






import math
import sys

import cva
import numpy as np






def 							lowerCamelCase__ (       __lowerCamelCase     :	np.ndarray    ,						__lowerCamelCase     :	float						):
  # For applying gaussian function for each element in matrix.
  __UpperCAmelCase				:      int	 =    math.sqrt(__lowerCamelCase						)
  __UpperCAmelCase				:      Optional[Any]	 =    1 / (sigma * math.sqrt(2 * math.pi						))
  return cons * np.exp(-((img / sigma) ** 2) * 0.5						)






def 							lowerCamelCase__ (       __lowerCamelCase     :	np.ndarray    ,						__lowerCamelCase     :	int    ,						__lowerCamelCase     :	int    ,						__lowerCamelCase     :	int						):
  __UpperCAmelCase				:      List[Any]	 =    kernel_size // 2
  return img[x - half : x + half + 1, y - half : y + half + 1]






def 							lowerCamelCase__ (       __lowerCamelCase     :	int    ,						__lowerCamelCase     :	float						):
  # Creates a gaussian kernel of given dimension.
  __UpperCAmelCase				:      Union[str, Any]	 =    np.zeros((kernel_size, kernel_size)						)
  for i in range(0    ,						__lowerCamelCase						):
    for j in range(0    ,						__lowerCamelCase						):
      __UpperCAmelCase				:      str	 =    math.sqrt(
          abs(i - kernel_size // 2						) ** 2 + abs(j - kernel_size // 2						) ** 2						)
  return vec_gaussian(__lowerCamelCase    ,						__lowerCamelCase						)






def 							lowerCamelCase__ (       __lowerCamelCase     :	np.ndarray    ,						__lowerCamelCase     :	float    ,						__lowerCamelCase     :	float    ,						__lowerCamelCase     :	int    ,						):
  __UpperCAmelCase				:      Optional[Any]	 =    np.zeros(img.shape						)
  __UpperCAmelCase				:      int	 =    get_gauss_kernel(__lowerCamelCase    ,						__lowerCamelCase						)
  __UpperCAmelCase ,					__UpperCAmelCase				:      Tuple	 =    img.shape
  for i in range(kernel_size // 2    ,						size_x - kernel_size // 2						):
    for j in range(kernel_size // 2    ,						size_y - kernel_size // 2						):
      __UpperCAmelCase				:      int	 =    get_slice(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase						)
      __UpperCAmelCase				:      Dict	 =    img_s - img_s[kernel_size // 2, kernel_size // 2]
      __UpperCAmelCase				:      Optional[Any]	 =    vec_gaussian(__lowerCamelCase    ,						__lowerCamelCase						)
      __UpperCAmelCase				:      Any	 =    np.multiply(__lowerCamelCase    ,						__lowerCamelCase						)
      __UpperCAmelCase				:      str	 =    np.multiply(__lowerCamelCase    ,						__lowerCamelCase						)
      __UpperCAmelCase				:      List[str]	 =    np.sum(__lowerCamelCase						) / np.sum(__lowerCamelCase						)
      __UpperCAmelCase				:      List[Any]	 =    val
  return imga






def 							lowerCamelCase__ (       __lowerCamelCase     :	list						):
  __UpperCAmelCase				:      List[str]	 =    args[1] if args[1:] else """../image_data/lena.jpg"""
  __UpperCAmelCase				:      Optional[Any]	 =    float(args[2]						) if args[2:] else 1.0
  __UpperCAmelCase				:      Dict	 =    float(args[3]						) if args[3:] else 1.0
  if args[4:]:
    __UpperCAmelCase				:      Optional[int]	 =    int(args[4]						)
    __UpperCAmelCase				:      List[str]	 =    kernel_size + abs(kernel_size % 2 - 1						)
  else:
    __UpperCAmelCase				:      int	 =    5
  return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
       a	,a	,a	,a						:			Optional[Any]     	=					parse_args(sys.argv)
       a						:			Optional[int]     	=					cva.imread(filename, 0)
       cva.imshow("input image", img)

       a						:			int     	=					img / 255
       a						:			Union[str, Any]     	=					out.astype("float32")
       a						:			Any     	=					bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
       a						:			Optional[int]     	=					out * 255
       a						:			Union[str, Any]     	=					np.uinta(out)
       cva.imshow("output image", out)
       cva.waitKey(0)
       cva.destroyAllWindows()







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a_								=       logging.get_logger(__name__)

a_								=       {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""t5"""
 lowerCAmelCase__							       =	["""past_key_values"""]
 lowerCAmelCase__							       =	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




 def __init__(    self						,					__UpperCAmelCase=32128						,					__UpperCAmelCase=512						,					__UpperCAmelCase=64						,					__UpperCAmelCase=2048						,					__UpperCAmelCase=6						,					__UpperCAmelCase=None						,					__UpperCAmelCase=8						,					__UpperCAmelCase=32						,					__UpperCAmelCase=128						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=1E-6						,					__UpperCAmelCase=1.0						,					__UpperCAmelCase="relu"						,					__UpperCAmelCase=True						,					__UpperCAmelCase=True						,					__UpperCAmelCase=0						,					__UpperCAmelCase=1						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     vocab_size
    __lowerCamelCase    				=     d_model
    __lowerCamelCase    				=     d_kv
    __lowerCamelCase    				=     d_ff
    __lowerCamelCase    				=     num_layers
    __lowerCamelCase    				=     (
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    __lowerCamelCase    				=     num_heads
    __lowerCamelCase    				=     relative_attention_num_buckets
    __lowerCamelCase    				=     relative_attention_max_distance
    __lowerCamelCase    				=     dropout_rate
    __lowerCamelCase    				=     layer_norm_epsilon
    __lowerCamelCase    				=     initializer_factor
    __lowerCamelCase    				=     feed_forward_proj
    __lowerCamelCase    				=     use_cache

    __lowerCamelCase    				=     self.feed_forward_proj.split('''-'''					)
    __lowerCamelCase    				=     act_info[-1]
    __lowerCamelCase    				=     act_info[0] == '''gated'''

    if len(__UpperCAmelCase					) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase					) > 2:
       raise ValueError(
           F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
           '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
           '''\'gated-gelu\' or \'relu\''''					)

    # for backwards compatibility
    if feed_forward_proj == "gated-gelu":
       __lowerCamelCase    				=     '''gelu_new'''

    super().__init__(
        pad_token_id=__UpperCAmelCase						,					eos_token_id=__UpperCAmelCase						,					is_encoder_decoder=__UpperCAmelCase						,					**__UpperCAmelCase						,					)



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     {
        '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
        '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
    }
    if self.use_past:
       __lowerCamelCase    				=     '''past_encoder_sequence + sequence'''
       __lowerCamelCase    				=     {0: '''batch'''}
       __lowerCamelCase    				=     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    else:
       __lowerCamelCase    				=     {0: '''batch''', 1: '''decoder_sequence'''}
       __lowerCamelCase    				=     {0: '''batch''', 1: '''decoder_sequence'''}

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

    return common_inputs




 @property
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return 13






