'''simple docstring'''




import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__ = False       ,						UpperCamelCase__ = 1       ,						UpperCamelCase__ = 1       ,						UpperCamelCase__ = 1_00_00       ,						)			->					Dict:
    assert len(timesteps.shape       ) == 1, "Timesteps should be a 1d-array"

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

    __lowerCamelCase						=						torch.exp(UpperCamelCase__       )
    __lowerCamelCase						=						timesteps[:, None].float() * emb[None, :]

    # scale embeddings
    __lowerCamelCase						=						scale * emb

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

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

    # zero pad
    if embedding_dim % 2 == 1:
        __lowerCamelCase						=						torch.nn.functional.pad(UpperCamelCase__       ,						(0, 1, 0, 0)       )
    return emb






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__=False       ,						UpperCamelCase__=0       )			->					Union[str, Any]:
    __lowerCamelCase						=						np.arange(UpperCamelCase__       ,						dtype=np.floataa       )
    __lowerCamelCase						=						np.arange(UpperCamelCase__       ,						dtype=np.floataa       )
    __lowerCamelCase						=						np.meshgrid(UpperCamelCase__       ,						UpperCamelCase__       )  # here w goes first
    __lowerCamelCase						=						np.stack(UpperCamelCase__       ,						axis=0       )

    __lowerCamelCase						=						grid.reshape([2, 1, grid_size, grid_size]       )
    __lowerCamelCase						=						get_ad_sincos_pos_embed_from_grid(UpperCamelCase__       ,						UpperCamelCase__       )
    if cls_token and extra_tokens > 0:
        __lowerCamelCase						=						np.concatenate([np.zeros([extra_tokens, embed_dim]       ), pos_embed]       ,						axis=0       )
    return pos_embed






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       )			->					Optional[int]:


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

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

    __lowerCamelCase						=						np.concatenate([emb_h, emb_w]       ,						axis=1       )  # (H*W, D)
    return emb






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       )			->					List[Any]:


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

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

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

    __lowerCamelCase						=						np.sin(UpperCamelCase__       )  # (M, D/2)
    __lowerCamelCase						=						np.cos(UpperCamelCase__       )  # (M, D/2)

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




class 					a__   (     nn.Module				):






     def __init__(				self	:	str						,       a	:	int=2_24						,       a	:	Dict=2_24						,       a	:	List[Any]=16						,       a	:	str=3						,       a	:	List[str]=7_68						,       a	:	Tuple=False						,       a	:	Optional[int]=True						,       a	:	str=True						,       ):





         """simple docstring"""



         super().__init__()

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

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

         __lowerCamelCase						=						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      SCREAMING_SNAKE_CASE__     (				self	:	Optional[int]						,       a	:	str					):





         """simple docstring"""



         __lowerCamelCase						=						self.proj(a					)
         if self.flatten:
             __lowerCamelCase						=						latent.flatten(2					).transpose(1						,       2					)  # BCHW -> BNC
         if self.layer_norm:
             __lowerCamelCase						=						self.norm(a					)
         return latent + self.pos_embed




class 					a__   (     nn.Module				):






     def __init__(				self	:	Union[str, Any]						,       a	:	int						,       a	:	int						,       a	:	str = "silu"						,       a	:	int = None						,       a	:	Optional[str] = None						,       a	:	int=None						,       ):





         """simple docstring"""



         super().__init__()

         __lowerCamelCase						=						nn.Linear(a						,       a					)

         if cond_proj_dim is not None:
             __lowerCamelCase						=						nn.Linear(a						,       a						,       bias=a					)
         else:
             __lowerCamelCase						=						None

         __lowerCamelCase						=						get_activation(a					)

         if out_dim is not None:
             __lowerCamelCase						=						out_dim
         else:
             __lowerCamelCase						=						time_embed_dim
         __lowerCamelCase						=						nn.Linear(a						,       a					)

         if post_act_fn is None:
             __lowerCamelCase						=						None
         else:
             __lowerCamelCase						=						get_activation(a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[int]						,       a	:	Tuple						,       a	:	Optional[Any]=None					):





