





"""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__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase = False		,   _UpperCAmelCase = 1		,   _UpperCAmelCase = 1		,   _UpperCAmelCase = 1_0_0_0_0		,   )			->     Dict:

 '''simple docstring'''






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

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

 snake_case__										= torch.exp(_UpperCAmelCase		)
 snake_case__										= timesteps[:, None].float() * emb[None, :]

 # scale embeddings
 snake_case__										= scale * emb

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

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

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





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase=False		,   _UpperCAmelCase=0		)			->     str:

 '''simple docstring'''






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

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





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Dict:

 '''simple docstring'''






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

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

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





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Optional[Any]:

 '''simple docstring'''






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

 snake_case__										= np.arange(embed_dim // 2		,   dtype=np.floataa		)
 omega /= embed_dim / 2.0
 snake_case__										= 1.0 / 1_0_0_0_0**omega  # (D/2,)

 snake_case__										= pos.reshape(-1		)  # (M,)
 snake_case__										= np.einsum("m,d->md"		,   _UpperCAmelCase		,   _UpperCAmelCase		)  # (M, D/2), outer product

 snake_case__										= np.sin(_UpperCAmelCase		)  # (M, D/2)
 snake_case__										= np.cos(_UpperCAmelCase		)  # (M, D/2)

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



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE=224      ,    __SCREAMING_SNAKE_CASE=224      ,    __SCREAMING_SNAKE_CASE=16      ,    __SCREAMING_SNAKE_CASE=3      ,    __SCREAMING_SNAKE_CASE=768      ,    __SCREAMING_SNAKE_CASE=False      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=True      ,    ):






        '''simple docstring'''

        super().__init__()

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

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

        snake_case__										= get_ad_sincos_pos_embed(__SCREAMING_SNAKE_CASE      ,    int(num_patches**0.5   )   )
        self.register_buffer("pos_embed"      ,    torch.from_numpy(__SCREAMING_SNAKE_CASE   ).float().unsqueeze(0   )      ,    persistent=__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

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



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = "silu"      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE=None      ,    ):






        '''simple docstring'''

        super().__init__()

        snake_case__										= nn.Linear(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

        if cond_proj_dim is not None:
         snake_case__										= nn.Linear(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    bias=__SCREAMING_SNAKE_CASE   )
        else:
         snake_case__										= None

        snake_case__										= get_activation(__SCREAMING_SNAKE_CASE   )

        if out_dim is not None:
         snake_case__										= out_dim
        else:
         snake_case__										= time_embed_dim
        snake_case__										= nn.Linear(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

        if post_act_fn is None:
         snake_case__										= None
        else:
         snake_case__										= get_activation(__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        if condition is not None:
         snake_case__										= sample + self.cond_proj(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.linear_a(__SCREAMING_SNAKE_CASE   )

        if self.act is not None:
         snake_case__										= self.act(__SCREAMING_SNAKE_CASE   )

        snake_case__										= self.linear_a(__SCREAMING_SNAKE_CASE   )

        if self.post_act is not None:
         snake_case__										= self.post_act(__SCREAMING_SNAKE_CASE   )
        return sample



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= get_timestep_embedding(
            __SCREAMING_SNAKE_CASE      ,    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      ,    __SCREAMING_SNAKE_CASE = 256      ,    __SCREAMING_SNAKE_CASE = 1.0      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=True      ,    __SCREAMING_SNAKE_CASE=False   ):






        '''simple docstring'''

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

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

         snake_case__										= self.W

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        if self.log:
         snake_case__										= torch.log(__SCREAMING_SNAKE_CASE   )

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

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



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        super().__init__()

        snake_case__										= height
        snake_case__										= width
        snake_case__										= num_embed
        snake_case__										= embed_dim

        snake_case__										= nn.Embedding(self.num_embed      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= nn.Embedding(self.height      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= nn.Embedding(self.width      ,    __SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= self.emb(__SCREAMING_SNAKE_CASE   )

        snake_case__										= 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
        snake_case__										= height_emb.unsqueeze(2   )

        snake_case__										= 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
        snake_case__										= width_emb.unsqueeze(1   )

        snake_case__										= height_emb + width_emb

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

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

        return emb



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        super().__init__()
        snake_case__										= dropout_prob > 0
        snake_case__										= nn.Embedding(num_classes + use_cfg_embedding      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= num_classes
        snake_case__										= dropout_prob

