





import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						List[str]     ,    _UpperCAmelCase				:						List[Any]     ,    _UpperCAmelCase				:						Any = False     ,    _UpperCAmelCase				:						Optional[int] = 1     ,    _UpperCAmelCase				:						Optional[Any] = 1     ,    _UpperCAmelCase				:						List[Any] = 1_0000     ,    ):
 assert len(timesteps.shape ) == 1, "Timesteps should be a 1d-array"

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

 lowerCAmelCase													=   torch.exp(UpperCamelCase_ )
 lowerCAmelCase													=   timesteps[:, None].float() * emb[None, :]

 # scale embeddings
 lowerCAmelCase													=   scale * emb

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

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

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

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Optional[Any]     ,    _UpperCAmelCase				:						List[str]     ,    _UpperCAmelCase				:						int=False     ,    _UpperCAmelCase				:						Tuple=0 ):
 lowerCAmelCase													=   np.arange(UpperCamelCase_     ,    dtype=np.floataa )
 lowerCAmelCase													=   np.arange(UpperCamelCase_     ,    dtype=np.floataa )
 lowerCAmelCase													=   np.meshgrid(UpperCamelCase_     ,    UpperCamelCase_ )  # here w goes first
 lowerCAmelCase													=   np.stack(UpperCamelCase_     ,    axis=0 )

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

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Any     ,    _UpperCAmelCase				:						Optional[int] ):






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

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

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

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Tuple     ,    _UpperCAmelCase				:						Optional[Any] ):






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

 lowerCAmelCase													=   np.arange(embed_dim // 2     ,    dtype=np.floataa )
 omega /= embed_dim / 2.0
 lowerCAmelCase													=   1.0 / 1_0000**omega  # (D/2,)

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

 lowerCAmelCase													=   np.sin(UpperCamelCase_ )  # (M, D/2)
 lowerCAmelCase													=   np.cos(UpperCamelCase_ )  # (M, D/2)

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







class 			a       (      nn.Module     ):



 def __init__(    self							,					_snake_case=2_24							,					_snake_case=2_24							,					_snake_case=16							,					_snake_case=3							,					_snake_case=7_68							,					_snake_case=False							,					_snake_case=True							,					_snake_case=True							,					):







  """simple docstring"""






  super().__init__()

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

  lowerCAmelCase													=   nn.Convad(
      __snake_case							,					__snake_case							,					kernel_size=(patch_size, patch_size)							,					stride=__snake_case							,					bias=__snake_case    )
  if layer_norm:
   lowerCAmelCase													=   nn.LayerNorm(__snake_case							,					elementwise_affine=__snake_case							,					eps=1E-6    )
  else:
   lowerCAmelCase													=   None

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



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






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







class 			a       (      nn.Module     ):



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







  """simple docstring"""






  super().__init__()

  lowerCAmelCase													=   nn.Linear(__snake_case							,					__snake_case    )

  if cond_proj_dim is not None:
   lowerCAmelCase													=   nn.Linear(__snake_case							,					__snake_case							,					bias=__snake_case    )
  else:
   lowerCAmelCase													=   None

  lowerCAmelCase													=   get_activation(__snake_case    )

  if out_dim is not None:
   lowerCAmelCase													=   out_dim
  else:
   lowerCAmelCase													=   time_embed_dim
  lowerCAmelCase													=   nn.Linear(__snake_case							,					__snake_case    )

  if post_act_fn is None:
   lowerCAmelCase													=   None
  else:
   lowerCAmelCase													=   get_activation(__snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case=None    ):







  """simple docstring"""






  if condition is not None:
   lowerCAmelCase													=   sample + self.cond_proj(__snake_case    )
  lowerCAmelCase													=   self.linear_a(__snake_case    )

  if self.act is not None:
   lowerCAmelCase													=   self.act(__snake_case    )

  lowerCAmelCase													=   self.linear_a(__snake_case    )

  if self.post_act is not None:
   lowerCAmelCase													=   self.post_act(__snake_case    )
  return sample







class 			a       (      nn.Module     ):



 def __init__(    self							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






