





"""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_     (  lowercase_       :							torch.Tensor			,				lowercase_       :							int			,				lowercase_       :							bool = False			,				lowercase_       :							float = 1			,				lowercase_       :							float = 1			,				lowercase_       :							int = 1_0000			,				):

 '''simple docstring'''






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

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	embedding_dim // 2
 __SCREAMING_SNAKE_CASE			:      Any 							=	-math.log(_lowerCamelCase		) * torch.arange(
     start=0			,				end=_lowerCamelCase			,				dtype=torch.floataa			,				device=timesteps.device		)
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	exponent / (half_dim - downscale_freq_shift)

 __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.exp(_lowerCamelCase		)
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	timesteps[:, None].float() * emb[None, :]

 # scale embeddings
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	scale * emb

 # concat sine and cosine embeddings
 __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.cat([torch.sin(_lowerCamelCase		), torch.cos(_lowerCamelCase		)]			,				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			:      Union[str, Any] 							=	torch.nn.functional.pad(_lowerCamelCase			,				(0, 1, 0, 0)		)
 return emb




def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							Optional[Any]			,				lowercase_       :							Union[str, Any]=False			,				lowercase_       :							str=0		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Any 							=	np.arange(_lowerCamelCase			,				dtype=np.floataa		)
 __SCREAMING_SNAKE_CASE			:      Any 							=	np.arange(_lowerCamelCase			,				dtype=np.floataa		)
 __SCREAMING_SNAKE_CASE			:      Dict 							=	np.meshgrid(_lowerCamelCase			,				_lowerCamelCase		)  # here w goes first
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	np.stack(_lowerCamelCase			,				axis=0		)

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	grid.reshape([2, 1, grid_size, grid_size]		)
 __SCREAMING_SNAKE_CASE			:      Dict 							=	get_ad_sincos_pos_embed_from_grid(_lowerCamelCase			,				_lowerCamelCase		)
 if cls_token and extra_tokens > 0:
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	np.concatenate([np.zeros([extra_tokens, embed_dim]		), pos_embed]			,				axis=0		)
 return pos_embed




def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							int		):

 '''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			:      List[str] 							=	get_ad_sincos_pos_embed_from_grid(embed_dim // 2			,				grid[0]		)  # (H*W, D/2)
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	get_ad_sincos_pos_embed_from_grid(embed_dim // 2			,				grid[1]		)  # (H*W, D/2)

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




def 		lowerCAmelCase_     (  lowercase_       :							Dict			,				lowercase_       :							Union[str, Any]		):

 '''simple docstring'''






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

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	np.arange(embed_dim // 2			,				dtype=np.floataa		)
 omega /= embed_dim / 2.0
 __SCREAMING_SNAKE_CASE			:      Dict 							=	1.0 / 1_0000**omega  # (D/2,)

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

 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	np.sin(_lowerCamelCase		)  # (M, D/2)
 __SCREAMING_SNAKE_CASE			:      str 							=	np.cos(_lowerCamelCase		)  # (M, D/2)

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





class       snake_case		(       nn.Module       ):

      def __init__(      self       :Union[str, Any]						,							_lowerCamelCase       :Optional[int]=2_2_4						,							_lowerCamelCase       :Dict=2_2_4						,							_lowerCamelCase       :Tuple=1_6						,							_lowerCamelCase       :Tuple=3						,							_lowerCamelCase       :Dict=7_6_8						,							_lowerCamelCase       :Optional[Any]=False						,							_lowerCamelCase       :List[str]=True						,							_lowerCamelCase       :List[Any]=True						,							):
       super().__init__()

       __SCREAMING_SNAKE_CASE			:      int 							=	(height // patch_size) * (width // patch_size)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	flatten
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	layer_norm

       __SCREAMING_SNAKE_CASE			:      str 							=	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:
        __SCREAMING_SNAKE_CASE			:      int 							=	nn.LayerNorm(__SCREAMING_SNAKE_CASE						,							elementwise_affine=__SCREAMING_SNAKE_CASE						,							eps=1e-6						)
       else:
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	None