         """simple docstring"""



         if condition is not None:
             __lowerCamelCase						=						sample + self.cond_proj(a					)
         __lowerCamelCase						=						self.linear_a(a					)

         if self.act is not None:
             __lowerCamelCase						=						self.act(a					)

         __lowerCamelCase						=						self.linear_a(a					)

         if self.post_act is not None:
             __lowerCamelCase						=						self.post_act(a					)
         return sample




class 					a__   (     nn.Module				):






     def __init__(				self	:	int						,       a	:	int						,       a	:	bool						,       a	:	float					):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase						=						num_channels
         __lowerCamelCase						=						flip_sin_to_cos
         __lowerCamelCase						=						downscale_freq_shift






     def      SCREAMING_SNAKE_CASE__     (				self	:	str						,       a	:	str					):





         """simple docstring"""



         __lowerCamelCase						=						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 					a__   (     nn.Module				):






     def __init__(				self	:	List[str]						,       a	:	int = 2_56						,       a	:	float = 1.0						,       a	:	int=True						,       a	:	Any=True						,       a	:	List[Any]=False					):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase						=						nn.Parameter(torch.randn(a					) * scale						,       requires_grad=a					)
         __lowerCamelCase						=						log
         __lowerCamelCase						=						flip_sin_to_cos

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

             __lowerCamelCase						=						self.W






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]						,       a	:	Optional[Any]					):





         """simple docstring"""



         if self.log:
             __lowerCamelCase						=						torch.log(a					)

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

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




class 					a__   (     nn.Module				):






     def __init__(				self	:	Optional[int]						,       a	:	int						,       a	:	int						,       a	:	int						,       a	:	int						,       ):





         """simple docstring"""



         super().__init__()

         __lowerCamelCase						=						height
         __lowerCamelCase						=						width
         __lowerCamelCase						=						num_embed
         __lowerCamelCase						=						embed_dim

         __lowerCamelCase						=						nn.Embedding(self.num_embed						,       a					)
         __lowerCamelCase						=						nn.Embedding(self.height						,       a					)
         __lowerCamelCase						=						nn.Embedding(self.width						,       a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	int						,       a	:	Tuple					):





         """simple docstring"""



         __lowerCamelCase						=						self.emb(a					)

         __lowerCamelCase						=						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
         __lowerCamelCase						=						height_emb.unsqueeze(2					)

         __lowerCamelCase						=						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
         __lowerCamelCase						=						width_emb.unsqueeze(1					)

         __lowerCamelCase						=						height_emb + width_emb

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

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

         return emb




class 					a__   (     nn.Module				):






     def __init__(				self	:	Optional[Any]						,       a	:	Tuple						,       a	:	int						,       a	:	Union[str, Any]					):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase						=						dropout_prob > 0
         __lowerCamelCase						=						nn.Embedding(num_classes + use_cfg_embedding						,       a					)
         __lowerCamelCase						=						num_classes
         __lowerCamelCase						=						dropout_prob






     def      SCREAMING_SNAKE_CASE__     (				self	:	int						,       a	:	Tuple						,       a	:	List[str]=None					):





         """simple docstring"""



         if force_drop_ids is None:
             __lowerCamelCase						=						torch.rand(labels.shape[0]						,       device=labels.device					) < self.dropout_prob
         else:
             __lowerCamelCase						=						torch.tensor(force_drop_ids == 1					)
         __lowerCamelCase						=						torch.where(a						,       self.num_classes						,       a					)
         return labels






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]						,       a	:	torch.LongTensor						,       a	:	Tuple=None					):





         """simple docstring"""



         __lowerCamelCase						=						self.dropout_prob > 0
         if (self.training and use_dropout) or (force_drop_ids is not None):
             __lowerCamelCase						=						self.token_drop(a						,       a					)
         __lowerCamelCase						=						self.embedding_table(a					)
         return embeddings




class 					a__   (     nn.Module				):






     def __init__(				self	:	Any						,       a	:	int = 10_24						,       a	:	int = 7_68						,       a	:	int = 7_68						,       a	:	int = 10						,       ):





         """simple docstring"""



         super().__init__()

