency=a__       ,     )
              UpperCAmelCase  =						encoder_inputs['''input_features'''].shape[2]
              UpperCAmelCase  =						encoder_sequence_length // 2 if self.use_past else seq_length

              UpperCAmelCase  =						super().generate_dummy_inputs(
                  preprocessor.tokenizer       ,     a__       ,     a__       ,     a__       ,     a__						)

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

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

              return dummy_inputs



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







"""simple docstring"""







import requests

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			List[str]						    = {"Content-Type": "application/json"}
						snake_case   :			int						    = requests.post(lowerCamelCase_     ,		json={"text": message_body}     ,		headers=lowerCamelCase_ )
						if response.status_code != 2_0_0:
												snake_case   :			str						    = (
												    "Request to slack returned an error "
												    f'''{response.status_code}, the response is:\n{response.text}'''
												)
												raise ValueError(lowerCamelCase_ )


if __name__ == "__main__":
			# Set the slack url to the one provided by Slack when you create the webhook at
			# https://my.slack.com/services/new/incoming-webhook/
			send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')



