



'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase: Tuple							=						logging.get_logger(__name__)

lowerCAmelCase: Tuple							=						{
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """speech_to_text_2"""
    lowercase__			=   ["""past_key_values"""]
    lowercase__			=   {"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}







    def __init__(				self			:  Union[str, Any]						,      __snake_case			:  Optional[int]=1_00_00						,      __snake_case			:  Optional[int]=6						,      __snake_case			:  Optional[int]=20_48						,      __snake_case			:  int=4						,      __snake_case			:  List[str]=0.0						,      __snake_case			:  Union[str, Any]=True						,      __snake_case			:  Dict="relu"						,      __snake_case			:  int=2_56						,      __snake_case			:  List[Any]=0.1						,      __snake_case			:  Optional[int]=0.0						,      __snake_case			:  List[Any]=0.0						,      __snake_case			:  int=0.02						,      __snake_case			:  int=2						,      __snake_case			:  Tuple=True						,      __snake_case			:  Optional[Any]=1						,      __snake_case			:  Union[str, Any]=0						,      __snake_case			:  str=2						,      __snake_case			:  str=10_24						,      **__snake_case			:  Dict						,      ):
      a						: List[str]					       =					vocab_size
      a						: List[str]					       =					d_model
      a						: Any					       =					decoder_ffn_dim
      a						: str					       =					decoder_layers
      a						: Optional[int]					       =					decoder_attention_heads
      a						: Tuple					       =					dropout
      a						: List[Any]					       =					attention_dropout
      a						: List[Any]					       =					activation_dropout
      a						: Dict					       =					activation_function
      a						: int					       =					init_std
      a						: List[str]					       =					decoder_layerdrop
      a						: Optional[int]					       =					use_cache
      a						: Any					       =					decoder_layers
      a						: List[str]					       =					scale_embedding  # scale factor will be sqrt(d_model) if True
      a						: List[Any]					       =					max_target_positions

      super().__init__(
          pad_token_id=__snake_case						,      bos_token_id=__snake_case						,      eos_token_id=__snake_case						,      decoder_start_token_id=__snake_case						,      **__snake_case						,      )




'''simple docstring'''






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class 							a__:







    def __init__(				self			:  Optional[int]     ):
      a						: int					       =					''
      a						: List[str]					       =					''
      a						: int					       =					[]
      a						: Optional[Any]					       =					0
      a						: Optional[Any]					       =					2_56
      a						: int					       =					0
      a						: Optional[int]					       =					0
      a						: str					       =					0
      a						: int					       =					0







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  int     ):
      a						: Optional[Any]					       =					cva.imread(__snake_case						,      0     )
      a						: int					       =					copy.deepcopy(self.img     )
      a       ,     a       ,     a						: Optional[int]					       =					plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]						,      label='x'     )
      a						: str					       =					np.sum(__snake_case     )
      for i in range(len(__snake_case     )     ):
        a						: List[str]					       =					x[i] / self.k
        self.sk += prk
        a						: List[Any]					       =					(self.L - 1) * self.sk
        if self.rem != 0:
          a						: Union[str, Any]					       =					int(last % last     )
        a						: int					       =					int(last + 1 if self.rem >= 0.5 else last     )
        self.last_list.append(__snake_case     )
        a						: int					       =					int(np.ma.count(self.img     ) / self.img[1].size     )
        a						: Dict					       =					self.img[1].size
      for i in range(self.number_of_cols     ):
        for j in range(self.number_of_rows     ):
          a						: Tuple					       =					self.img[j][i]
          if num != self.last_list[num]:
            a						: Union[str, Any]					       =					self.last_list[num]
      cva.imwrite('output_data/output.jpg'						,      self.img     )







    def       lowercase_	(				self			:  Union[str, Any]     ):
      plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]     )







    def       lowercase_	(				self			:  Any     ):
      cva.imshow('Output-Image'						,      self.img     )
      cva.imshow('Input-Image'						,      self.original_image     )
      cva.waitKey(50_00     )
      cva.destroyAllWindows()


if __name__ == "__main__":
     lowerCAmelCase: Dict							=						os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
     lowerCAmelCase: Optional[Any]							=						ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()