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

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



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = 1024      ,    __SCREAMING_SNAKE_CASE = 768      ,    __SCREAMING_SNAKE_CASE = 768      ,    __SCREAMING_SNAKE_CASE = 10      ,    ):






        '''simple docstring'''

        super().__init__()

        snake_case__										= num_image_text_embeds
        snake_case__										= nn.Linear(__SCREAMING_SNAKE_CASE      ,    self.num_image_text_embeds * cross_attention_dim   )
        snake_case__										= nn.Linear(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= text_embeds.shape[0]

        # image
        snake_case__										= self.image_embeds(__SCREAMING_SNAKE_CASE   )
        snake_case__										= image_text_embeds.reshape(__SCREAMING_SNAKE_CASE      ,    self.num_image_text_embeds      ,    -1   )

        # text
        snake_case__										= self.text_proj(__SCREAMING_SNAKE_CASE   )

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



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = 768      ,    __SCREAMING_SNAKE_CASE = 768      ,    __SCREAMING_SNAKE_CASE = 32      ,    ):






        '''simple docstring'''

        super().__init__()

        snake_case__										= num_image_text_embeds
        snake_case__										= nn.Linear(__SCREAMING_SNAKE_CASE      ,    self.num_image_text_embeds * cross_attention_dim   )
        snake_case__										= nn.LayerNorm(__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= image_embeds.shape[0]

        # image
        snake_case__										= self.image_embeds(__SCREAMING_SNAKE_CASE   )
        snake_case__										= image_embeds.reshape(__SCREAMING_SNAKE_CASE      ,    self.num_image_text_embeds      ,    -1   )
        snake_case__										= self.norm(__SCREAMING_SNAKE_CASE   )
        return image_embeds



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=0.1   ):






        '''simple docstring'''

        super().__init__()

        snake_case__										= Timesteps(num_channels=256      ,    flip_sin_to_cos=__SCREAMING_SNAKE_CASE      ,    downscale_freq_shift=1   )
        snake_case__										= TimestepEmbedding(in_channels=256      ,    time_embed_dim=__SCREAMING_SNAKE_CASE   )
        snake_case__										= LabelEmbedding(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= self.time_proj(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.timestep_embedder(timesteps_proj.to(dtype=__SCREAMING_SNAKE_CASE   )   )  # (N, D)

        snake_case__										= self.class_embedder(__SCREAMING_SNAKE_CASE   )  # (N, D)

        snake_case__										= timesteps_emb + class_labels  # (N, D)

        return conditioning



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 64   ):






        '''simple docstring'''

        super().__init__()
        snake_case__										= nn.LayerNorm(__SCREAMING_SNAKE_CASE   )
        snake_case__										= AttentionPooling(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= nn.Linear(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= nn.LayerNorm(__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= self.norma(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.pool(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.proj(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.norma(__SCREAMING_SNAKE_CASE   )
        return hidden_states



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = 768      ,    __SCREAMING_SNAKE_CASE = 768      ,    __SCREAMING_SNAKE_CASE = 1536   ):






        '''simple docstring'''

        super().__init__()
        snake_case__										= nn.Linear(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= nn.LayerNorm(__SCREAMING_SNAKE_CASE   )
        snake_case__										= nn.Linear(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= self.text_proj(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.text_norm(__SCREAMING_SNAKE_CASE   )

        # image
        snake_case__										= self.image_proj(__SCREAMING_SNAKE_CASE   )

        return time_image_embeds + time_text_embeds



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = 768      ,    __SCREAMING_SNAKE_CASE = 1536   ):






        '''simple docstring'''

        super().__init__()
        snake_case__										= nn.Linear(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= nn.LayerNorm(__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= self.image_proj(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.image_norm(__SCREAMING_SNAKE_CASE   )
        return time_image_embeds



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = 768      ,    __SCREAMING_SNAKE_CASE = 1536   ):






