

'''simple docstring'''


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = False ,UpperCamelCase = 1 ,UpperCamelCase = 1 ,UpperCamelCase = 1_0_0_0_0 ,)     ->     str:
	assert len(timesteps.shape	) == 1, "Timesteps should be a 1d-array"

	UpperCAmelCase_		:   Union[str, Any]													=       embedding_dim // 2
	UpperCAmelCase_		:   Optional[Any]													=       -math.log(lowerCAmelCase_	) * torch.arange(
	    start=0 ,end=lowerCAmelCase_ ,dtype=torch.floataa ,device=timesteps.device	)
	UpperCAmelCase_		:   str													=       exponent / (half_dim - downscale_freq_shift)

	UpperCAmelCase_		:   Optional[Any]													=       torch.exp(lowerCAmelCase_	)
	UpperCAmelCase_		:   Any													=       timesteps[:, None].float() * emb[None, :]

	# scale embeddings
	UpperCAmelCase_		:   Optional[Any]													=       scale * emb

	# concat sine and cosine embeddings
	UpperCAmelCase_		:   str													=       torch.cat([torch.sin(lowerCAmelCase_	), torch.cos(lowerCAmelCase_	)] ,dim=-1	)

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

	# zero pad
	if embedding_dim % 2 == 1:
		UpperCAmelCase_		:   Optional[int]													=       torch.nn.functional.pad(lowerCAmelCase_ ,(0, 1, 0, 0)	)
	return emb







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase=False ,UpperCamelCase=0	)     ->     Tuple:
	UpperCAmelCase_		:   Optional[int]													=       np.arange(lowerCAmelCase_ ,dtype=np.floataa	)
	UpperCAmelCase_		:   Optional[Any]													=       np.arange(lowerCAmelCase_ ,dtype=np.floataa	)
	UpperCAmelCase_		:   Union[str, Any]													=       np.meshgrid(lowerCAmelCase_ ,lowerCAmelCase_	)  # here w goes first
	UpperCAmelCase_		:   int													=       np.stack(lowerCAmelCase_ ,axis=0	)

	UpperCAmelCase_		:   Union[str, Any]													=       grid.reshape([2, 1, grid_size, grid_size]	)
	UpperCAmelCase_		:   List[Any]													=       get_ad_sincos_pos_embed_from_grid(lowerCAmelCase_ ,lowerCAmelCase_	)
	if cls_token and extra_tokens > 0:
		UpperCAmelCase_		:   Optional[Any]													=       np.concatenate([np.zeros([extra_tokens, embed_dim]	), pos_embed] ,axis=0	)
	return pos_embed







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     str:
	if embed_dim % 2 != 0:
		raise ValueError('embed_dim must be divisible by 2'	)

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

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







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     str:
	if embed_dim % 2 != 0:
		raise ValueError('embed_dim must be divisible by 2'	)

	UpperCAmelCase_		:   Tuple													=       np.arange(embed_dim // 2 ,dtype=np.floataa	)
	omega /= embed_dim / 2.0
	UpperCAmelCase_		:   Optional[int]													=       1.0 / 1_0_0_0_0**omega  # (D/2,)

	UpperCAmelCase_		:   Any													=       pos.reshape(-1	)  # (M,)
	UpperCAmelCase_		:   List[str]													=       np.einsum('m,d->md' ,lowerCAmelCase_ ,lowerCAmelCase_	)  # (M, D/2), outer product

	UpperCAmelCase_		:   str													=       np.sin(lowerCAmelCase_	)  # (M, D/2)
	UpperCAmelCase_		:   Dict													=       np.cos(lowerCAmelCase_	)  # (M, D/2)

	UpperCAmelCase_		:   Union[str, Any]													=       np.concatenate([emb_sin, emb_cos] ,axis=1	)  # (M, D)
	return emb



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case=224     ,      _snake_case=224     ,      _snake_case=16     ,      _snake_case=3     ,      _snake_case=768     ,      _snake_case=False     ,      _snake_case=True     ,      _snake_case=True     ,      )				->					Tuple:
								super().__init__()

								UpperCAmelCase_		:   Tuple													=       (height // patch_size) * (width // patch_size)
								UpperCAmelCase_		:   List[Any]													=       flatten
								UpperCAmelCase_		:   List[str]													=       layer_norm

								UpperCAmelCase_		:   Dict													=       nn.Convad(
								    __UpperCamelCase     ,      __UpperCamelCase     ,      kernel_size=(patch_size, patch_size)     ,      stride=__UpperCamelCase     ,      bias=__UpperCamelCase)
								if layer_norm:
									UpperCAmelCase_		:   Union[str, Any]													=       nn.LayerNorm(__UpperCamelCase     ,      elementwise_affine=__UpperCamelCase     ,      eps=1e-6)
								else:
									UpperCAmelCase_		:   Any													=       None

