





'''simple docstring'''
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation
def     lowercase__       (       __lowercase						:  torch.Tensor    ,  __lowercase						:  int    ,  __lowercase						:  bool = False    ,  __lowercase						:  float = 1    ,  __lowercase						:  float = 1    ,  __lowercase						:  int = 10000    ,  )			->				str:



       """simple docstring"""






       assert len(timesteps.shape					) == 1, "Timesteps should be a 1d-array"

       __UpperCamelCase	=  embedding_dim // 2
       __UpperCamelCase	=  -math.log(__lowercase					) * torch.arange(
           start=0    ,  end=__lowercase    ,  dtype=torch.floataa    ,  device=timesteps.device					)
       __UpperCamelCase	=  exponent / (half_dim - downscale_freq_shift)

       __UpperCamelCase	=  torch.exp(__lowercase					)
       __UpperCamelCase	=  timesteps[:, None].float() * emb[None, :]

       # scale embeddings
       __UpperCamelCase	=  scale * emb

       # concat sine and cosine embeddings
       __UpperCamelCase	=  torch.cat([torch.sin(__lowercase					), torch.cos(__lowercase					)]    ,  dim=-1					)

       # flip sine and cosine embeddings
       if flip_sin_to_cos:
              __UpperCamelCase	=  torch.cat([emb[:, half_dim:], emb[:, :half_dim]]    ,  dim=-1					)

       # zero pad
       if embedding_dim % 2 == 1:
              __UpperCamelCase	=  torch.nn.functional.pad(__lowercase    ,  (0, 1, 0, 0)					)
       return emb
def     lowercase__       (       __lowercase						:  Optional[Any]    ,  __lowercase						:  List[Any]    ,  __lowercase						:  Tuple=False    ,  __lowercase						:  Optional[Any]=0					)			->				Tuple:



       """simple docstring"""






       __UpperCamelCase	=  np.arange(__lowercase    ,  dtype=np.floataa					)
       __UpperCamelCase	=  np.arange(__lowercase    ,  dtype=np.floataa					)
       __UpperCamelCase	=  np.meshgrid(__lowercase    ,  __lowercase					)  # here w goes first
       __UpperCamelCase	=  np.stack(__lowercase    ,  axis=0					)

       __UpperCamelCase	=  grid.reshape([2, 1, grid_size, grid_size]					)
       __UpperCamelCase	=  get_ad_sincos_pos_embed_from_grid(__lowercase    ,  __lowercase					)
       if cls_token and extra_tokens > 0:
              __UpperCamelCase	=  np.concatenate([np.zeros([extra_tokens, embed_dim]					), pos_embed]    ,  axis=0					)
       return pos_embed
def     lowercase__       (       __lowercase						:  int    ,  __lowercase						:  Any					)			->				Optional[int]:



       """simple docstring"""






       if embed_dim % 2 != 0:
              raise ValueError('embed_dim must be divisible by 2'					)

       # use half of dimensions to encode grid_h
       __UpperCamelCase	=  get_ad_sincos_pos_embed_from_grid(embed_dim // 2    ,  grid[0]					)  # (H*W, D/2)
       __UpperCamelCase	=  get_ad_sincos_pos_embed_from_grid(embed_dim // 2    ,  grid[1]					)  # (H*W, D/2)

       __UpperCamelCase	=  np.concatenate([emb_h, emb_w]    ,  axis=1					)  # (H*W, D)
       return emb
def     lowercase__       (       __lowercase						:  List[str]    ,  __lowercase						:  List[str]					)			->				List[str]:



       """simple docstring"""






       if embed_dim % 2 != 0:
              raise ValueError('embed_dim must be divisible by 2'					)

       __UpperCamelCase	=  np.arange(embed_dim // 2    ,  dtype=np.floataa					)
       omega /= embed_dim / 2.0
       __UpperCamelCase	=  1.0 / 10000**omega  # (D/2,)

       __UpperCamelCase	=  pos.reshape(-1					)  # (M,)
       __UpperCamelCase	=  np.einsum('m,d->md'    ,  __lowercase    ,  __lowercase					)  # (M, D/2), outer product

       __UpperCamelCase	=  np.sin(__lowercase					)  # (M, D/2)
       __UpperCamelCase	=  np.cos(__lowercase					)  # (M, D/2)

       __UpperCamelCase	=  np.concatenate([emb_sin, emb_cos]    ,  axis=1					)  # (M, D)
       return emb

class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							Union[str, Any]							,     __A	:							Optional[Any]=2_2_4							,     __A	:							Union[str, Any]=2_2_4							,     __A	:							str=1_6							,     __A	:							Union[str, Any]=3							,     __A	:							int=7_6_8							,     __A	:							List[str]=False							,     __A	:							List[str]=True							,     __A	:							Optional[Any]=True							,     ):
        super().__init__()