        '''simple docstring'''

        super().__init__()
        snake_case__										= nn.Linear(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= nn.LayerNorm(__SCREAMING_SNAKE_CASE   )
        snake_case__										= 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 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= self.image_proj(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.image_norm(__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.input_hint_block(__SCREAMING_SNAKE_CASE   )
        return time_image_embeds, hint



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__							,      snake_case__							,      snake_case__										= x.size()

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

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

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

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

        # (bs*n_heads, dim_per_head, class_token_length)
        snake_case__										= torch.einsum("bts,bcs->bct"      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

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

        return a[:, 0, :]  # cls_token








"""simple docstring"""


import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
a__    :   Any    						= logging.get_logger(__name__)



@dataclass
class    lowercase_ :
       snake_case__ :							nn.Module
       snake_case__ :							List[nn.Module] 							=   field(default_factory=__lowerCAmelCase							)
       snake_case__ :							list 							=   field(default_factory=__lowerCAmelCase							)

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= len(list(m.modules()   )   ) == 1 or isinstance(__SCREAMING_SNAKE_CASE      ,    nn.Convad   ) or isinstance(__SCREAMING_SNAKE_CASE      ,    nn.BatchNormad   )
        if has_not_submodules:
         if not isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          self.traced.append(__SCREAMING_SNAKE_CASE   )

       def __call__(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook   )   )
        self.module(__SCREAMING_SNAKE_CASE   )
        [x.remove() for x in self.handles]
        return self

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        return list(filter(lambda __SCREAMING_SNAKE_CASE   : len(list(x.state_dict().keys()   )   ) > 0      ,    self.traced   )   )



@dataclass
class    lowercase_ :
       snake_case__ :							nn.Module
       snake_case__ :							nn.Module
       snake_case__ :							int 							=   0
       snake_case__ :							List 							=   field(default_factory=__lowerCAmelCase							)
       snake_case__ :							List 							=   field(default_factory=__lowerCAmelCase							)

       def __call__(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= Tracker(self.dest   )(__SCREAMING_SNAKE_CASE   ).parametrized
        snake_case__										= Tracker(self.src   )(__SCREAMING_SNAKE_CASE   ).parametrized

        snake_case__										= list(filter(lambda __SCREAMING_SNAKE_CASE   : type(__SCREAMING_SNAKE_CASE   ) not in self.src_skip      ,    __SCREAMING_SNAKE_CASE   )   )
        snake_case__										= list(filter(lambda __SCREAMING_SNAKE_CASE   : type(__SCREAMING_SNAKE_CASE   ) not in self.dest_skip      ,    __SCREAMING_SNAKE_CASE   )   )

        if len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
         raise Exception(
             F"Numbers of operations are different. Source module has {len(__SCREAMING_SNAKE_CASE   )} operations while"
             F" destination module has {len(__SCREAMING_SNAKE_CASE   )}."   )

        for dest_m, src_m in zip(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
         dest_m.load_state_dict(src_m.state_dict()   )
         if self.verbose == 1:
          print(F"Transfered from={src_m} to={dest_m}"   )





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     nn.Module:

 '''simple docstring'''






 snake_case__										= from_model.state_dict()
 snake_case__										= our_model.state_dict()
 snake_case__										= our_model.config
 snake_case__										= []
 for stage_idx in range(len(config.hidden_sizes		)		):
  for block_id in range(config.depths[stage_idx]		):
   snake_case__										= f"block{stage_idx + 1}.{block_id}.layer_scale_1"
   snake_case__										= f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

   all_keys.append((from_key, to_key)		)
   snake_case__										= f"block{stage_idx + 1}.{block_id}.layer_scale_2"
   snake_case__										= f"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

   all_keys.append((from_key, to_key)		)

 for from_key, to_key in all_keys:
  snake_case__										= from_state_dict.pop(_UpperCAmelCase		)

 our_model.load_state_dict(_UpperCAmelCase		)
 return our_model





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase = True		,   )			->     Union[str, Any]:

 '''simple docstring'''






 print(f"Downloading weights for {name}..."		)
 snake_case__										= cached_download(_UpperCAmelCase		)
 print(f"Converting {name}..."		)
 snake_case__										= torch.load(_UpperCAmelCase		)["state_dict"]
 from_model.load_state_dict(_UpperCAmelCase		)
 from_model.eval()
 with torch.no_grad():
  snake_case__										= VanForImageClassification(_UpperCAmelCase		).eval()
  snake_case__										= ModuleTransfer(src=_UpperCAmelCase		,   dest=_UpperCAmelCase		)
  snake_case__										= torch.randn((1, 3, 2_2_4, 2_2_4)		)
  module_transfer(_UpperCAmelCase		)
  snake_case__										= copy_parameters(_UpperCAmelCase		,   _UpperCAmelCase		)

 if not torch.allclose(from_model(_UpperCAmelCase		)		,   our_model(_UpperCAmelCase		).logits		):
  raise ValueError("The model logits don't match the original one."		)

 snake_case__										= name
 print(_UpperCAmelCase		)

 if push_to_hub:
  our_model.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,   commit_message="Add model"		,   use_temp_dir=_UpperCAmelCase		,   )

  # we can use the convnext one
  snake_case__										= AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"		)
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,   commit_message="Add image processor"		,   use_temp_dir=_UpperCAmelCase		,   )

  print(f"Pushed {checkpoint_name}"		)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase = None		,   _UpperCAmelCase = True		)			->     Union[str, Any]:

 '''simple docstring'''






 snake_case__										= "imagenet-1k-id2label.json"
 snake_case__										= 1_0_0_0

 snake_case__										= "huggingface/label-files"
 snake_case__										= num_labels
 snake_case__										= json.load(open(hf_hub_download(_UpperCAmelCase		,   _UpperCAmelCase		,   repo_type="dataset"		)		,   "r"		)		)
 snake_case__										= {int(_UpperCAmelCase		): v for k, v in idalabel.items()}

 snake_case__										= idalabel
 snake_case__										= {v: k for k, v in idalabel.items()}

 snake_case__										= partial(_UpperCAmelCase		,   num_labels=_UpperCAmelCase		,   idalabel=_UpperCAmelCase		,   labelaid=_UpperCAmelCase		)

 snake_case__										= {
     "van-tiny": ImageNetPreTrainedConfig(
         hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]		,   depths=[3, 3, 5, 2]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
     "van-small": ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		,   depths=[2, 2, 4, 2]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
     "van-base": ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		,   depths=[3, 3, 1_2, 3]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
     "van-large": ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		,   depths=[3, 5, 2_7, 3]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
 }

 snake_case__										= {
     "van-tiny": van_tiny,
     "van-small": van_small,
     "van-base": van_base,
     "van-large": van_large,
 }

 snake_case__										= {
     "van-tiny": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
     ),
     "van-small": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
     ),
     "van-base": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
     ),
     "van-large": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
     ),
 }

 if model_name:
  convert_weight_and_push(
      _UpperCAmelCase		,   names_to_config[model_name]		,   checkpoint=names_to_original_checkpoints[model_name]		,   from_model=names_to_original_models[model_name]()		,   save_directory=_UpperCAmelCase		,   push_to_hub=_UpperCAmelCase		,   )
 else:
  for model_name, config in names_to_config.items():
   convert_weight_and_push(
       _UpperCAmelCase		,   _UpperCAmelCase		,   checkpoint=names_to_original_checkpoints[model_name]		,   from_model=names_to_original_models[model_name]()		,   save_directory=_UpperCAmelCase		,   push_to_hub=_UpperCAmelCase		,   )


if __name__ == "__main__":
   a__    :   Any    						= argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model-name""",
       default=None,
       type=str,
       help=(
           """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
           """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=Path,
       required=True,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument(
       """--van_dir""",
       required=True,
       type=Path,
       help=(
           """A path to VAN's original implementation directory. You can download from here:"""
           """ https://github.com/Visual-Attention-Network/VAN-Classification"""
       ),
   )
   parser.add_argument(
       """--push_to_hub""",
       default=True,
       type=bool,
       required=False,
       help="""If True, push model and image processor to the hub.""",
   )

   a__    :   Any    						= parser.parse_args()
   a__    :   Path    						= args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   a__    :   int    						= args.van_dir
   # append the path to the parents to maskformer dir
   sys.path.append(str(van_dir.parent))
   from van.models.van import van_base, van_large, van_small, van_tiny

   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

