


"""simple docstring"""






import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation
def    A_ ( _lowerCAmelCase  :       torch.Tensor,  _lowerCAmelCase  :       int,  _lowerCAmelCase  :       bool = False,  _lowerCAmelCase  :       float = 1,  _lowerCAmelCase  :       float = 1,  _lowerCAmelCase  :       int = 1_00_00,  ):






     """simple docstring"""

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

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

     _a     						=					torch.exp(_lowercase							)
     _a     						=					timesteps[:, None].float() * emb[None, :]

     # scale embeddings
     _a     						=					scale * emb

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

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

     # zero pad
     if embedding_dim % 2 == 1:
          _a     						=					torch.nn.functional.pad(_lowercase,  (0, 1, 0, 0)							)
     return emb
def    A_ ( _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[int],  _lowerCAmelCase  :       Union[str, Any]=False,  _lowerCAmelCase  :       Dict=0							):






     """simple docstring"""

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

     _a     						=					grid.reshape([2, 1, grid_size, grid_size]							)
     _a     						=					get_ad_sincos_pos_embed_from_grid(_lowercase,  _lowercase							)
     if cls_token and extra_tokens > 0:
          _a     						=					np.concatenate([np.zeros([extra_tokens, embed_dim]							), pos_embed],  axis=0							)
     return pos_embed
def    A_ ( _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Any							):






     """simple docstring"""

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

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

     _a     						=					np.concatenate([emb_h, emb_w],  axis=1							)  # (H*W, D)
     return emb
def    A_ ( _lowerCAmelCase  :       Any,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

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

     _a     						=					np.arange(embed_dim // 2,  dtype=np.floataa							)
     omega /= embed_dim / 2.0
     _a     						=					1.0 / 1_00_00**omega  # (D/2,)

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

     _a     						=					np.sin(_lowercase							)  # (M, D/2)
     _a     						=					np.cos(_lowercase							)  # (M, D/2)

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


class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase=224     ,					__UpperCAmelCase=224     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					)							->				List[str]:
          super().__init__()

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

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

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





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Dict:
          _a     						=					self.proj(__lowerCAmelCase				)
          if self.flatten:
               _a     						=					latent.flatten(2				).transpose(1     ,					2				)  # BCHW -> BNC
          if self.layer_norm:
               _a     						=					self.norm(__lowerCAmelCase				)
          return latent + self.pos_embed







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = "silu"     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase=None     ,					)							->				Optional[Any]:
          super().__init__()

          _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase				)

          if cond_proj_dim is not None:
               _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase     ,					bias=__lowerCAmelCase				)
          else:
               _a     						=					None

          _a     						=					get_activation(__lowerCAmelCase				)

          if out_dim is not None:
               _a     						=					out_dim
          else:
               _a     						=					time_embed_dim
          _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase				)

          if post_act_fn is None:
               _a     						=					None
          else:
               _a     						=					get_activation(__lowerCAmelCase				)





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None				)							->				str:
          if condition is not None:
               _a     						=					sample + self.cond_proj(__lowerCAmelCase				)
          _a     						=					self.linear_a(__lowerCAmelCase				)

          if self.act is not None:
               _a     						=					self.act(__lowerCAmelCase				)

          _a     						=					self.linear_a(__lowerCAmelCase				)

          if self.post_act is not None:
               _a     						=					self.post_act(__lowerCAmelCase				)
          return sample







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Union[str, Any]:
          super().__init__()
          _a     						=					num_channels
          _a     						=					flip_sin_to_cos
          _a     						=					downscale_freq_shift





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Any:
          _a     						=					get_timestep_embedding(
              __lowerCAmelCase     ,					self.num_channels     ,					flip_sin_to_cos=self.flip_sin_to_cos     ,					downscale_freq_shift=self.downscale_freq_shift     ,					)
          return t_emb







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase = 256     ,					__UpperCAmelCase = 1.0     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=False				)							->				Any:
          super().__init__()
          _a     						=					nn.Parameter(torch.randn(__lowerCAmelCase				) * scale     ,					requires_grad=__lowerCAmelCase				)
          _a     						=					log
          _a     						=					flip_sin_to_cos

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

               _a     						=					self.W





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Any:
          if self.log:
               _a     						=					torch.log(__lowerCAmelCase				)

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

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







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					)							->				Optional[Any]:
          super().__init__()

          _a     						=					height
          _a     						=					width
          _a     						=					num_embed
          _a     						=					embed_dim