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





							def     _snake_case				(      self     ,      _snake_case)				->					Optional[Any]:
								UpperCAmelCase_		:   List[str]													=       self.proj(__UpperCamelCase)
								if self.flatten:
									UpperCAmelCase_		:   Union[str, Any]													=       latent.flatten(2).transpose(1     ,      2)  # BCHW -> BNC
								if self.layer_norm:
									UpperCAmelCase_		:   Tuple													=       self.norm(__UpperCamelCase)
								return latent + self.pos_embed



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case = "silu"     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case=None     ,      )				->					Dict:
								super().__init__()

								UpperCAmelCase_		:   Optional[int]													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase)

								if cond_proj_dim is not None:
									UpperCAmelCase_		:   List[str]													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase     ,      bias=__UpperCamelCase)
								else:
									UpperCAmelCase_		:   List[str]													=       None

								UpperCAmelCase_		:   Optional[int]													=       get_activation(__UpperCamelCase)

								if out_dim is not None:
									UpperCAmelCase_		:   Optional[Any]													=       out_dim
								else:
									UpperCAmelCase_		:   int													=       time_embed_dim
								UpperCAmelCase_		:   Optional[int]													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase)

								if post_act_fn is None:
									UpperCAmelCase_		:   Any													=       None
								else:
									UpperCAmelCase_		:   Optional[int]													=       get_activation(__UpperCamelCase)





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case=None)				->					Optional[Any]:
								if condition is not None:
									UpperCAmelCase_		:   List[Any]													=       sample + self.cond_proj(__UpperCamelCase)
								UpperCAmelCase_		:   List[Any]													=       self.linear_a(__UpperCamelCase)

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

								UpperCAmelCase_		:   List[str]													=       self.linear_a(__UpperCamelCase)

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



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case)				->					Optional[Any]:
								super().__init__()
								UpperCAmelCase_		:   List[str]													=       num_channels
								UpperCAmelCase_		:   Tuple													=       flip_sin_to_cos
								UpperCAmelCase_		:   Optional[Any]													=       downscale_freq_shift





							def     _snake_case				(      self     ,      _snake_case)				->					str:
								UpperCAmelCase_		:   List[str]													=       get_timestep_embedding(
								    __UpperCamelCase     ,      self.num_channels     ,      flip_sin_to_cos=self.flip_sin_to_cos     ,      downscale_freq_shift=self.downscale_freq_shift     ,      )
								return t_emb



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case = 256     ,      _snake_case = 1.0     ,      _snake_case=True     ,      _snake_case=True     ,      _snake_case=False)				->					Tuple:
								super().__init__()
								UpperCAmelCase_		:   Dict													=       nn.Parameter(torch.randn(__UpperCamelCase) * scale     ,      requires_grad=__UpperCamelCase)
								UpperCAmelCase_		:   int													=       log
								UpperCAmelCase_		:   Union[str, Any]													=       flip_sin_to_cos

								if set_W_to_weight:
									# to delete later
									UpperCAmelCase_		:   Optional[int]													=       nn.Parameter(torch.randn(__UpperCamelCase) * scale     ,      requires_grad=__UpperCamelCase)

									UpperCAmelCase_		:   str													=       self.W





							def     _snake_case				(      self     ,      _snake_case)				->					Tuple:
								if self.log:
									UpperCAmelCase_		:   str													=       torch.log(__UpperCamelCase)

								UpperCAmelCase_		:   Union[str, Any]													=       x[:, None] * self.weight[None, :] * 2 * np.pi

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



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					int:
								super().__init__()

								UpperCAmelCase_		:   Any													=       height
								UpperCAmelCase_		:   Dict													=       width
								UpperCAmelCase_		:   Union[str, Any]													=       num_embed
								UpperCAmelCase_		:   Optional[Any]													=       embed_dim

								UpperCAmelCase_		:   List[Any]													=       nn.Embedding(self.num_embed     ,      __UpperCamelCase)
								UpperCAmelCase_		:   Union[str, Any]													=       nn.Embedding(self.height     ,      __UpperCamelCase)
								UpperCAmelCase_		:   int													=       nn.Embedding(self.width     ,      __UpperCamelCase)





							def     _snake_case				(      self     ,      _snake_case)				->					Optional[int]:
								UpperCAmelCase_		:   Dict													=       self.emb(__UpperCamelCase)

								UpperCAmelCase_		:   Optional[int]													=       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_		:   Dict													=       height_emb.unsqueeze(2)

