


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case = False  ,		__snake_case = 1  ,		__snake_case = 1  ,		__snake_case = 10000  ,		)  ->    Any:







 """simple docstring"""


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

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

 _lowercase							   =torch.exp(__snake_case		)
 _lowercase							   =timesteps[:, None].float() * emb[None, :]

 # scale embeddings
 _lowercase							   =scale * emb

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

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

 # zero pad
 if embedding_dim % 2 == 1:
  _lowercase							   =torch.nn.functional.pad(__snake_case  ,		(0, 1, 0, 0)		)
 return emb
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case=False  ,		__snake_case=0		)  ->    str:







 """simple docstring"""


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

 _lowercase							   =grid.reshape([2, 1, grid_size, grid_size]		)
 _lowercase							   =get_ad_sincos_pos_embed_from_grid(__snake_case  ,		__snake_case		)
 if cls_token and extra_tokens > 0:
  _lowercase							   =np.concatenate([np.zeros([extra_tokens, embed_dim]		), pos_embed]  ,		axis=0		)
 return pos_embed
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    Any:







 """simple docstring"""


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

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

 _lowercase							   =np.concatenate([emb_h, emb_w]  ,		axis=1		)  # (H*W, D)
 return emb
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    Dict:







 """simple docstring"""


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

 _lowercase							   =np.arange(embed_dim // 2  ,		dtype=np.floataa		)
 omega /= embed_dim / 2.0
 _lowercase							   =1.0 / 10000**omega  # (D/2,)

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

 _lowercase							   =np.sin(__snake_case		)  # (M, D/2)
 _lowercase							   =np.cos(__snake_case		)  # (M, D/2)

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







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase=2_2_4	,  UpperCAmelCase=2_2_4	,  UpperCAmelCase=1_6	,  UpperCAmelCase=3	,  UpperCAmelCase=7_6_8	,  UpperCAmelCase=False	,  UpperCAmelCase=True	,  UpperCAmelCase=True	,  )  ->     Union[str, Any]:
      super().__init__()

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

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

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





     def   __A      (self	,  UpperCAmelCase )  ->     str:
      _lowercase							   =self.proj(UpperCAmelCase )
      if self.flatten:
       _lowercase							   =latent.flatten(2 ).transpose(1	,  2 )  # BCHW -> BNC
      if self.layer_norm:
       _lowercase							   =self.norm(UpperCAmelCase )
      return latent + self.pos_embed







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase = "silu"	,  UpperCAmelCase = None	,  UpperCAmelCase = None	,  UpperCAmelCase=None	,  )  ->     Any:
      super().__init__()

      _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase )

      if cond_proj_dim is not None:
       _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase	,  bias=UpperCAmelCase )
      else:
       _lowercase							   =None

      _lowercase							   =get_activation(UpperCAmelCase )

      if out_dim is not None:
       _lowercase							   =out_dim
      else:
       _lowercase							   =time_embed_dim
      _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase )

      if post_act_fn is None:
       _lowercase							   =None
      else:
       _lowercase							   =get_activation(UpperCAmelCase )





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase=None )  ->     Union[str, Any]:
      if condition is not None:
       _lowercase							   =sample + self.cond_proj(UpperCAmelCase )
      _lowercase							   =self.linear_a(UpperCAmelCase )

      if self.act is not None:
       _lowercase							   =self.act(UpperCAmelCase )

      _lowercase							   =self.linear_a(UpperCAmelCase )

      if self.post_act is not None:
       _lowercase							   =self.post_act(UpperCAmelCase )
      return sample







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Optional[Any]:
      super().__init__()
      _lowercase							   =num_channels
      _lowercase							   =flip_sin_to_cos
      _lowercase							   =downscale_freq_shift





     def   __A      (self	,  UpperCAmelCase )  ->     Any:
      _lowercase							   =get_timestep_embedding(
          UpperCAmelCase	,  self.num_channels	,  flip_sin_to_cos=self.flip_sin_to_cos	,  downscale_freq_shift=self.downscale_freq_shift	,  )
      return t_emb







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase = 2_5_6	,  UpperCAmelCase = 1.0	,  UpperCAmelCase=True	,  UpperCAmelCase=True	,  UpperCAmelCase=False )  ->     Tuple:
      super().__init__()
      _lowercase							   =nn.Parameter(torch.randn(UpperCAmelCase ) * scale	,  requires_grad=UpperCAmelCase )
      _lowercase							   =log
      _lowercase							   =flip_sin_to_cos