        __UpperCamelCase	=  (height // patch_size) * (width // patch_size)
        __UpperCamelCase	=  flatten
        __UpperCamelCase	=  layer_norm

        __UpperCamelCase	=  nn.Convad(
            __A							,     __A							,     kernel_size=(patch_size, patch_size)							,     stride=__A							,     bias=__A     )
        if layer_norm:
               __UpperCamelCase	=  nn.LayerNorm(__A							,     elementwise_affine=__A							,     eps=1e-6     )
        else:
               __UpperCamelCase	=  None

        __UpperCamelCase	=  get_ad_sincos_pos_embed(__A							,     int(num_patches**0.5     )     )
        self.register_buffer('pos_embed'							,     torch.from_numpy(__A     ).float().unsqueeze(0     )							,     persistent=__A     )






 def 	_lowerCamelCase (	self	:							Optional[int]							,     __A	:							Tuple     ):
        __UpperCamelCase	=  self.proj(__A     )
        if self.flatten:
               __UpperCamelCase	=  latent.flatten(2     ).transpose(1							,     2     )  # BCHW -> BNC
        if self.layer_norm:
               __UpperCamelCase	=  self.norm(__A     )
        return latent + self.pos_embed






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							int							,     __A	:							int							,     __A	:							int							,     __A	:							str = "silu"							,     __A	:							int = None							,     __A	:							Optional[str] = None							,     __A	:							List[Any]=None							,     ):
        super().__init__()

        __UpperCamelCase	=  nn.Linear(__A							,     __A     )

        if cond_proj_dim is not None:
               __UpperCamelCase	=  nn.Linear(__A							,     __A							,     bias=__A     )
        else:
               __UpperCamelCase	=  None

        __UpperCamelCase	=  get_activation(__A     )

        if out_dim is not None:
               __UpperCamelCase	=  out_dim
        else:
               __UpperCamelCase	=  time_embed_dim
        __UpperCamelCase	=  nn.Linear(__A							,     __A     )

        if post_act_fn is None:
               __UpperCamelCase	=  None
        else:
               __UpperCamelCase	=  get_activation(__A     )






 def 	_lowerCamelCase (	self	:							str							,     __A	:							Dict							,     __A	:							Optional[Any]=None     ):
        if condition is not None:
               __UpperCamelCase	=  sample + self.cond_proj(__A     )
        __UpperCamelCase	=  self.linear_a(__A     )

        if self.act is not None:
               __UpperCamelCase	=  self.act(__A     )

        __UpperCamelCase	=  self.linear_a(__A     )

        if self.post_act is not None:
               __UpperCamelCase	=  self.post_act(__A     )
        return sample






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							str							,     __A	:							int							,     __A	:							bool							,     __A	:							float     ):
        super().__init__()
        __UpperCamelCase	=  num_channels
        __UpperCamelCase	=  flip_sin_to_cos
        __UpperCamelCase	=  downscale_freq_shift






 def 	_lowerCamelCase (	self	:							int							,     __A	:							Dict     ):
        __UpperCamelCase	=  get_timestep_embedding(
            __A							,     self.num_channels							,     flip_sin_to_cos=self.flip_sin_to_cos							,     downscale_freq_shift=self.downscale_freq_shift							,     )
        return t_emb






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							str							,     __A	:							int = 2_5_6							,     __A	:							float = 1.0							,     __A	:							str=True							,     __A	:							List[Any]=True							,     __A	:							Optional[int]=False     ):
        super().__init__()
        __UpperCamelCase	=  nn.Parameter(torch.randn(__A     ) * scale							,     requires_grad=__A     )
        __UpperCamelCase	=  log
        __UpperCamelCase	=  flip_sin_to_cos

        if set_W_to_weight:
               # to delete later
               __UpperCamelCase	=  nn.Parameter(torch.randn(__A     ) * scale							,     requires_grad=__A     )

               __UpperCamelCase	=  self.W






 def 	_lowerCamelCase (	self	:							Tuple							,     __A	:							Union[str, Any]     ):
        if self.log:
               __UpperCamelCase	=  torch.log(__A     )