								UpperCAmelCase_		:   List[Any]													=       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_		:   int													=       width_emb.unsqueeze(1)

								UpperCAmelCase_		:   Dict													=       height_emb + width_emb

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

								UpperCAmelCase_		:   Tuple													=       emb + pos_emb[:, : emb.shape[1], :]

								return emb



class        lowercase    (	nn.Module			):


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


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





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case=None)				->					List[str]:
								UpperCAmelCase_		:   int													=       self.dropout_prob > 0
								if (self.training and use_dropout) or (force_drop_ids is not None):
									UpperCAmelCase_		:   Dict													=       self.token_drop(__UpperCamelCase     ,      __UpperCamelCase)
								UpperCAmelCase_		:   Optional[Any]													=       self.embedding_table(__UpperCamelCase)
								return embeddings



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case = 1024     ,      _snake_case = 768     ,      _snake_case = 768     ,      _snake_case = 10     ,      )				->					str:
								super().__init__()

								UpperCAmelCase_		:   int													=       num_image_text_embeds
								UpperCAmelCase_		:   Tuple													=       nn.Linear(__UpperCamelCase     ,      self.num_image_text_embeds * cross_attention_dim)
								UpperCAmelCase_		:   Dict													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase)





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					int:
								UpperCAmelCase_		:   Any													=       text_embeds.shape[0]

								# image
								UpperCAmelCase_		:   str													=       self.image_embeds(__UpperCamelCase)
								UpperCAmelCase_		:   List[Any]													=       image_text_embeds.reshape(__UpperCamelCase     ,      self.num_image_text_embeds     ,      -1)

								# text
								UpperCAmelCase_		:   Any													=       self.text_proj(__UpperCamelCase)

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



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case = 768     ,      _snake_case = 768     ,      _snake_case = 32     ,      )				->					Dict:
								super().__init__()

								UpperCAmelCase_		:   List[Any]													=       num_image_text_embeds
								UpperCAmelCase_		:   Tuple													=       nn.Linear(__UpperCamelCase     ,      self.num_image_text_embeds * cross_attention_dim)
								UpperCAmelCase_		:   List[str]													=       nn.LayerNorm(__UpperCamelCase)





							def     _snake_case				(      self     ,      _snake_case)				->					Optional[Any]:
								UpperCAmelCase_		:   int													=       image_embeds.shape[0]

								# image
								UpperCAmelCase_		:   Optional[Any]													=       self.image_embeds(__UpperCamelCase)
								UpperCAmelCase_		:   str													=       image_embeds.reshape(__UpperCamelCase     ,      self.num_image_text_embeds     ,      -1)
								UpperCAmelCase_		:   Union[str, Any]													=       self.norm(__UpperCamelCase)
								return image_embeds



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case=0.1)				->					Union[str, Any]:
								super().__init__()

								UpperCAmelCase_		:   Tuple													=       Timesteps(num_channels=256     ,      flip_sin_to_cos=__UpperCamelCase     ,      downscale_freq_shift=1)
								UpperCAmelCase_		:   Tuple													=       TimestepEmbedding(in_channels=256     ,      time_embed_dim=__UpperCamelCase)
								UpperCAmelCase_		:   List[str]													=       LabelEmbedding(__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase)





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case=None)				->					Tuple:
								UpperCAmelCase_		:   Dict													=       self.time_proj(__UpperCamelCase)
								UpperCAmelCase_		:   Any													=       self.timestep_embedder(timesteps_proj.to(dtype=__UpperCamelCase))  # (N, D)

								UpperCAmelCase_		:   List[str]													=       self.class_embedder(__UpperCamelCase)  # (N, D)

								UpperCAmelCase_		:   int													=       timesteps_emb + class_labels  # (N, D)

								return conditioning



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case = 64)				->					Any:
								super().__init__()
								UpperCAmelCase_		:   Optional[Any]													=       nn.LayerNorm(__UpperCamelCase)
								UpperCAmelCase_		:   Union[str, Any]													=       AttentionPooling(__UpperCamelCase     ,      __UpperCamelCase)
								UpperCAmelCase_		:   Optional[int]													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase)
								UpperCAmelCase_		:   Optional[int]													=       nn.LayerNorm(__UpperCamelCase)





							def     _snake_case				(      self     ,      _snake_case)				->					Tuple:
								UpperCAmelCase_		:   Optional[Any]													=       self.norma(__UpperCamelCase)
								UpperCAmelCase_		:   Dict													=       self.pool(__UpperCamelCase)
								UpperCAmelCase_		:   str													=       self.proj(__UpperCamelCase)
								UpperCAmelCase_		:   List[Any]													=       self.norma(__UpperCamelCase)
								return hidden_states