         __lowerCamelCase						=						num_image_text_embeds
         __lowerCamelCase						=						nn.Linear(a						,       self.num_image_text_embeds * cross_attention_dim					)
         __lowerCamelCase						=						nn.Linear(a						,       a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Tuple						,       a	:	torch.FloatTensor						,       a	:	torch.FloatTensor					):





         """simple docstring"""



         __lowerCamelCase						=						text_embeds.shape[0]

         # image
         __lowerCamelCase						=						self.image_embeds(a					)
         __lowerCamelCase						=						image_text_embeds.reshape(a						,       self.num_image_text_embeds						,       -1					)

         # text
         __lowerCamelCase						=						self.text_proj(a					)

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




class 					a__   (     nn.Module				):






     def __init__(				self	:	Optional[int]						,       a	:	int = 7_68						,       a	:	int = 7_68						,       a	:	int = 32						,       ):





         """simple docstring"""



         super().__init__()

         __lowerCamelCase						=						num_image_text_embeds
         __lowerCamelCase						=						nn.Linear(a						,       self.num_image_text_embeds * cross_attention_dim					)
         __lowerCamelCase						=						nn.LayerNorm(a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[str]						,       a	:	torch.FloatTensor					):





         """simple docstring"""



         __lowerCamelCase						=						image_embeds.shape[0]

         # image
         __lowerCamelCase						=						self.image_embeds(a					)
         __lowerCamelCase						=						image_embeds.reshape(a						,       self.num_image_text_embeds						,       -1					)
         __lowerCamelCase						=						self.norm(a					)
         return image_embeds




class 					a__   (     nn.Module				):






     def __init__(				self	:	List[str]						,       a	:	int						,       a	:	Union[str, Any]						,       a	:	Optional[Any]=0.1					):





         """simple docstring"""



         super().__init__()

         __lowerCamelCase						=						Timesteps(num_channels=2_56						,       flip_sin_to_cos=a						,       downscale_freq_shift=1					)
         __lowerCamelCase						=						TimestepEmbedding(in_channels=2_56						,       time_embed_dim=a					)
         __lowerCamelCase						=						LabelEmbedding(a						,       a						,       a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	int						,       a	:	Optional[int]						,       a	:	int						,       a	:	Optional[int]=None					):





         """simple docstring"""



         __lowerCamelCase						=						self.time_proj(a					)
         __lowerCamelCase						=						self.timestep_embedder(timesteps_proj.to(dtype=a					)					)  # (N, D)

         __lowerCamelCase						=						self.class_embedder(a					)  # (N, D)

         __lowerCamelCase						=						timesteps_emb + class_labels  # (N, D)

         return conditioning




class 					a__   (     nn.Module				):






     def __init__(				self	:	str						,       a	:	int						,       a	:	int						,       a	:	int = 64					):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase						=						nn.LayerNorm(a					)
         __lowerCamelCase						=						AttentionPooling(a						,       a					)
         __lowerCamelCase						=						nn.Linear(a						,       a					)
         __lowerCamelCase						=						nn.LayerNorm(a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	int						,       a	:	Optional[int]					):





         """simple docstring"""



         __lowerCamelCase						=						self.norma(a					)
         __lowerCamelCase						=						self.pool(a					)
         __lowerCamelCase						=						self.proj(a					)
         __lowerCamelCase						=						self.norma(a					)
         return hidden_states




class 					a__   (     nn.Module				):






     def __init__(				self	:	Tuple						,       a	:	int = 7_68						,       a	:	int = 7_68						,       a	:	int = 15_36					):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase						=						nn.Linear(a						,       a					)
         __lowerCamelCase						=						nn.LayerNorm(a					)
         __lowerCamelCase						=						nn.Linear(a						,       a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	str						,       a	:	torch.FloatTensor						,       a	:	torch.FloatTensor					):





         """simple docstring"""