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



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   get_timestep_embedding(
      __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 			a       (      nn.Module     ):



 def __init__(    self							,					_snake_case = 2_56							,					_snake_case = 1.0							,					_snake_case=True							,					_snake_case=True							,					_snake_case=False    ):







  """simple docstring"""






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

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

   lowerCAmelCase													=   self.W



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  if self.log:
   lowerCAmelCase													=   torch.log(__snake_case    )

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

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







class 			a       (      nn.Module     ):



 def __init__(    self							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					):







  """simple docstring"""






  super().__init__()

  lowerCAmelCase													=   height
  lowerCAmelCase													=   width
  lowerCAmelCase													=   num_embed
  lowerCAmelCase													=   embed_dim

  lowerCAmelCase													=   nn.Embedding(self.num_embed							,					__snake_case    )
  lowerCAmelCase													=   nn.Embedding(self.height							,					__snake_case    )
  lowerCAmelCase													=   nn.Embedding(self.width							,					__snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.emb(__snake_case    )

  lowerCAmelCase													=   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
  lowerCAmelCase													=   height_emb.unsqueeze(2    )

  lowerCAmelCase													=   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
  lowerCAmelCase													=   width_emb.unsqueeze(1    )

  lowerCAmelCase													=   height_emb + width_emb

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

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

  return emb







class 			a       (      nn.Module     ):



 def __init__(    self							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase													=   dropout_prob > 0
  lowerCAmelCase													=   nn.Embedding(num_classes + use_cfg_embedding							,					__snake_case    )
  lowerCAmelCase													=   num_classes
  lowerCAmelCase													=   dropout_prob



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case=None    ):







  """simple docstring"""






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



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case=None    ):







  """simple docstring"""






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







class 			a       (      nn.Module     ):



 def __init__(    self							,					_snake_case = 10_24							,					_snake_case = 7_68							,					_snake_case = 7_68							,					_snake_case = 10							,					):







  """simple docstring"""






  super().__init__()

  lowerCAmelCase													=   num_image_text_embeds
  lowerCAmelCase													=   nn.Linear(__snake_case							,					self.num_image_text_embeds * cross_attention_dim    )
  lowerCAmelCase													=   nn.Linear(__snake_case							,					__snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   text_embeds.shape[0]

  # image
  lowerCAmelCase													=   self.image_embeds(__snake_case    )
  lowerCAmelCase													=   image_text_embeds.reshape(__snake_case							,					self.num_image_text_embeds							,					-1    )

  # text
  lowerCAmelCase													=   self.text_proj(__snake_case    )

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







class 			a       (      nn.Module     ):



 def __init__(    self							,					_snake_case = 7_68							,					_snake_case = 7_68							,					_snake_case = 32							,					):







  """simple docstring"""






  super().__init__()

  lowerCAmelCase													=   num_image_text_embeds
  lowerCAmelCase													=   nn.Linear(__snake_case							,					self.num_image_text_embeds * cross_attention_dim    )
  lowerCAmelCase													=   nn.LayerNorm(__snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   image_embeds.shape[0]

  # image
  lowerCAmelCase													=   self.image_embeds(__snake_case    )
  lowerCAmelCase													=   image_embeds.reshape(__snake_case							,					self.num_image_text_embeds							,					-1    )
  lowerCAmelCase													=   self.norm(__snake_case    )
  return image_embeds







class 			a       (      nn.Module     ):



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







  """simple docstring"""






  super().__init__()

  lowerCAmelCase													=   Timesteps(num_channels=2_56							,					flip_sin_to_cos=__snake_case							,					downscale_freq_shift=1    )
  lowerCAmelCase													=   TimestepEmbedding(in_channels=2_56							,					time_embed_dim=__snake_case    )
  lowerCAmelCase													=   LabelEmbedding(__snake_case							,					__snake_case							,					__snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case=None    ):







  """simple docstring"""






  lowerCAmelCase													=   self.time_proj(__snake_case    )
  lowerCAmelCase													=   self.timestep_embedder(timesteps_proj.to(dtype=__snake_case    )    )  # (N, D)

  lowerCAmelCase													=   self.class_embedder(__snake_case    )  # (N, D)

  lowerCAmelCase													=   timesteps_emb + class_labels  # (N, D)

  return conditioning







class 			a       (      nn.Module     ):



