
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ = False ,						a__ = 1 ,						a__ = 1 ,						a__ = 10_000 ,						):



    """simple docstring"""



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

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = embedding_dim // 2
    SCREAMING_SNAKE_CASE				:				Dict				      = -math.log(a__   ) * torch.arange(
        start=0 ,						end=a__ ,						dtype=torch.floataa ,						device=timesteps.device   )
    SCREAMING_SNAKE_CASE				:				Tuple				      = exponent / (half_dim - downscale_freq_shift)

    SCREAMING_SNAKE_CASE				:				List[str]				      = torch.exp(a__   )
    SCREAMING_SNAKE_CASE				:				Tuple				      = timesteps[:, None].float() * emb[None, :]

    # scale embeddings
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = scale * emb

    # concat sine and cosine embeddings
    SCREAMING_SNAKE_CASE				:				Tuple				      = torch.cat([torch.sin(a__   ), torch.cos(a__   )] ,						dim=-1   )

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

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







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__=False ,						a__=0   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				int				      = np.arange(a__ ,						dtype=np.floataa   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = np.arange(a__ ,						dtype=np.floataa   )
    SCREAMING_SNAKE_CASE				:				str				      = np.meshgrid(a__ ,						a__   )  # here w goes first
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = np.stack(a__ ,						axis=0   )

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







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



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

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

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







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



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

    SCREAMING_SNAKE_CASE				:				str				      = np.arange(embed_dim // 2 ,						dtype=np.floataa   )
    omega /= embed_dim / 2.0
    SCREAMING_SNAKE_CASE				:				List[Any]				      = 1.0 / 10_000**omega  # (D/2,)

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

    SCREAMING_SNAKE_CASE				:				List[str]				      = np.sin(a__   )  # (M, D/2)
    SCREAMING_SNAKE_CASE				:				Dict				      = np.cos(a__   )  # (M, D/2)

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






class     a_       (			nn.Module							):



       """simple docstring"""






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

           SCREAMING_SNAKE_CASE				:				List[Any]				      = (height // patch_size) * (width // patch_size)
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = flatten
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = layer_norm

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

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = 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 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				str				      = self.proj(_lowerCamelCase   )
           if self.flatten:
               SCREAMING_SNAKE_CASE				:				List[str]				      = latent.flatten(2   ).transpose(1				,       2   )  # BCHW -> BNC
           if self.layer_norm:
               SCREAMING_SNAKE_CASE				:				List[str]				      = self.norm(_lowerCamelCase   )
           return latent + self.pos_embed






class     a_       (			nn.Module							):



       """simple docstring"""






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

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = nn.Linear(_lowerCamelCase				,       _lowerCamelCase   )

           if cond_proj_dim is not None:
               SCREAMING_SNAKE_CASE				:				str				      = nn.Linear(_lowerCamelCase				,       _lowerCamelCase				,       bias=_lowerCamelCase   )
           else:
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = None

           SCREAMING_SNAKE_CASE				:				str				      = get_activation(_lowerCamelCase   )

           if out_dim is not None:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = out_dim
           else:
               SCREAMING_SNAKE_CASE				:				int				      = time_embed_dim
           SCREAMING_SNAKE_CASE				:				Tuple				      = nn.Linear(_lowerCamelCase				,       _lowerCamelCase   )

           if post_act_fn is None:
               SCREAMING_SNAKE_CASE				:				Tuple				      = None
           else:
               SCREAMING_SNAKE_CASE				:				List[Any]				      = get_activation(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=None   )					->List[Any]:
           if condition is not None:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = sample + self.cond_proj(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.linear_a(_lowerCamelCase   )

           if self.act is not None:
               SCREAMING_SNAKE_CASE				:				Any				      = self.act(_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.linear_a(_lowerCamelCase   )

           if self.post_act is not None:
               SCREAMING_SNAKE_CASE				:				Tuple				      = self.post_act(_lowerCamelCase   )
           return sample






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[int]:
           super().__init__()
           SCREAMING_SNAKE_CASE				:				str				      = num_channels
           SCREAMING_SNAKE_CASE				:				Dict				      = flip_sin_to_cos
           SCREAMING_SNAKE_CASE				:				Dict				      = downscale_freq_shift