        __UpperCamelCase	=  x[:, None] * self.weight[None, :] * 2 * np.pi

        if self.flip_sin_to_cos:
               __UpperCamelCase	=  torch.cat([torch.cos(__A     ), torch.sin(__A     )]							,     dim=-1     )
        else:
               __UpperCamelCase	=  torch.cat([torch.sin(__A     ), torch.cos(__A     )]							,     dim=-1     )
        return out






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							Optional[Any]							,     __A	:							int							,     __A	:							int							,     __A	:							int							,     __A	:							int							,     ):
        super().__init__()

        __UpperCamelCase	=  height
        __UpperCamelCase	=  width
        __UpperCamelCase	=  num_embed
        __UpperCamelCase	=  embed_dim

        __UpperCamelCase	=  nn.Embedding(self.num_embed							,     __A     )
        __UpperCamelCase	=  nn.Embedding(self.height							,     __A     )
        __UpperCamelCase	=  nn.Embedding(self.width							,     __A     )






 def 	_lowerCamelCase (	self	:							Any							,     __A	:							Any     ):
        __UpperCamelCase	=  self.emb(__A     )

        __UpperCamelCase	=  self.height_emb(torch.arange(self.height							,     device=index.device     ).view(1							,     self.height     )     )

        # 1 x H x D -> 1 x H x 1 x D
        __UpperCamelCase	=  height_emb.unsqueeze(2     )

        __UpperCamelCase	=  self.width_emb(torch.arange(self.width							,     device=index.device     ).view(1							,     self.width     )     )

        # 1 x W x D -> 1 x 1 x W x D
        __UpperCamelCase	=  width_emb.unsqueeze(1     )

        __UpperCamelCase	=  height_emb + width_emb

        # 1 x H x W x D -> 1 x L xD
        __UpperCamelCase	=  pos_emb.view(1							,     self.height * self.width							,     -1     )

        __UpperCamelCase	=  emb + pos_emb[:, : emb.shape[1], :]

        return emb






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							str							,     __A	:							Optional[Any]							,     __A	:							Optional[Any]							,     __A	:							Optional[int]     ):
        super().__init__()
        __UpperCamelCase	=  dropout_prob > 0
        __UpperCamelCase	=  nn.Embedding(num_classes + use_cfg_embedding							,     __A     )
        __UpperCamelCase	=  num_classes
        __UpperCamelCase	=  dropout_prob






 def 	_lowerCamelCase (	self	:							int							,     __A	:							List[Any]							,     __A	:							Dict=None     ):
        if force_drop_ids is None:
               __UpperCamelCase	=  torch.rand(labels.shape[0]							,     device=labels.device     ) < self.dropout_prob
        else:
               __UpperCamelCase	=  torch.tensor(force_drop_ids == 1     )
        __UpperCamelCase	=  torch.where(__A							,     self.num_classes							,     __A     )
        return labels






 def 	_lowerCamelCase (	self	:							Optional[int]							,     __A	:							torch.LongTensor							,     __A	:							Union[str, Any]=None     ):
        __UpperCamelCase	=  self.dropout_prob > 0
        if (self.training and use_dropout) or (force_drop_ids is not None):
               __UpperCamelCase	=  self.token_drop(__A							,     __A     )
        __UpperCamelCase	=  self.embedding_table(__A     )
        return embeddings






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							Dict							,     __A	:							int = 1_0_2_4							,     __A	:							int = 7_6_8							,     __A	:							int = 7_6_8							,     __A	:							int = 1_0							,     ):
        super().__init__()

        __UpperCamelCase	=  num_image_text_embeds
        __UpperCamelCase	=  nn.Linear(__A							,     self.num_image_text_embeds * cross_attention_dim     )
        __UpperCamelCase	=  nn.Linear(__A							,     __A     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							torch.FloatTensor							,     __A	:							torch.FloatTensor     ):
        __UpperCamelCase	=  text_embeds.shape[0]

        # image
        __UpperCamelCase	=  self.image_embeds(__A     )
        __UpperCamelCase	=  image_text_embeds.reshape(__A							,     self.num_image_text_embeds							,     -1     )

        # text
        __UpperCamelCase	=  self.text_proj(__A     )

        return torch.cat([image_text_embeds, text_embeds]							,     dim=1     )






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							Any							,     __A	:							int = 7_6_8							,     __A	:							int = 7_6_8							,     __A	:							int = 3_2							,     ):
        super().__init__()