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

       _lowercase							   =self.W





     def   __A      (self	,  UpperCAmelCase )  ->     Optional[int]:
      if self.log:
       _lowercase							   =torch.log(UpperCAmelCase )

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

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







class    lowerCamelCase__     ( nn.Module):





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

      _lowercase							   =height
      _lowercase							   =width
      _lowercase							   =num_embed
      _lowercase							   =embed_dim

      _lowercase							   =nn.Embedding(self.num_embed	,  UpperCAmelCase )
      _lowercase							   =nn.Embedding(self.height	,  UpperCAmelCase )
      _lowercase							   =nn.Embedding(self.width	,  UpperCAmelCase )





     def   __A      (self	,  UpperCAmelCase )  ->     Union[str, Any]:
      _lowercase							   =self.emb(UpperCAmelCase )

      _lowercase							   =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
      _lowercase							   =height_emb.unsqueeze(2 )

      _lowercase							   =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
      _lowercase							   =width_emb.unsqueeze(1 )

      _lowercase							   =height_emb + width_emb

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

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

      return emb







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Optional[int]:
      super().__init__()
      _lowercase							   =dropout_prob > 0
      _lowercase							   =nn.Embedding(num_classes + use_cfg_embedding	,  UpperCAmelCase )
      _lowercase							   =num_classes
      _lowercase							   =dropout_prob





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





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase=None )  ->     Tuple:
      _lowercase							   =self.dropout_prob > 0
      if (self.training and use_dropout) or (force_drop_ids is not None):
       _lowercase							   =self.token_drop(UpperCAmelCase	,  UpperCAmelCase )
      _lowercase							   =self.embedding_table(UpperCAmelCase )
      return embeddings







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase = 1_0_2_4	,  UpperCAmelCase = 7_6_8	,  UpperCAmelCase = 7_6_8	,  UpperCAmelCase = 1_0	,  )  ->     Any:
      super().__init__()

      _lowercase							   =num_image_text_embeds
      _lowercase							   =nn.Linear(UpperCAmelCase	,  self.num_image_text_embeds * cross_attention_dim )
      _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase )





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase )  ->     int:
      _lowercase							   =text_embeds.shape[0]

      # image
      _lowercase							   =self.image_embeds(UpperCAmelCase )
      _lowercase							   =image_text_embeds.reshape(UpperCAmelCase	,  self.num_image_text_embeds	,  -1 )

      # text
      _lowercase							   =self.text_proj(UpperCAmelCase )

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







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase = 7_6_8	,  UpperCAmelCase = 7_6_8	,  UpperCAmelCase = 3_2	,  )  ->     Tuple:
      super().__init__()

      _lowercase							   =num_image_text_embeds
      _lowercase							   =nn.Linear(UpperCAmelCase	,  self.num_image_text_embeds * cross_attention_dim )
      _lowercase							   =nn.LayerNorm(UpperCAmelCase )





     def   __A      (self	,  UpperCAmelCase )  ->     Optional[Any]:
      _lowercase							   =image_embeds.shape[0]

      # image
      _lowercase							   =self.image_embeds(UpperCAmelCase )
      _lowercase							   =image_embeds.reshape(UpperCAmelCase	,  self.num_image_text_embeds	,  -1 )
      _lowercase							   =self.norm(UpperCAmelCase )
      return image_embeds







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase=0.1 )  ->     Optional[int]:
      super().__init__()

      _lowercase							   =Timesteps(num_channels=2_5_6	,  flip_sin_to_cos=UpperCAmelCase	,  downscale_freq_shift=1 )
      _lowercase							   =TimestepEmbedding(in_channels=2_5_6	,  time_embed_dim=UpperCAmelCase )
      _lowercase							   =LabelEmbedding(UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase=None )  ->     Tuple:
      _lowercase							   =self.time_proj(UpperCAmelCase )
      _lowercase							   =self.timestep_embedder(timesteps_proj.to(dtype=UpperCAmelCase ) )  # (N, D)

      _lowercase							   =self.class_embedder(UpperCAmelCase )  # (N, D)