       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	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       :str						,							_lowerCamelCase       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.proj(__SCREAMING_SNAKE_CASE						)
       if self.flatten:
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	latent.flatten(2						).transpose(1						,							2						)  # BCHW -> BNC
       if self.layer_norm:
        __SCREAMING_SNAKE_CASE			:      Tuple 							=	self.norm(__SCREAMING_SNAKE_CASE						)
       return latent + self.pos_embed







class       snake_case		(       nn.Module       ):

      def __init__(      self       :List[str]						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Any						,							_lowerCamelCase       :Union[str, Any] = "silu"						,							_lowerCamelCase       :List[str] = None						,							_lowerCamelCase       :Union[str, Any] = None						,							_lowerCamelCase       :Any=None						,							):
       super().__init__()

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	nn.Linear(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)

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

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	get_activation(__SCREAMING_SNAKE_CASE						)

       if out_dim is not None:
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	out_dim
       else:
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	time_embed_dim
       __SCREAMING_SNAKE_CASE			:      Dict 							=	nn.Linear(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)

       if post_act_fn is None:
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	None
       else:
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	get_activation(__SCREAMING_SNAKE_CASE						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :str=None						):




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

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

       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.linear_a(__SCREAMING_SNAKE_CASE						)

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







class       snake_case		(       nn.Module       ):

      def __init__(      self       :List[str]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :List[str]						):
       super().__init__()
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	num_channels
       __SCREAMING_SNAKE_CASE			:      int 							=	flip_sin_to_cos
       __SCREAMING_SNAKE_CASE			:      int 							=	downscale_freq_shift



      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						,							_lowerCamelCase       :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	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       snake_case		(       nn.Module       ):

      def __init__(      self       :str						,							_lowerCamelCase       :List[Any] = 2_5_6						,							_lowerCamelCase       :List[str] = 1.0						,							_lowerCamelCase       :List[str]=True						,							_lowerCamelCase       :int=True						,							_lowerCamelCase       :Optional[int]=False						):
       super().__init__()
       __SCREAMING_SNAKE_CASE			:      Any 							=	nn.Parameter(torch.randn(__SCREAMING_SNAKE_CASE						) * scale						,							requires_grad=__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	log
       __SCREAMING_SNAKE_CASE			:      Any 							=	flip_sin_to_cos

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

        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.W



      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :List[Any]						):




       if self.log:
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	torch.log(__SCREAMING_SNAKE_CASE						)

       __SCREAMING_SNAKE_CASE			:      List[str] 							=	x[:, None] * self.weight[None, :] * 2 * np.pi

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







class       snake_case		(       nn.Module       ):

      def __init__(      self       :int						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :str						,							_lowerCamelCase       :Optional[int]						,							_lowerCamelCase       :int						,							):
       super().__init__()

       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	height
       __SCREAMING_SNAKE_CASE			:      Dict 							=	width
       __SCREAMING_SNAKE_CASE			:      str 							=	num_embed
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	embed_dim

       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	nn.Embedding(self.num_embed						,							__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      int 							=	nn.Embedding(self.height						,							__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      str 							=	nn.Embedding(self.width						,							__SCREAMING_SNAKE_CASE						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						,							_lowerCamelCase       :str						):
       __SCREAMING_SNAKE_CASE			:      int 							=	self.emb(__SCREAMING_SNAKE_CASE						)

       __SCREAMING_SNAKE_CASE			:      Dict 							=	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			:      Optional[Any] 							=	height_emb.unsqueeze(2						)

       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	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			:      str 							=	width_emb.unsqueeze(1						)

       __SCREAMING_SNAKE_CASE			:      Optional[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			:      Dict 							=	emb + pos_emb[:, : emb.shape[1], :]

       return emb







class       snake_case		(       nn.Module       ):

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

      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						,							_lowerCamelCase       :Tuple						,							_lowerCamelCase       :str=None						):




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



      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :List[str]=None						):
       __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			:      List[Any] 							=	self.token_drop(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	self.embedding_table(__SCREAMING_SNAKE_CASE						)
       return embeddings







class       snake_case		(       nn.Module       ):

      def __init__(      self       :str						,							_lowerCamelCase       :Union[str, Any] = 1_0_2_4						,							_lowerCamelCase       :Tuple = 7_6_8						,							_lowerCamelCase       :Optional[Any] = 7_6_8						,							_lowerCamelCase       :Optional[Any] = 1_0						,							):
       super().__init__()