        __UpperCamelCase	=  num_image_text_embeds
        __UpperCamelCase	=  nn.Linear(__A							,     self.num_image_text_embeds * cross_attention_dim     )
        __UpperCamelCase	=  nn.LayerNorm(__A     )






 def 	_lowerCamelCase (	self	:							List[Any]							,     __A	:							torch.FloatTensor     ):
        __UpperCamelCase	=  image_embeds.shape[0]

        # image
        __UpperCamelCase	=  self.image_embeds(__A     )
        __UpperCamelCase	=  image_embeds.reshape(__A							,     self.num_image_text_embeds							,     -1     )
        __UpperCamelCase	=  self.norm(__A     )
        return image_embeds






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							int							,     __A	:							List[Any]							,     __A	:							Dict							,     __A	:							Union[str, Any]=0.1     ):
        super().__init__()

        __UpperCamelCase	=  Timesteps(num_channels=2_5_6							,     flip_sin_to_cos=__A							,     downscale_freq_shift=1     )
        __UpperCamelCase	=  TimestepEmbedding(in_channels=2_5_6							,     time_embed_dim=__A     )
        __UpperCamelCase	=  LabelEmbedding(__A							,     __A							,     __A     )






 def 	_lowerCamelCase (	self	:							Any							,     __A	:							Tuple							,     __A	:							Tuple							,     __A	:							str=None     ):
        __UpperCamelCase	=  self.time_proj(__A     )
        __UpperCamelCase	=  self.timestep_embedder(timesteps_proj.to(dtype=__A     )     )  # (N, D)

        __UpperCamelCase	=  self.class_embedder(__A     )  # (N, D)

        __UpperCamelCase	=  timesteps_emb + class_labels  # (N, D)

        return conditioning






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							Optional[Any]							,     __A	:							int							,     __A	:							int							,     __A	:							int = 6_4     ):
        super().__init__()
        __UpperCamelCase	=  nn.LayerNorm(__A     )
        __UpperCamelCase	=  AttentionPooling(__A							,     __A     )
        __UpperCamelCase	=  nn.Linear(__A							,     __A     )
        __UpperCamelCase	=  nn.LayerNorm(__A     )






 def 	_lowerCamelCase (	self	:							List[Any]							,     __A	:							Dict     ):
        __UpperCamelCase	=  self.norma(__A     )
        __UpperCamelCase	=  self.pool(__A     )
        __UpperCamelCase	=  self.proj(__A     )
        __UpperCamelCase	=  self.norma(__A     )
        return hidden_states






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							str							,     __A	:							int = 7_6_8							,     __A	:							int = 7_6_8							,     __A	:							int = 1_5_3_6     ):
        super().__init__()
        __UpperCamelCase	=  nn.Linear(__A							,     __A     )
        __UpperCamelCase	=  nn.LayerNorm(__A     )
        __UpperCamelCase	=  nn.Linear(__A							,     __A     )






 def 	_lowerCamelCase (	self	:							int							,     __A	:							torch.FloatTensor							,     __A	:							torch.FloatTensor     ):
        # text
        __UpperCamelCase	=  self.text_proj(__A     )
        __UpperCamelCase	=  self.text_norm(__A     )

        # image
        __UpperCamelCase	=  self.image_proj(__A     )

        return time_image_embeds + time_text_embeds






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							List[Any]							,     __A	:							int = 7_6_8							,     __A	:							int = 1_5_3_6     ):
        super().__init__()
        __UpperCamelCase	=  nn.Linear(__A							,     __A     )
        __UpperCamelCase	=  nn.LayerNorm(__A     )






 def 	_lowerCamelCase (	self	:							int							,     __A	:							torch.FloatTensor     ):
        # image
        __UpperCamelCase	=  self.image_proj(__A     )
        __UpperCamelCase	=  self.image_norm(__A     )
        return time_image_embeds






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							List[str]							,     __A	:							int = 7_6_8							,     __A	:							int = 1_5_3_6     ):
        super().__init__()
        __UpperCamelCase	=  nn.Linear(__A							,     __A     )
        __UpperCamelCase	=  nn.LayerNorm(__A     )
        __UpperCamelCase	=  nn.Sequential(
            nn.Convad(3							,     1_6							,     3							,     padding=1     )							,     nn.SiLU()							,     nn.Convad(1_6							,     1_6							,     3							,     padding=1     )							,     nn.SiLU()							,     nn.Convad(1_6							,     3_2							,     3							,     padding=1							,     stride=2     )							,     nn.SiLU()							,     nn.Convad(3_2							,     3_2							,     3							,     padding=1     )							,     nn.SiLU()							,     nn.Convad(3_2							,     9_6							,     3							,     padding=1							,     stride=2     )							,     nn.SiLU()							,     nn.Convad(9_6							,     9_6							,     3							,     padding=1     )							,     nn.SiLU()							,     nn.Convad(9_6							,     2_5_6							,     3							,     padding=1							,     stride=2     )							,     nn.SiLU()							,     nn.Convad(2_5_6							,     4							,     3							,     padding=1     )							,     )