      _lowercase							   =timesteps_emb + class_labels  # (N, D)

      return conditioning







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase = 6_4 )  ->     Union[str, Any]:
      super().__init__()
      _lowercase							   =nn.LayerNorm(UpperCAmelCase )
      _lowercase							   =AttentionPooling(UpperCAmelCase	,  UpperCAmelCase )
      _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase )
      _lowercase							   =nn.LayerNorm(UpperCAmelCase )





     def   __A      (self	,  UpperCAmelCase )  ->     str:
      _lowercase							   =self.norma(UpperCAmelCase )
      _lowercase							   =self.pool(UpperCAmelCase )
      _lowercase							   =self.proj(UpperCAmelCase )
      _lowercase							   =self.norma(UpperCAmelCase )
      return hidden_states







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase = 7_6_8	,  UpperCAmelCase = 7_6_8	,  UpperCAmelCase = 1_5_3_6 )  ->     Union[str, Any]:
      super().__init__()
      _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase )
      _lowercase							   =nn.LayerNorm(UpperCAmelCase )
      _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase )





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Dict:
      # text
      _lowercase							   =self.text_proj(UpperCAmelCase )
      _lowercase							   =self.text_norm(UpperCAmelCase )

      # image
      _lowercase							   =self.image_proj(UpperCAmelCase )

      return time_image_embeds + time_text_embeds







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase = 7_6_8	,  UpperCAmelCase = 1_5_3_6 )  ->     Any:
      super().__init__()
      _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase )
      _lowercase							   =nn.LayerNorm(UpperCAmelCase )





     def   __A      (self	,  UpperCAmelCase )  ->     Optional[Any]:
      # image
      _lowercase							   =self.image_proj(UpperCAmelCase )
      _lowercase							   =self.image_norm(UpperCAmelCase )
      return time_image_embeds







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase = 7_6_8	,  UpperCAmelCase = 1_5_3_6 )  ->     List[str]:
      super().__init__()
      _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase )
      _lowercase							   =nn.LayerNorm(UpperCAmelCase )
      _lowercase							   =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   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase )  ->     List[str]:
      # image
      _lowercase							   =self.image_proj(UpperCAmelCase )
      _lowercase							   =self.image_norm(UpperCAmelCase )
      _lowercase							   =self.input_hint_block(UpperCAmelCase )
      return time_image_embeds, hint







class    lowerCamelCase__     ( nn.Module):





     def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase=None )  ->     int:
      super().__init__()
      _lowercase							   =dtype
      _lowercase							   =nn.Parameter(torch.randn(1	,  UpperCAmelCase ) / embed_dim**0.5 )
      _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase	,  dtype=self.dtype )
      _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase	,  dtype=self.dtype )
      _lowercase							   =nn.Linear(UpperCAmelCase	,  UpperCAmelCase	,  dtype=self.dtype )
      _lowercase							   =num_heads
      _lowercase							   =embed_dim // self.num_heads





     def   __A      (self	,  UpperCAmelCase )  ->     int:
      _lowercase   ,					_lowercase   ,					_lowercase							   =x.size()

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

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

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

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

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

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

      return a[:, 0, :]  # cls_token





import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf

  from transformers.activations_tf import get_tf_activation







@require_tf
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Optional[Any]:
      _lowercase							   =tf.constant([-1_0_0, -1.0, -0.1, 0, 0.1, 1.0, 100.0] )
      _lowercase							   =get_tf_activation('''gelu''' )
      _lowercase							   =get_tf_activation('''gelu_10''' )

      _lowercase							   =gelu(UpperCAmelCase )
      _lowercase							   =geluaa(UpperCAmelCase )

      _lowercase							   =tf.where(y_gelu_aa < 10.0	,  1.0	,  0.0 )

      self.assertEqual(tf.math.reduce_max(UpperCAmelCase ).numpy().item()	,  10.0 )
      self.assertTrue(np.allclose(y_gelu * clipped_mask	,  y_gelu_aa * clipped_mask ) )





     def   __A      (self )  ->     Optional[Any]:
      get_tf_activation('''gelu''' )
      get_tf_activation('''gelu_10''' )
      get_tf_activation('''gelu_fast''' )
      get_tf_activation('''gelu_new''' )
      get_tf_activation('''glu''' )
      get_tf_activation('''mish''' )
      get_tf_activation('''quick_gelu''' )
      get_tf_activation('''relu''' )
      get_tf_activation('''sigmoid''' )
      get_tf_activation('''silu''' )
      get_tf_activation('''swish''' )
      get_tf_activation('''tanh''' )
      with self.assertRaises(UpperCAmelCase ):
       get_tf_activation('''bogus''' )
      with self.assertRaises(UpperCAmelCase ):
       get_tf_activation(UpperCAmelCase )