       __SCREAMING_SNAKE_CASE			:      Dict 							=	num_image_text_embeds
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	nn.Linear(__SCREAMING_SNAKE_CASE						,							self.num_image_text_embeds * cross_attention_dim						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	nn.Linear(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						,							_lowerCamelCase       :Any						,							_lowerCamelCase       :Optional[Any]						):
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	text_embeds.shape[0]

       # image
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.image_embeds(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_text_embeds.reshape(__SCREAMING_SNAKE_CASE						,							self.num_image_text_embeds						,							-1						)

       # text
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.text_proj(__SCREAMING_SNAKE_CASE						)

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







class       snake_case		(       nn.Module       ):

      def __init__(      self       :Union[str, Any]						,							_lowerCamelCase       :int = 7_6_8						,							_lowerCamelCase       :Optional[Any] = 7_6_8						,							_lowerCamelCase       :Tuple = 3_2						,							):
       super().__init__()

       __SCREAMING_SNAKE_CASE			:      int 							=	num_image_text_embeds
       __SCREAMING_SNAKE_CASE			:      Dict 							=	nn.Linear(__SCREAMING_SNAKE_CASE						,							self.num_image_text_embeds * cross_attention_dim						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	nn.LayerNorm(__SCREAMING_SNAKE_CASE						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						,							_lowerCamelCase       :int						):
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	image_embeds.shape[0]

       # image
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.image_embeds(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	image_embeds.reshape(__SCREAMING_SNAKE_CASE						,							self.num_image_text_embeds						,							-1						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.norm(__SCREAMING_SNAKE_CASE						)
       return image_embeds







class       snake_case		(       nn.Module       ):

      def __init__(      self       :List[str]						,							_lowerCamelCase       :List[Any]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :Optional[Any]=0.1						):
       super().__init__()

       __SCREAMING_SNAKE_CASE			:      Dict 							=	Timesteps(num_channels=2_5_6						,							flip_sin_to_cos=__SCREAMING_SNAKE_CASE						,							downscale_freq_shift=1						)
       __SCREAMING_SNAKE_CASE			:      str 							=	TimestepEmbedding(in_channels=2_5_6						,							time_embed_dim=__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      str 							=	LabelEmbedding(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :str						,							_lowerCamelCase       :int=None						):
       __SCREAMING_SNAKE_CASE			:      int 							=	self.time_proj(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      int 							=	self.timestep_embedder(timesteps_proj.to(dtype=__SCREAMING_SNAKE_CASE						)						)  # (N, D)

       __SCREAMING_SNAKE_CASE			:      int 							=	self.class_embedder(__SCREAMING_SNAKE_CASE						)  # (N, D)

       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	timesteps_emb + class_labels  # (N, D)

       return conditioning







class       snake_case		(       nn.Module       ):

      def __init__(      self       :List[str]						,							_lowerCamelCase       :Optional[int]						,							_lowerCamelCase       :str						,							_lowerCamelCase       :int = 6_4						):
       super().__init__()
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	nn.LayerNorm(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	AttentionPooling(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	nn.Linear(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	nn.LayerNorm(__SCREAMING_SNAKE_CASE						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :str						,							_lowerCamelCase       :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	self.norma(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      str 							=	self.pool(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.proj(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.norma(__SCREAMING_SNAKE_CASE						)
       return hidden_states







class       snake_case		(       nn.Module       ):

      def __init__(      self       :Any						,							_lowerCamelCase       :Optional[Any] = 7_6_8						,							_lowerCamelCase       :str = 7_6_8						,							_lowerCamelCase       :int = 1_5_3_6						):
       super().__init__()
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	nn.Linear(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      int 							=	nn.LayerNorm(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	nn.Linear(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.text_proj(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.text_norm(__SCREAMING_SNAKE_CASE						)

