
"""simple docstring"""



import math
import sys

import cva
import numpy as np


def  _UpperCamelCase	(						_A					,      _A      )    ->  np.ndarray:





    """simple docstring"""

    _UpperCAmelCase          =   math.sqrt(_A      )
    _UpperCAmelCase          =   1 / (sigma * math.sqrt(2 * math.pi      ))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5      )


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A      )    ->  np.ndarray:





    """simple docstring"""

    _UpperCAmelCase          =   kernel_size // 2
    return img[x - half : x + half + 1, y - half : y + half + 1]


def  _UpperCamelCase	(						_A					,      _A      )    ->  np.ndarray:





    """simple docstring"""

    _UpperCAmelCase          =   np.zeros((kernel_size, kernel_size)      )
    for i in range(0					,      _A      ):
        for j in range(0					,      _A      ):
            _UpperCAmelCase          =   math.sqrt(
                abs(i - kernel_size // 2      ) ** 2 + abs(j - kernel_size // 2      ) ** 2      )
    return vec_gaussian(_A					,      _A      )


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      )    ->  np.ndarray:





    """simple docstring"""

    _UpperCAmelCase          =   np.zeros(img.shape      )
    _UpperCAmelCase          =   get_gauss_kernel(_A					,      _A      )
    _UpperCAmelCase    ,_UpperCAmelCase          =   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          =   get_slice(_A					,      _A					,      _A					,      _A      )
            _UpperCAmelCase          =   img_s - img_s[kernel_size // 2, kernel_size // 2]
            _UpperCAmelCase          =   vec_gaussian(_A					,      _A      )
            _UpperCAmelCase          =   np.multiply(_A					,      _A      )
            _UpperCAmelCase          =   np.multiply(_A					,      _A      )
            _UpperCAmelCase          =   np.sum(_A      ) / np.sum(_A      )
            _UpperCAmelCase          =   val
    return imga


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    _UpperCAmelCase          =   args[1] if args[1:] else """../image_data/lena.jpg"""
    _UpperCAmelCase          =   float(args[2]      ) if args[2:] else 1.0
    _UpperCAmelCase          =   float(args[3]      ) if args[3:] else 1.0
    if args[4:]:
        _UpperCAmelCase          =   int(args[4]      )
        _UpperCAmelCase          =   kernel_size + abs(kernel_size % 2 - 1      )
    else:
        _UpperCAmelCase          =   5
    return filename, spatial_variance, intensity_variance, kernel_size


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

      a					:	str							=		img / 2_5_5
      a					:	Union[str, Any]							=		out.astype('''float32''')
      a					:	str							=		bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
      a					:	List[str]							=		out * 2_5_5
      a					:	Any							=		np.uinta(out)
      cva.imshow('''output image''', out)
      cva.waitKey(0)
      cva.destroyAllWindows()

"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 		a_						( nn.Module	):







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       int = 16     ,					__UpperCamelCase			:       int = 88     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       int = 1     ,					__UpperCamelCase			:       float = 0.0     ,					__UpperCamelCase			:       int = 32     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       bool = False     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       str = "geglu"     ,					__UpperCamelCase			:       Optional[int] = None     ,					)		->Dict:






        '''simple docstring'''






        super().__init__()
        _UpperCAmelCase          =   nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=__UpperCamelCase     ,					attention_head_dim=__UpperCamelCase     ,					in_channels=__UpperCamelCase     ,					num_layers=__UpperCamelCase     ,					dropout=__UpperCamelCase     ,					norm_num_groups=__UpperCamelCase     ,					cross_attention_dim=__UpperCamelCase     ,					attention_bias=__UpperCamelCase     ,					sample_size=__UpperCamelCase     ,					num_vector_embeds=__UpperCamelCase     ,					activation_fn=__UpperCamelCase     ,					num_embeds_ada_norm=__UpperCamelCase     ,					)
                for _ in range(2     )
            ]     )

        # Variables that can be set by a pipeline:

        # The ratio of transformer1 to transformer2's output states to be combined during inference
        _UpperCAmelCase          =   0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        _UpperCAmelCase          =   [77, 2_57]

        # Which transformer to use to encode which condition.
        # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
        _UpperCAmelCase          =   [1, 0]







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       bool = True     ,					)		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   hidden_states

        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   0
        # attention_mask is not used yet
        for i in range(2     ):
            # for each of the two transformers, pass the corresponding condition tokens
            _UpperCAmelCase          =   encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            _UpperCAmelCase          =   self.transformer_index_for_condition[i]
            _UpperCAmelCase          =   self.transformers[transformer_index](
                __UpperCamelCase     ,					encoder_hidden_states=__UpperCamelCase     ,					timestep=__UpperCamelCase     ,					cross_attention_kwargs=__UpperCamelCase     ,					return_dict=__UpperCamelCase     ,					)[0]
            encoded_states.append(encoded_state - input_states     )
            tokens_start += self.condition_lengths[i]

        _UpperCAmelCase          =   encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        _UpperCAmelCase          =   output_states + input_states

        if not return_dict:
            return (output_states,)

        return TransformeraDModelOutput(sample=__UpperCamelCase     )