         __lowerCamelCase						=						self.text_proj(a					)
         __lowerCamelCase						=						self.text_norm(a					)

         # image
         __lowerCamelCase						=						self.image_proj(a					)

         return time_image_embeds + time_text_embeds




class 					a__   (     nn.Module				):






     def __init__(				self	:	int						,       a	:	int = 7_68						,       a	:	int = 15_36					):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase						=						nn.Linear(a						,       a					)
         __lowerCamelCase						=						nn.LayerNorm(a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Any						,       a	:	torch.FloatTensor					):





         """simple docstring"""



         __lowerCamelCase						=						self.image_proj(a					)
         __lowerCamelCase						=						self.image_norm(a					)
         return time_image_embeds




class 					a__   (     nn.Module				):






     def __init__(				self	:	List[str]						,       a	:	int = 7_68						,       a	:	int = 15_36					):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase						=						nn.Linear(a						,       a					)
         __lowerCamelCase						=						nn.LayerNorm(a					)
         __lowerCamelCase						=						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						,       2_56						,       3						,       padding=1						,       stride=2					)						,       nn.SiLU()						,       nn.Convad(2_56						,       4						,       3						,       padding=1					)						,       )






     def      SCREAMING_SNAKE_CASE__     (				self	:	Any						,       a	:	torch.FloatTensor						,       a	:	torch.FloatTensor					):





         """simple docstring"""



         __lowerCamelCase						=						self.image_proj(a					)
         __lowerCamelCase						=						self.image_norm(a					)
         __lowerCamelCase						=						self.input_hint_block(a					)
         return time_image_embeds, hint




class 					a__   (     nn.Module				):






     def __init__(				self	:	List[Any]						,       a	:	Any						,       a	:	Union[str, Any]						,       a	:	int=None					):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase						=						dtype
         __lowerCamelCase						=						nn.Parameter(torch.randn(1						,       a					) / embed_dim**0.5					)
         __lowerCamelCase						=						nn.Linear(a						,       a						,       dtype=self.dtype					)
         __lowerCamelCase						=						nn.Linear(a						,       a						,       dtype=self.dtype					)
         __lowerCamelCase						=						nn.Linear(a						,       a						,       dtype=self.dtype					)
         __lowerCamelCase						=						num_heads
         __lowerCamelCase						=						embed_dim // self.num_heads






     def      SCREAMING_SNAKE_CASE__     (				self	:	int						,       a	:	Optional[int]					):





         """simple docstring"""



         __lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase						=						x.size()

         def shape(a	:	List[Any]					):
             # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
             __lowerCamelCase						=						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)
             __lowerCamelCase						=						x.transpose(1						,       2					)
             # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
             __lowerCamelCase						=						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)
             __lowerCamelCase						=						x.transpose(1						,       2					)
             return x

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

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

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

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

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

         return a[:, 0, :]  # cls_token

'''simple docstring'''




def __lowerCAmelCase				(							UpperCamelCase__       )			->					bool:
    if not isinstance(UpperCamelCase__       ,						UpperCamelCase__       ):
        raise ValueError('''Input series is not valid, valid series - [1, 2/3, 2]'''       )
    if len(UpperCamelCase__       ) == 0:
        raise ValueError('''Input list must be a non empty list'''       )
    if len(UpperCamelCase__       ) == 1 and series[0] != 0:
        return True
    __lowerCamelCase						=						[]
    __lowerCamelCase						=						len(UpperCamelCase__       )
    for i in range(0       ,						UpperCamelCase__       ):
        if series[i] == 0:
            raise ValueError('''Input series cannot have 0 as an element'''       )
        rec_series.append(1 / series[i]       )
    __lowerCamelCase						=						rec_series[1] - rec_series[0]
    for index in range(2       ,						UpperCamelCase__       ):
        if rec_series[index] - rec_series[index - 1] != common_diff:
            return False
    return True






def __lowerCAmelCase				(							UpperCamelCase__       )			->					float:
    if not isinstance(UpperCamelCase__       ,						UpperCamelCase__       ):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''       )
    if len(UpperCamelCase__       ) == 0:
        raise ValueError('''Input list must be a non empty list'''       )
    __lowerCamelCase						=						0
    for val in series:
        answer += 1 / val
    return len(UpperCamelCase__       ) / answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()