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case = 768     ,      _snake_case = 768     ,      _snake_case = 1536)				->					Optional[int]:
								super().__init__()
								UpperCAmelCase_		:   Dict													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase)
								UpperCAmelCase_		:   List[str]													=       nn.LayerNorm(__UpperCamelCase)
								UpperCAmelCase_		:   Dict													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase)





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					Optional[int]:
								# text
								UpperCAmelCase_		:   Optional[int]													=       self.text_proj(__UpperCamelCase)
								UpperCAmelCase_		:   List[str]													=       self.text_norm(__UpperCamelCase)

								# image
								UpperCAmelCase_		:   Dict													=       self.image_proj(__UpperCamelCase)

								return time_image_embeds + time_text_embeds



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case = 768     ,      _snake_case = 1536)				->					List[Any]:
								super().__init__()
								UpperCAmelCase_		:   Dict													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase)
								UpperCAmelCase_		:   Any													=       nn.LayerNorm(__UpperCamelCase)





							def     _snake_case				(      self     ,      _snake_case)				->					Tuple:
								# image
								UpperCAmelCase_		:   str													=       self.image_proj(__UpperCamelCase)
								UpperCAmelCase_		:   Optional[int]													=       self.image_norm(__UpperCamelCase)
								return time_image_embeds



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case = 768     ,      _snake_case = 1536)				->					List[str]:
								super().__init__()
								UpperCAmelCase_		:   Tuple													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase)
								UpperCAmelCase_		:   str													=       nn.LayerNorm(__UpperCamelCase)
								UpperCAmelCase_		:   Optional[Any]													=       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     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					Tuple:
								# image
								UpperCAmelCase_		:   Dict													=       self.image_proj(__UpperCamelCase)
								UpperCAmelCase_		:   List[Any]													=       self.image_norm(__UpperCamelCase)
								UpperCAmelCase_		:   List[Any]													=       self.input_hint_block(__UpperCamelCase)
								return time_image_embeds, hint



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case=None)				->					Tuple:
								super().__init__()
								UpperCAmelCase_		:   str													=       dtype
								UpperCAmelCase_		:   Optional[int]													=       nn.Parameter(torch.randn(1     ,      __UpperCamelCase) / embed_dim**0.5)
								UpperCAmelCase_		:   Tuple													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase     ,      dtype=self.dtype)
								UpperCAmelCase_		:   List[str]													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase     ,      dtype=self.dtype)
								UpperCAmelCase_		:   int													=       nn.Linear(__UpperCamelCase     ,      __UpperCamelCase     ,      dtype=self.dtype)
								UpperCAmelCase_		:   Any													=       num_heads
								UpperCAmelCase_		:   Any													=       embed_dim // self.num_heads





							def     _snake_case				(      self     ,      _snake_case)				->					Optional[Any]:
								UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_		:   Tuple													=       x.size()

								def shape(_snake_case):
									# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
									UpperCAmelCase_		:   Union[str, Any]													=       x.view(__UpperCamelCase     ,      -1     ,      self.num_heads     ,      self.dim_per_head)
									# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
									UpperCAmelCase_		:   Optional[Any]													=       x.transpose(1     ,      2)
									# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
									UpperCAmelCase_		:   Union[str, Any]													=       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_		:   List[str]													=       x.transpose(1     ,      2)
									return x

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

								# (bs*n_heads, class_token_length, dim_per_head)
								UpperCAmelCase_		:   Dict													=       shape(self.q_proj(__UpperCamelCase))
								# (bs*n_heads, length+class_token_length, dim_per_head)
								UpperCAmelCase_		:   Dict													=       shape(self.k_proj(__UpperCamelCase))
								UpperCAmelCase_		:   Optional[Any]													=       shape(self.v_proj(__UpperCamelCase))

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

								# (bs*n_heads, dim_per_head, class_token_length)
								UpperCAmelCase_		:   List[Any]													=       torch.einsum('bts,bcs->bct'     ,      __UpperCamelCase     ,      __UpperCamelCase)

								# (bs, length+1, width)
								UpperCAmelCase_		:   List[Any]													=       a.reshape(__UpperCamelCase     ,      -1     ,      1).transpose(1     ,      2)

								return a[:, 0, :]  # cls_token











'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__  :	Optional[Any]            =      {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	List[Any]            =      ['MarianTokenizer']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	List[str]            =      [
          'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MarianForCausalLM',
          'MarianModel',
          'MarianMTModel',
          'MarianPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	Optional[Any]            =      ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	Any            =      ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
      from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_marian import MarianTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_marian import (
                MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarianForCausalLM,
                MarianModel,
                MarianMTModel,
                MarianPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
      import sys

      lowercase__  :	Any            =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