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				List[str]				      = 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     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase = 256				,       _lowerCamelCase = 1.0				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase=False   )					->Any:
           super().__init__()
           SCREAMING_SNAKE_CASE				:				int				      = nn.Parameter(torch.randn(_lowerCamelCase   ) * scale				,       requires_grad=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = log
           SCREAMING_SNAKE_CASE				:				int				      = flip_sin_to_cos

           if set_W_to_weight:
               # to delete later
               SCREAMING_SNAKE_CASE				:				str				      = nn.Parameter(torch.randn(_lowerCamelCase   ) * scale				,       requires_grad=_lowerCamelCase   )

               SCREAMING_SNAKE_CASE				:				List[str]				      = self.W






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Tuple:
           if self.log:
               SCREAMING_SNAKE_CASE				:				Any				      = torch.log(_lowerCamelCase   )

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

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






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->List[Any]:
           super().__init__()

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = height
           SCREAMING_SNAKE_CASE				:				Any				      = width
           SCREAMING_SNAKE_CASE				:				List[str]				      = num_embed
           SCREAMING_SNAKE_CASE				:				int				      = embed_dim

           SCREAMING_SNAKE_CASE				:				List[str]				      = nn.Embedding(self.num_embed				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = nn.Embedding(self.height				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = nn.Embedding(self.width				,       _lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.emb(_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				List[Any]				      = 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
           SCREAMING_SNAKE_CASE				:				List[str]				      = height_emb.unsqueeze(2   )

           SCREAMING_SNAKE_CASE				:				List[str]				      = 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
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = width_emb.unsqueeze(1   )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = height_emb + width_emb

           # 1 x H x W x D -> 1 x L xD
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = pos_emb.view(1				,       self.height * self.width				,       -1   )

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = emb + pos_emb[:, : emb.shape[1], :]

           return emb






class     a_       (			nn.Module							):



       """simple docstring"""






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






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=None   )					->Dict:
           if force_drop_ids is None:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.rand(labels.shape[0]				,       device=labels.device   ) < self.dropout_prob
           else:
               SCREAMING_SNAKE_CASE				:				str				      = torch.tensor(force_drop_ids == 1   )
           SCREAMING_SNAKE_CASE				:				int				      = torch.where(_lowerCamelCase				,       self.num_classes				,       _lowerCamelCase   )
           return labels






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=None   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.dropout_prob > 0
           if (self.training and use_dropout) or (force_drop_ids is not None):
               SCREAMING_SNAKE_CASE				:				int				      = self.token_drop(_lowerCamelCase				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = self.embedding_table(_lowerCamelCase   )
           return embeddings






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase = 1024				,       _lowerCamelCase = 768				,       _lowerCamelCase = 768				,       _lowerCamelCase = 10				,       )					->List[Any]:
           super().__init__()

           SCREAMING_SNAKE_CASE				:				List[Any]				      = num_image_text_embeds
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = nn.Linear(_lowerCamelCase				,       self.num_image_text_embeds * cross_attention_dim   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = nn.Linear(_lowerCamelCase				,       _lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = text_embeds.shape[0]

           # image
           SCREAMING_SNAKE_CASE				:				str				      = self.image_embeds(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = image_text_embeds.reshape(_lowerCamelCase				,       self.num_image_text_embeds				,       -1   )

           # text
           SCREAMING_SNAKE_CASE				:				str				      = self.text_proj(_lowerCamelCase   )

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






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase = 768				,       _lowerCamelCase = 768				,       _lowerCamelCase = 32				,       )					->List[Any]:
           super().__init__()

           SCREAMING_SNAKE_CASE				:				int				      = num_image_text_embeds
           SCREAMING_SNAKE_CASE				:				List[Any]				      = nn.Linear(_lowerCamelCase				,       self.num_image_text_embeds * cross_attention_dim   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = nn.LayerNorm(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = image_embeds.shape[0]

           # image
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.image_embeds(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = image_embeds.reshape(_lowerCamelCase				,       self.num_image_text_embeds				,       -1   )
           SCREAMING_SNAKE_CASE				:				int				      = self.norm(_lowerCamelCase   )
           return image_embeds






class     a_       (			nn.Module							):



       """simple docstring"""