          _a     						=					nn.Embedding(self.num_embed     ,					__lowerCAmelCase				)
          _a     						=					nn.Embedding(self.height     ,					__lowerCAmelCase				)
          _a     						=					nn.Embedding(self.width     ,					__lowerCAmelCase				)





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				str:
          _a     						=					self.emb(__lowerCAmelCase				)

          _a     						=					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
          _a     						=					height_emb.unsqueeze(2				)

          _a     						=					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
          _a     						=					width_emb.unsqueeze(1				)

          _a     						=					height_emb + width_emb

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

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

          return emb







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				List[Any]:
          super().__init__()
          _a     						=					dropout_prob > 0
          _a     						=					nn.Embedding(num_classes + use_cfg_embedding     ,					__lowerCAmelCase				)
          _a     						=					num_classes
          _a     						=					dropout_prob


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None				)							->				Any:
          if force_drop_ids is None:
               _a     						=					torch.rand(labels.shape[0]     ,					device=labels.device				) < self.dropout_prob
          else:
               _a     						=					torch.tensor(force_drop_ids == 1				)
          _a     						=					torch.where(__lowerCAmelCase     ,					self.num_classes     ,					__lowerCAmelCase				)
          return labels





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None				)							->				Union[str, Any]:
          _a     						=					self.dropout_prob > 0
          if (self.training and use_dropout) or (force_drop_ids is not None):
               _a     						=					self.token_drop(__lowerCAmelCase     ,					__lowerCAmelCase				)
          _a     						=					self.embedding_table(__lowerCAmelCase				)
          return embeddings







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase = 1024     ,					__UpperCAmelCase = 768     ,					__UpperCAmelCase = 768     ,					__UpperCAmelCase = 10     ,					)							->				Union[str, Any]:
          super().__init__()

          _a     						=					num_image_text_embeds
          _a     						=					nn.Linear(__lowerCAmelCase     ,					self.num_image_text_embeds * cross_attention_dim				)
          _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase				)





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Any:
          _a     						=					text_embeds.shape[0]

          # image
          _a     						=					self.image_embeds(__lowerCAmelCase				)
          _a     						=					image_text_embeds.reshape(__lowerCAmelCase     ,					self.num_image_text_embeds     ,					-1				)

          # text
          _a     						=					self.text_proj(__lowerCAmelCase				)

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







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase = 768     ,					__UpperCAmelCase = 768     ,					__UpperCAmelCase = 32     ,					)							->				Union[str, Any]:
          super().__init__()

          _a     						=					num_image_text_embeds
          _a     						=					nn.Linear(__lowerCAmelCase     ,					self.num_image_text_embeds * cross_attention_dim				)
          _a     						=					nn.LayerNorm(__lowerCAmelCase				)





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Any:
          _a     						=					image_embeds.shape[0]

          # image
          _a     						=					self.image_embeds(__lowerCAmelCase				)
          _a     						=					image_embeds.reshape(__lowerCAmelCase     ,					self.num_image_text_embeds     ,					-1				)
          _a     						=					self.norm(__lowerCAmelCase				)
          return image_embeds







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=0.1				)							->				str:
          super().__init__()

          _a     						=					Timesteps(num_channels=256     ,					flip_sin_to_cos=__lowerCAmelCase     ,					downscale_freq_shift=1				)
          _a     						=					TimestepEmbedding(in_channels=256     ,					time_embed_dim=__lowerCAmelCase				)
          _a     						=					LabelEmbedding(__lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase				)





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None				)							->				List[Any]:
          _a     						=					self.time_proj(__lowerCAmelCase				)
          _a     						=					self.timestep_embedder(timesteps_proj.to(dtype=__lowerCAmelCase				)				)  # (N, D)

          _a     						=					self.class_embedder(__lowerCAmelCase				)  # (N, D)

          _a     						=					timesteps_emb + class_labels  # (N, D)

          return conditioning







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = 64				)							->				Dict:
          super().__init__()
          _a     						=					nn.LayerNorm(__lowerCAmelCase				)
          _a     						=					AttentionPooling(__lowerCAmelCase     ,					__lowerCAmelCase				)
          _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase				)
          _a     						=					nn.LayerNorm(__lowerCAmelCase				)





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				List[str]:
          _a     						=					self.norma(__lowerCAmelCase				)
          _a     						=					self.pool(__lowerCAmelCase				)
          _a     						=					self.proj(__lowerCAmelCase				)
          _a     						=					self.norma(__lowerCAmelCase				)
          return hidden_states