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







  """simple docstring"""






  super().__init__()
  lowerCAmelCase													=   nn.LayerNorm(__snake_case    )
  lowerCAmelCase													=   AttentionPooling(__snake_case							,					__snake_case    )
  lowerCAmelCase													=   nn.Linear(__snake_case							,					__snake_case    )
  lowerCAmelCase													=   nn.LayerNorm(__snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.norma(__snake_case    )
  lowerCAmelCase													=   self.pool(__snake_case    )
  lowerCAmelCase													=   self.proj(__snake_case    )
  lowerCAmelCase													=   self.norma(__snake_case    )
  return hidden_states







class 			a       (      nn.Module     ):



 def __init__(    self							,					_snake_case = 7_68							,					_snake_case = 7_68							,					_snake_case = 15_36    ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase													=   nn.Linear(__snake_case							,					__snake_case    )
  lowerCAmelCase													=   nn.LayerNorm(__snake_case    )
  lowerCAmelCase													=   nn.Linear(__snake_case							,					__snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.text_proj(__snake_case    )
  lowerCAmelCase													=   self.text_norm(__snake_case    )

  # image
  lowerCAmelCase													=   self.image_proj(__snake_case    )

  return time_image_embeds + time_text_embeds







class 			a       (      nn.Module     ):



 def __init__(    self							,					_snake_case = 7_68							,					_snake_case = 15_36    ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase													=   nn.Linear(__snake_case							,					__snake_case    )
  lowerCAmelCase													=   nn.LayerNorm(__snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.image_proj(__snake_case    )
  lowerCAmelCase													=   self.image_norm(__snake_case    )
  return time_image_embeds







class 			a       (      nn.Module     ):



 def __init__(    self							,					_snake_case = 7_68							,					_snake_case = 15_36    ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase													=   nn.Linear(__snake_case							,					__snake_case    )
  lowerCAmelCase													=   nn.LayerNorm(__snake_case    )
  lowerCAmelCase													=   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							,					2_56							,					3							,					padding=1							,					stride=2    )							,					nn.SiLU()							,					nn.Convad(2_56							,					4							,					3							,					padding=1    )							,					)



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.image_proj(__snake_case    )
  lowerCAmelCase													=   self.image_norm(__snake_case    )
  lowerCAmelCase													=   self.input_hint_block(__snake_case    )
  return time_image_embeds, hint







class 			a       (      nn.Module     ):



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







  """simple docstring"""






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



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase													=   x.size()

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

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

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

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

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

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

  return a[:, 0, :]  # cls_token












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase    :	Dict						=     logging.get_logger(__name__)

__UpperCamelCase    :	List[Any]						=     {
    '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
}







class 			a       (      a__     ):
 snake_case__								=					'''mpnet'''



 def __init__(    self							,					_snake_case=3_05_27							,					_snake_case=7_68							,					_snake_case=12							,					_snake_case=12							,					_snake_case=30_72							,					_snake_case="gelu"							,					_snake_case=0.1							,					_snake_case=0.1							,					_snake_case=5_12							,					_snake_case=0.02							,					_snake_case=1E-12							,					_snake_case=32							,					_snake_case=1							,					_snake_case=0							,					_snake_case=2							,					**_snake_case							,					):







  """simple docstring"""






  super().__init__(pad_token_id=_snake_case							,					bos_token_id=_snake_case							,					eos_token_id=_snake_case							,					**_snake_case    )

  lowerCAmelCase													=   vocab_size
  lowerCAmelCase													=   hidden_size
  lowerCAmelCase													=   num_hidden_layers
  lowerCAmelCase													=   num_attention_heads
  lowerCAmelCase													=   hidden_act
  lowerCAmelCase													=   intermediate_size
  lowerCAmelCase													=   hidden_dropout_prob
  lowerCAmelCase													=   attention_probs_dropout_prob
  lowerCAmelCase													=   max_position_embeddings
  lowerCAmelCase													=   initializer_range
  lowerCAmelCase													=   layer_norm_eps
  lowerCAmelCase													=   relative_attention_num_buckets