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

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = Timesteps(num_channels=256				,       flip_sin_to_cos=_lowerCamelCase				,       downscale_freq_shift=1   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = TimestepEmbedding(in_channels=256				,       time_embed_dim=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = LabelEmbedding(_lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase=None   )					->str:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.time_proj(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = self.timestep_embedder(timesteps_proj.to(dtype=_lowerCamelCase   )   )  # (N, D)

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.class_embedder(_lowerCamelCase   )  # (N, D)

           SCREAMING_SNAKE_CASE				:				Any				      = timesteps_emb + class_labels  # (N, D)

           return conditioning






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase = 64   )					->List[Any]:
           super().__init__()
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = nn.LayerNorm(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = AttentionPooling(_lowerCamelCase				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = nn.Linear(_lowerCamelCase				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = nn.LayerNorm(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->str:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.norma(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = self.pool(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.proj(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = self.norma(_lowerCamelCase   )
           return hidden_states






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase = 768				,       _lowerCamelCase = 768				,       _lowerCamelCase = 1536   )					->List[Any]:
           super().__init__()
           SCREAMING_SNAKE_CASE				:				List[Any]				      = nn.Linear(_lowerCamelCase				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = nn.LayerNorm(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = nn.Linear(_lowerCamelCase				,       _lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Any:
           # text
           SCREAMING_SNAKE_CASE				:				Any				      = self.text_proj(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.text_norm(_lowerCamelCase   )

           # image
           SCREAMING_SNAKE_CASE				:				int				      = self.image_proj(_lowerCamelCase   )

           return time_image_embeds + time_text_embeds






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase = 768				,       _lowerCamelCase = 1536   )					->Tuple:
           super().__init__()
           SCREAMING_SNAKE_CASE				:				List[Any]				      = nn.Linear(_lowerCamelCase				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = nn.LayerNorm(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->int:
           # image
           SCREAMING_SNAKE_CASE				:				Dict				      = self.image_proj(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = self.image_norm(_lowerCamelCase   )
           return time_image_embeds






class     a_       (			nn.Module							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase = 768				,       _lowerCamelCase = 1536   )					->Any:
           super().__init__()
           SCREAMING_SNAKE_CASE				:				Any				      = nn.Linear(_lowerCamelCase				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = nn.LayerNorm(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = 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 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[int]:
           # image
           SCREAMING_SNAKE_CASE				:				Dict				      = self.image_proj(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = self.image_norm(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.input_hint_block(_lowerCamelCase   )
           return time_image_embeds, hint






class     a_       (			nn.Module							):



       """simple docstring"""






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






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Optional[int]:
           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Tuple				      = x.size()

           def shape(_lowerCamelCase   ):
               # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
               SCREAMING_SNAKE_CASE				:				int				      = 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)
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = x.transpose(1				,       2   )
               # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
               SCREAMING_SNAKE_CASE				:				int				      = 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)
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = x.transpose(1				,       2   )
               return x

           SCREAMING_SNAKE_CASE				:				Dict				      = x.mean(dim=1				,       keepdim=_lowerCamelCase   ) + self.positional_embedding.to(x.dtype   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = torch.cat([class_token, x]				,       dim=1   )  # (bs, length+1, width)

           # (bs*n_heads, class_token_length, dim_per_head)
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = shape(self.q_proj(_lowerCamelCase   )   )
           # (bs*n_heads, length+class_token_length, dim_per_head)
           SCREAMING_SNAKE_CASE				:				Tuple				      = shape(self.k_proj(_lowerCamelCase   )   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = shape(self.v_proj(_lowerCamelCase   )   )

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

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

           # (bs, length+1, width)
           SCREAMING_SNAKE_CASE				:				List[str]				      = a.reshape(_lowerCamelCase				,       -1				,       1   ).transpose(1				,       2   )

           return a[:, 0, :]  # cls_token


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__				:      List[Any]    		=     {
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__				:      List[str]    		=     [
          '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DecisionTransformerGPT2Model''',
          '''DecisionTransformerGPT2PreTrainedModel''',
          '''DecisionTransformerModel''',
          '''DecisionTransformerPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DecisionTransformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_decision_transformer import (
                DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DecisionTransformerGPTaModel,
                DecisionTransformerGPTaPreTrainedModel,
                DecisionTransformerModel,
                DecisionTransformerPreTrainedModel,
            )


else:
      import sys

      a__				:      str    		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