       # image
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	self.image_proj(__SCREAMING_SNAKE_CASE						)

       return time_image_embeds + time_text_embeds







class       snake_case		(       nn.Module       ):

      def __init__(      self       :Tuple						,							_lowerCamelCase       :Dict = 7_6_8						,							_lowerCamelCase       :Any = 1_5_3_6						):
       super().__init__()
       __SCREAMING_SNAKE_CASE			:      int 							=	nn.Linear(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      str 							=	nn.LayerNorm(__SCREAMING_SNAKE_CASE						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						,							_lowerCamelCase       :Dict						):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.image_proj(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.image_norm(__SCREAMING_SNAKE_CASE						)
       return time_image_embeds







class       snake_case		(       nn.Module       ):

      def __init__(      self       :str						,							_lowerCamelCase       :Any = 7_6_8						,							_lowerCamelCase       :int = 1_5_3_6						):
       super().__init__()
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	nn.Linear(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	nn.LayerNorm(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	nn.Sequential(
           nn.Convad(3						,							1_6						,							3						,							padding=1						)						,							nn.SiLU()						,							nn.Convad(1_6						,							1_6						,							3						,							padding=1						)						,							nn.SiLU()						,							nn.Convad(1_6						,							3_2						,							3						,							padding=1						,							stride=2						)						,							nn.SiLU()						,							nn.Convad(3_2						,							3_2						,							3						,							padding=1						)						,							nn.SiLU()						,							nn.Convad(3_2						,							9_6						,							3						,							padding=1						,							stride=2						)						,							nn.SiLU()						,							nn.Convad(9_6						,							9_6						,							3						,							padding=1						)						,							nn.SiLU()						,							nn.Convad(9_6						,							2_5_6						,							3						,							padding=1						,							stride=2						)						,							nn.SiLU()						,							nn.Convad(2_5_6						,							4						,							3						,							padding=1						)						,							)



      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :Dict						):
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	self.image_proj(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	self.image_norm(__SCREAMING_SNAKE_CASE						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.input_hint_block(__SCREAMING_SNAKE_CASE						)
       return time_image_embeds, hint







class       snake_case		(       nn.Module       ):

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



      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						,							_lowerCamelCase       :Dict						):
       __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	x.size()

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

       __SCREAMING_SNAKE_CASE			:      str 							=	x.mean(dim=1						,							keepdim=__SCREAMING_SNAKE_CASE						) + self.positional_embedding.to(x.dtype						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	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(__SCREAMING_SNAKE_CASE						)						)
       # (bs*n_heads, length+class_token_length, dim_per_head)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	shape(self.k_proj(__SCREAMING_SNAKE_CASE						)						)
       __SCREAMING_SNAKE_CASE			:      int 							=	shape(self.v_proj(__SCREAMING_SNAKE_CASE						)						)

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

       # (bs*n_heads, dim_per_head, class_token_length)
       __SCREAMING_SNAKE_CASE			:      Any 							=	torch.einsum('''bts,bcs->bct'''						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						)

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

       return a[:, 0, :]  # cls_token








"""simple docstring"""





import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)




def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							Union[str, Any]=False			,				lowercase_       :							Optional[Any]=False			,				lowercase_       :							Dict="data2vec."		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	'''backbone.''' if is_semantic else ''''''

 __SCREAMING_SNAKE_CASE			:      Any 							=	[]
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm1.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')		)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
         (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
         (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
     ]		)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ('''mask_token''', F'''{hf_prefix}embeddings.mask_token'''),
          (
              '''rel_pos_bias.relative_position_bias_table''',
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
          ),
          (
              '''rel_pos_bias.relative_position_index''',
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
          ),
          ('''norm.weight''', '''layernorm.weight'''),
          ('''norm.bias''', '''layernorm.bias'''),
      ]		)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
          ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
          ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
          ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
      ]		)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('''fc_norm.weight''', F'''{hf_prefix}pooler.layernorm.weight'''),
          ('''fc_norm.bias''', F'''{hf_prefix}pooler.layernorm.bias'''),
          ('''head.weight''', '''classifier.weight'''),
          ('''head.bias''', '''classifier.bias'''),
      ]		)

 return rename_keys




def 		lowerCAmelCase_     (  lowercase_       :							Union[str, Any]			,				lowercase_       :							Union[str, Any]			,				lowercase_       :							Optional[Any]=False			,				lowercase_       :							Any=False			,				lowercase_       :							Dict="data2vec_vision."		):

