



'''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

_lowerCamelCase	:       Union[str, Any]      	=	logging.get_logger(__name__)

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

# fmt: off
_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, 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
]
_lowerCamelCase	:       List[str]      	=	[
    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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _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      :					Optional[Any]  ,						UpperCamelCase__      :					List[str]=5_1_8_6_5  ,						UpperCamelCase__      :					str=8_0  ,						UpperCamelCase__      :					Tuple=6  ,						UpperCamelCase__      :					Optional[int]=4  ,						UpperCamelCase__      :					int=6  ,						UpperCamelCase__      :					Optional[int]=4  ,						UpperCamelCase__      :					Optional[Any]=1_5_3_6  ,						UpperCamelCase__      :					int=1_5_3_6  ,						UpperCamelCase__      :					Union[str, Any]=0.0  ,						UpperCamelCase__      :					Tuple=0.0  ,						UpperCamelCase__      :					Tuple=5_0_2_5_7  ,						UpperCamelCase__      :					Union[str, Any]=True  ,						UpperCamelCase__      :					List[str]=True  ,						UpperCamelCase__      :					Optional[int]="gelu"  ,						UpperCamelCase__      :					Tuple=2_5_6  ,						UpperCamelCase__      :					List[str]=0.0  ,						UpperCamelCase__      :					List[Any]=0.0  ,						UpperCamelCase__      :					Any=0.0  ,						UpperCamelCase__      :					Optional[int]=0.0_2  ,						UpperCamelCase__      :					Any=False  ,						UpperCamelCase__      :					Tuple=1_5_0_0  ,						UpperCamelCase__      :					List[str]=4_4_8  ,						UpperCamelCase__      :					List[Any]=5_0_2_5_6  ,						UpperCamelCase__      :					Any=5_0_2_5_6  ,						UpperCamelCase__      :					Dict=5_0_2_5_6  ,						UpperCamelCase__      :					Optional[Any]=None  ,						UpperCamelCase__      :					Tuple=[2_2_0, 5_0_2_5_6]  ,						UpperCamelCase__      :					int=False  ,						UpperCamelCase__      :					List[str]=2_5_6  ,						UpperCamelCase__      :					str=False  ,						UpperCamelCase__      :					Tuple=0.0_5  ,						UpperCamelCase__      :					Any=1_0  ,						UpperCamelCase__      :					Dict=2  ,						UpperCamelCase__      :					Union[str, Any]=0.0  ,						UpperCamelCase__      :					str=1_0  ,						UpperCamelCase__      :					List[Any]=0  ,						UpperCamelCase__      :					Tuple=7  ,						**UpperCamelCase__      :					Tuple  ,						):


       """simple docstring"""







       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=UpperCamelCase__  ,						bos_token_id=UpperCamelCase__  ,						eos_token_id=UpperCamelCase__  ,						is_encoder_decoder=UpperCamelCase__  ,						decoder_start_token_id=UpperCamelCase__  ,						suppress_tokens=UpperCamelCase__  ,						begin_suppress_tokens=UpperCamelCase__  ,						**UpperCamelCase__  ,						)

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    @property
    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       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_(UpperCamelCase__  ,						direction='inputs' )

       return common_inputs






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					Optional["TensorType"] = None  ,						UpperCamelCase__      :					int = 2_2_0_5_0  ,						UpperCamelCase__      :					float = 5.0  ,						UpperCamelCase__      :					int = 2_2_0  ,						):


       """simple docstring"""







       UpperCamelCase					=  OrderedDict()
       UpperCamelCase					=  OnnxConfig.generate_dummy_inputs(
           self  ,						preprocessor=preprocessor.feature_extractor  ,						batch_size=UpperCamelCase__  ,						framework=UpperCamelCase__  ,						sampling_rate=UpperCamelCase__  ,						time_duration=UpperCamelCase__  ,						frequency=UpperCamelCase__  ,						)
       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  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

       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  A       (  self      :					Dict ):


       """simple docstring"""







       return 1E-3






'''simple docstring'''


from PIL import Image







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Image:






   """simple docstring"""

   def brightness(A__    ) -> float:
      return 128 + level + (c - 128)

   if not -255.0 <= level <= 255.0:
      raise ValueError('level must be between -255.0 (black) and 255.0 (white)'    )
   return img.point(A__    )


if __name__ == "__main__":
   # Load image
   with Image.open("image_data/lena.jpg") as img:
      # Change brightness to 100
      _lowerCamelCase	:       List[str]      	=	change_brightness(img, 100)
      brigt_img.save("image_data/lena_brightness.png", format="png")