 def 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							torch.FloatTensor							,     __A	:							torch.FloatTensor     ):
        # image
        __UpperCamelCase	=  self.image_proj(__A     )
        __UpperCamelCase	=  self.image_norm(__A     )
        __UpperCamelCase	=  self.input_hint_block(__A     )
        return time_image_embeds, hint






class 					snake_case					(   nn.Module	):




 """simple docstring"""



 def __init__(	self	:							List[Any]							,     __A	:							Union[str, Any]							,     __A	:							Dict							,     __A	:							Union[str, Any]=None     ):
        super().__init__()
        __UpperCamelCase	=  dtype
        __UpperCamelCase	=  nn.Parameter(torch.randn(1							,     __A     ) / embed_dim**0.5     )
        __UpperCamelCase	=  nn.Linear(__A							,     __A							,     dtype=self.dtype     )
        __UpperCamelCase	=  nn.Linear(__A							,     __A							,     dtype=self.dtype     )
        __UpperCamelCase	=  nn.Linear(__A							,     __A							,     dtype=self.dtype     )
        __UpperCamelCase	=  num_heads
        __UpperCamelCase	=  embed_dim // self.num_heads






 def 	_lowerCamelCase (	self	:							List[Any]							,     __A	:							int     ):
        __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  x.size()

        def shape(__A	:							Optional[Any]     ):
               # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
               __UpperCamelCase	=  x.view(__A							,     -1							,     self.num_heads							,     self.dim_per_head     )
               # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
               __UpperCamelCase	=  x.transpose(1							,     2     )
               # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
               __UpperCamelCase	=  x.reshape(bs * self.num_heads							,     -1							,     self.dim_per_head     )
               # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
               __UpperCamelCase	=  x.transpose(1							,     2     )
               return x

        __UpperCamelCase	=  x.mean(dim=1							,     keepdim=__A     ) + self.positional_embedding.to(x.dtype     )
        __UpperCamelCase	=  torch.cat([class_token, x]							,     dim=1     )  # (bs, length+1, width)

        # (bs*n_heads, class_token_length, dim_per_head)
        __UpperCamelCase	=  shape(self.q_proj(__A     )     )
        # (bs*n_heads, length+class_token_length, dim_per_head)
        __UpperCamelCase	=  shape(self.k_proj(__A     )     )
        __UpperCamelCase	=  shape(self.v_proj(__A     )     )

        # (bs*n_heads, class_token_length, length+class_token_length):
        __UpperCamelCase	=  1 / math.sqrt(math.sqrt(self.dim_per_head     )     )
        __UpperCamelCase	=  torch.einsum('bct,bcs->bts'							,     q * scale							,     k * scale     )  # More stable with f16 than dividing afterwards
        __UpperCamelCase	=  torch.softmax(weight.float()							,     dim=-1     ).type(weight.dtype     )

        # (bs*n_heads, dim_per_head, class_token_length)
        __UpperCamelCase	=  torch.einsum('bts,bcs->bct'							,     __A							,     __A     )

        # (bs, length+1, width)
        __UpperCamelCase	=  a.reshape(__A							,     -1							,     1     ).transpose(1							,     2     )

        return a[:, 0, :]  # cls_token







'''simple docstring'''
def     lowercase__       (       __lowercase						:  str    ,  __lowercase						:  Tuple					)			->				str:



       """simple docstring"""






       sequence.append(__lowercase					)

       __UpperCamelCase	=  0
       while sequence[index] != target:
              index += 1

       sequence.pop()

       if index == len(__lowercase					):
              return None

       return index


if __name__ == "__main__":
    a__							:							Any			=input('''Enter numbers separated by comma:\n''').strip()
    a__							:							Any			=[int(item) for item in user_input.split(''',''')]

    a__							:							Tuple			=input('''Enter a single number to be found in the list:\n''')
    a__							:							List[Any]			=int(target_input)
    a__							:							List[Any]			=sentinel_linear_search(sequence, target)
    if result is not None:
        print(f'{target} found at positions: {result}')
    else:
        print('''Not found''')