 '''simple docstring'''






 for i in range(config.num_hidden_layers		):
  __SCREAMING_SNAKE_CASE			:      int 							=	'''backbone.''' if is_semantic else ''''''
  # queries, keys and values
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	in_proj_weight[
      : config.hidden_size, :
  ]
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	q_bias
  __SCREAMING_SNAKE_CASE			:      str 							=	in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	in_proj_weight[
      -config.hidden_size :, :
  ]
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
  __SCREAMING_SNAKE_CASE			:      int 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	gamma_a
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   __SCREAMING_SNAKE_CASE			:      Dict 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''		)
   __SCREAMING_SNAKE_CASE			:      int 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''		)

   __SCREAMING_SNAKE_CASE			:      List[str] 							=	table
   __SCREAMING_SNAKE_CASE			:      Any 							=	index




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      List[str] 							=	argparse.ArgumentParser(
     '''Convert Data2VecVision to HF for image classification and pretraining'''			,				add_help=lowercase_		)
 parser.add_argument('''--hf_checkpoint_name'''			,				type=lowercase_		)
 parser.add_argument('''--input_size'''			,				default=224			,				type=lowercase_			,				help='''images input size'''		)
 parser.add_argument('''--beit_checkpoint'''			,				default=''''''			,				help='''beit checkpoint'''		)

 return parser.parse_args()




def 		lowerCAmelCase_     (  lowercase_       :							str			,				lowercase_       :							List[str]			,				lowercase_       :							Union[str, Any]		):

 '''simple docstring'''






 def load_state_dict(lowercase_       :							Optional[Any]			,				lowercase_       :							int			,				lowercase_       :							Union[str, Any]=""			,				lowercase_       :							Optional[int]="relative_position_index"		):
  __SCREAMING_SNAKE_CASE			:      Any 							=	[]
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	[]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	[]
  # copy state_dict so _load_from_state_dict can modify it
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	getattr(lowercase_			,				'''_metadata'''			,				lowercase_		)
  __SCREAMING_SNAKE_CASE			:      str 							=	state_dict.copy()
  if metadata is not None:
   __SCREAMING_SNAKE_CASE			:      int 							=	metadata

  def load(lowercase_       :							Any			,				lowercase_       :							Optional[Any]=""		):
   __SCREAMING_SNAKE_CASE			:      Dict 							=	{} if metadata is None else metadata.get(prefix[:-1]			,				{}		)
   module._load_from_state_dict(
       lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_		)
   for name, child in module._modules.items():
    if child is not None:
     load(lowercase_			,				prefix + name + '''.'''		)

  load(lowercase_			,				prefix=lowercase_		)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	[]
  for key in missing_keys:
   __SCREAMING_SNAKE_CASE			:      Dict 							=	True
   for ignore_key in ignore_missing.split('''|'''		):
    if ignore_key in key:
     __SCREAMING_SNAKE_CASE			:      Dict 							=	False
     break
   if keep_flag:
    warn_missing_keys.append(lowercase_		)
   else:
    ignore_missing_keys.append(lowercase_		)

  __SCREAMING_SNAKE_CASE			:      Tuple 							=	warn_missing_keys

  if len(lowercase_		) > 0:
   print(
       '''Weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print(
       '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print('''\n'''.join(lowercase_		)		)

 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	{
     '''pretrained''': False,
     '''use_shared_rel_pos_bias''': True,
     '''use_abs_pos_emb''': False,
     '''init_values''': 0.1,
 }

 if is_finetuned:
  model_kwargs.update(
      {
          '''num_classes''': 1000,
          '''use_mean_pooling''': True,
          '''init_scale''': 0.001,
          '''use_rel_pos_bias''': True,
      }		)

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	create_model(
     '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''			,				**lowercase_			,				)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	model.patch_embed.patch_size
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	(args.input_size // patch_size[0], args.input_size // patch_size[1])
 __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.load(args.beit_checkpoint			,				map_location='''cpu'''		)

 print(F'''Load ckpt from {args.beit_checkpoint}'''		)
 __SCREAMING_SNAKE_CASE			:      Any 							=	None
 for model_key in ("model", "module"):
  if model_key in checkpoint:
   __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint[model_key]
   print(F'''Load state_dict by model_key = {model_key}'''		)
   break