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase = 768     ,					__UpperCAmelCase = 768     ,					__UpperCAmelCase = 1536				)							->				Tuple:
          super().__init__()
          _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase				)
          _a     						=					nn.LayerNorm(__lowerCAmelCase				)
          _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase				)





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Union[str, Any]:
          # text
          _a     						=					self.text_proj(__lowerCAmelCase				)
          _a     						=					self.text_norm(__lowerCAmelCase				)

          # image
          _a     						=					self.image_proj(__lowerCAmelCase				)

          return time_image_embeds + time_text_embeds







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase = 768     ,					__UpperCAmelCase = 1536				)							->				Dict:
          super().__init__()
          _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase				)
          _a     						=					nn.LayerNorm(__lowerCAmelCase				)





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Dict:
          # image
          _a     						=					self.image_proj(__lowerCAmelCase				)
          _a     						=					self.image_norm(__lowerCAmelCase				)
          return time_image_embeds







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase = 768     ,					__UpperCAmelCase = 1536				)							->				Union[str, Any]:
          super().__init__()
          _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase				)
          _a     						=					nn.LayerNorm(__lowerCAmelCase				)
          _a     						=					nn.Sequential(
              nn.Convad(3     ,					16     ,					3     ,					padding=1				)     ,					nn.SiLU()     ,					nn.Convad(16     ,					16     ,					3     ,					padding=1				)     ,					nn.SiLU()     ,					nn.Convad(16     ,					32     ,					3     ,					padding=1     ,					stride=2				)     ,					nn.SiLU()     ,					nn.Convad(32     ,					32     ,					3     ,					padding=1				)     ,					nn.SiLU()     ,					nn.Convad(32     ,					96     ,					3     ,					padding=1     ,					stride=2				)     ,					nn.SiLU()     ,					nn.Convad(96     ,					96     ,					3     ,					padding=1				)     ,					nn.SiLU()     ,					nn.Convad(96     ,					256     ,					3     ,					padding=1     ,					stride=2				)     ,					nn.SiLU()     ,					nn.Convad(256     ,					4     ,					3     ,					padding=1				)     ,					)





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Optional[Any]:
          # image
          _a     						=					self.image_proj(__lowerCAmelCase				)
          _a     						=					self.image_norm(__lowerCAmelCase				)
          _a     						=					self.input_hint_block(__lowerCAmelCase				)
          return time_image_embeds, hint







class      __lowerCamelCase	(       nn.Module					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None				)							->				List[str]:
          super().__init__()
          _a     						=					dtype
          _a     						=					nn.Parameter(torch.randn(1     ,					__lowerCAmelCase				) / embed_dim**0.5				)
          _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase     ,					dtype=self.dtype				)
          _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase     ,					dtype=self.dtype				)
          _a     						=					nn.Linear(__lowerCAmelCase     ,					__lowerCAmelCase     ,					dtype=self.dtype				)
          _a     						=					num_heads
          _a     						=					embed_dim // self.num_heads





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Dict:
          _a						,   _a						,   _a     						=					x.size()

          def shape(__UpperCAmelCase				):
               # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
               _a     						=					x.view(__lowerCAmelCase     ,					-1     ,					self.num_heads     ,					self.dim_per_head				)
               # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
               _a     						=					x.transpose(1     ,					2				)
               # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
               _a     						=					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)
               _a     						=					x.transpose(1     ,					2				)
               return x

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

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

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

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

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

          return a[:, 0, :]  # cls_token






from __future__ import annotations

def      a    (   _lowercase :  float | int			,    _lowercase :  float | int			,    _lowercase :  float | int			,    )  ->list[float | int]:
		if not all((nth_term, start_term_a, common_ratio_r)							):
				return []
		UpperCamelCase__								=						[]
		UpperCamelCase__								=						1
		UpperCamelCase__								=						common_ratio_r
		for _ in range(int(_lowercase							)							):
				if not series:
						series.append(_lowercase							)
				else:
						power += 1
						series.append(float(start_term_a * multiple							)							)
						UpperCamelCase__								=						pow(float(_lowercase							)			,    _lowercase							)
		return series


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__lowerCAmelCase 	=      float(input("Enter the last number (n term) of the Geometric Series"))
				__lowerCAmelCase 	=      float(input("Enter the starting term (a) of the Geometric Series"))
				__lowerCAmelCase 	=      float(
				    input("Enter the common ratio between two terms (r) of the Geometric Series")
				)
				print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
				print(geometric_series(nth_term, start_term_a, common_ratio_r))