 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	list(checkpoint_model.keys()		)
 for key in all_keys:
  if "relative_position_index" in key:
   checkpoint_model.pop(lowercase_		)

  if "relative_position_bias_table" in key:
   __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint_model[key]
   __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	rel_pos_bias.size()
   __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	model.state_dict()[key].size()
   __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	model.patch_embed.patch_shape
   if dst_patch_shape[0] != dst_patch_shape[1]:
    raise NotImplementedError()

 load_state_dict(lowercase_			,				lowercase_			,				prefix=''''''		)

 return model




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	get_args()

 __SCREAMING_SNAKE_CASE			:      int 							=	'''ft1k''' in args.hf_checkpoint_name
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	'''large''' in args.hf_checkpoint_name

 if is_finetuned:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
  # into this folder.
  import modeling_finetune  # noqa: F401
 else:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
  # into this folder
  # IMPORTANT: Note that for now we've only converted the down-stream
  # model and not the full pretrained model. This means for the integration
  # test you need to add a `return x` after the following line:
  # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
  # to make the integration test pass.
  import modeling_cyclical  # noqa: F401

 # 1. Create model config
 __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionConfig()
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      int 							=	True
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	False
  __SCREAMING_SNAKE_CASE			:      Dict 							=	True
  __SCREAMING_SNAKE_CASE			:      int 							=	1000

  __SCREAMING_SNAKE_CASE			:      Any 							=	'''huggingface/label-files'''
  __SCREAMING_SNAKE_CASE			:      Dict 							=	'''imagenet-1k-id2label.json'''
  __SCREAMING_SNAKE_CASE			:      Dict 							=	json.load(open(hf_hub_download(lowercase_			,				lowercase_			,				repo_type='''dataset'''		)			,				'''r'''		)		)
  __SCREAMING_SNAKE_CASE			:      Dict 							=	{int(lowercase_		): v for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	idalabel
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	{v: k for k, v in idalabel.items()}
 else:
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	False
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	True
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	False

 if is_large:
  __SCREAMING_SNAKE_CASE			:      str 							=	1024
  __SCREAMING_SNAKE_CASE			:      Any 							=	4096
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	24
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	16

 # 2. Load Beit model
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	load_beit_model(lowercase_			,				lowercase_			,				lowercase_		)
 orig_model.eval()

 # 3. Forward Beit model
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	BeitImageProcessor(size=config.image_size			,				do_center_crop=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''		)
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_processor(images=lowercase_			,				return_tensors='''pt'''		)
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	encoding['''pixel_values''']

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	(pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	orig_model(*lowercase_		)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionForImageClassification(lowercase_		)
  hf_model.eval()
  __SCREAMING_SNAKE_CASE			:      Any 							=	False
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	'''data2vec_vision.'''
 else:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionModel(lowercase_		)
  hf_model.eval()
  __SCREAMING_SNAKE_CASE			:      str 							=	True
  __SCREAMING_SNAKE_CASE			:      Any 							=	''''''

 __SCREAMING_SNAKE_CASE			:      Dict 							=	create_rename_keys(lowercase_			,				hf_prefix=lowercase_			,				has_lm_head=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      Dict 							=	orig_model.state_dict()
 for src, dest in rename_keys:
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	state_dict.pop(lowercase_		)
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	val

 read_in_q_k_v(lowercase_			,				lowercase_			,				hf_prefix=lowercase_			,				has_lm_head=lowercase_		)
 __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	hf_model.load_state_dict(lowercase_			,				strict=lowercase_		)
 print('''HF missing'''			,				lowercase_		)
 print('''HF unexpected_keys'''			,				lowercase_		)

 # 5. Forward HF Data2VecVision model
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	hf_model(lowercase_		)

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

 # 6. Compare
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	torch.max(torch.abs(hf_output - orig_model_output		)		).item()

 print(F'''max_absolute_diff = {max_absolute_diff}'''		)
 __SCREAMING_SNAKE_CASE			:      str 							=	torch.allclose(lowercase_			,				lowercase_			,				atol=1E-3		)
 print('''Do both models output the same tensors?'''			,				'''🔥''' if success else '''💩'''		)
 if not success:
  raise Exception('''Something went wRoNg'''		)

 # 7. Save
 print(F'''Saving to {args.hf_checkpoint_name}'''		)
 hf_model.save_pretrained(args.hf_checkpoint_name		)
 image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
    main()
    # Run the following to convert checkpoints
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"

