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       :			torch.Tensor					,	__UpperCamelCase       :			int					,	__UpperCamelCase       :			bool = False					,	__UpperCamelCase       :			float = 1					,	__UpperCamelCase       :			float = 1					,	__UpperCamelCase       :			int = 1_00_00					,	)				->   List[str]:







  """simple docstring"""





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

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

  SCREAMING_SNAKE_CASE__     				= torch.exp(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__     				= timesteps[:, None].float() * emb[None, :]

  # scale embeddings
  SCREAMING_SNAKE_CASE__     				= scale * emb

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

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

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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[Any]					,	__UpperCamelCase       :			Any					,	__UpperCamelCase       :			Optional[Any]=False					,	__UpperCamelCase       :			Optional[Any]=0      )				->   Any:







  """simple docstring"""





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

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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Any					,	__UpperCamelCase       :			Dict      )				->   List[str]:







  """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__     				= get_ad_sincos_pos_embed_from_grid(embed_dim // 2					,	grid[0]      )  # (H*W, D/2)
  SCREAMING_SNAKE_CASE__     				= get_ad_sincos_pos_embed_from_grid(embed_dim // 2					,	grid[1]      )  # (H*W, D/2)

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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[str]					,	__UpperCamelCase       :			Tuple      )				->   List[Any]:







  """simple docstring"""





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

  SCREAMING_SNAKE_CASE__     				= np.arange(embed_dim // 2					,	dtype=np.floataa      )
  omega /= embed_dim / 2.0
  SCREAMING_SNAKE_CASE__     				= 1.0 / 1_00_00**omega  # (D/2,)

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

  SCREAMING_SNAKE_CASE__     				= np.sin(__UpperCamelCase      )  # (M, D/2)
  SCREAMING_SNAKE_CASE__     				= np.cos(__UpperCamelCase      )  # (M, D/2)

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







class __snake_case							(		nn.Module						):




   def __init__(       self     :							Dict			,      _lowercase     :							Any=2_24			,      _lowercase     :							Any=2_24			,      _lowercase     :							Any=16			,      _lowercase     :							Dict=3			,      _lowercase     :							Union[str, Any]=7_68			,      _lowercase     :							List[Any]=False			,      _lowercase     :							Optional[int]=True			,      _lowercase     :							str=True			,      ):



     """simple docstring"""






     super().__init__()

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

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

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




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							Optional[Any] ):



     """simple docstring"""






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







class __snake_case							(		nn.Module						):




   def __init__(       self     :							Dict			,      _lowercase     :							int			,      _lowercase     :							int			,      _lowercase     :							str = "silu"			,      _lowercase     :							int = None			,      _lowercase     :							Optional[str] = None			,      _lowercase     :							Union[str, Any]=None			,      ):



     """simple docstring"""






     super().__init__()

     SCREAMING_SNAKE_CASE__     				= nn.Linear(_lowercase			,      _lowercase )

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

     SCREAMING_SNAKE_CASE__     				= get_activation(_lowercase )

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

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




   def       __a		(       self     :							int			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict=None ):



     """simple docstring"""






     if condition is not None:
       SCREAMING_SNAKE_CASE__     				= sample + self.cond_proj(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.linear_a(_lowercase )

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

     SCREAMING_SNAKE_CASE__     				= self.linear_a(_lowercase )

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







class __snake_case							(		nn.Module						):




   def __init__(       self     :							Tuple			,      _lowercase     :							int			,      _lowercase     :							bool			,      _lowercase     :							float ):



     """simple docstring"""






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




   def       __a		(       self     :							List[str]			,      _lowercase     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= get_timestep_embedding(
         _lowercase			,      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     :							List[str]			,      _lowercase     :							int = 2_56			,      _lowercase     :							float = 1.0			,      _lowercase     :							int=True			,      _lowercase     :							List[str]=True			,      _lowercase     :							Dict=False ):



     """simple docstring"""






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

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

       SCREAMING_SNAKE_CASE__     				= self.W




   def       __a		(       self     :							Tuple			,      _lowercase     :							Optional[Any] ):



     """simple docstring"""






     if self.log:
       SCREAMING_SNAKE_CASE__     				= torch.log(_lowercase )

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

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







class __snake_case							(		nn.Module						):




   def __init__(       self     :							Any			,      _lowercase     :							int			,      _lowercase     :							int			,      _lowercase     :							int			,      _lowercase     :							int			,      ):



     """simple docstring"""






     super().__init__()

     SCREAMING_SNAKE_CASE__     				= height
     SCREAMING_SNAKE_CASE__     				= width
     SCREAMING_SNAKE_CASE__     				= num_embed
     SCREAMING_SNAKE_CASE__     				= embed_dim

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




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.emb(_lowercase )

     SCREAMING_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
     SCREAMING_SNAKE_CASE__     				= height_emb.unsqueeze(2 )

     SCREAMING_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
     SCREAMING_SNAKE_CASE__     				= width_emb.unsqueeze(1 )

     SCREAMING_SNAKE_CASE__     				= height_emb + width_emb

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

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

     return emb







class __snake_case							(		nn.Module						):




   def __init__(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							List[Any]			,      _lowercase     :							Optional[int] ):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__     				= dropout_prob > 0
     SCREAMING_SNAKE_CASE__     				= nn.Embedding(num_classes + use_cfg_embedding			,      _lowercase )
     SCREAMING_SNAKE_CASE__     				= num_classes
     SCREAMING_SNAKE_CASE__     				= dropout_prob




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							Dict			,      _lowercase     :							int=None ):



     """simple docstring"""






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




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							torch.LongTensor			,      _lowercase     :							str=None ):



     """simple docstring"""






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







class __snake_case							(		nn.Module						):




   def __init__(       self     :							Optional[int]			,      _lowercase     :							int = 10_24			,      _lowercase     :							int = 7_68			,      _lowercase     :							int = 7_68			,      _lowercase     :							int = 10			,      ):



     """simple docstring"""






     super().__init__()

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




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							torch.FloatTensor			,      _lowercase     :							torch.FloatTensor ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= text_embeds.shape[0]

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

     # text
     SCREAMING_SNAKE_CASE__     				= self.text_proj(_lowercase )

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







class __snake_case							(		nn.Module						):




   def __init__(       self     :							Optional[Any]			,      _lowercase     :							int = 7_68			,      _lowercase     :							int = 7_68			,      _lowercase     :							int = 32			,      ):



     """simple docstring"""






     super().__init__()

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




   def       __a		(       self     :							List[Any]			,      _lowercase     :							torch.FloatTensor ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= image_embeds.shape[0]

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







class __snake_case							(		nn.Module						):




   def __init__(       self     :							Tuple			,      _lowercase     :							Optional[int]			,      _lowercase     :							Dict			,      _lowercase     :							Optional[int]=0.1 ):



     """simple docstring"""






     super().__init__()

     SCREAMING_SNAKE_CASE__     				= Timesteps(num_channels=2_56			,      flip_sin_to_cos=_lowercase			,      downscale_freq_shift=1 )
     SCREAMING_SNAKE_CASE__     				= TimestepEmbedding(in_channels=2_56			,      time_embed_dim=_lowercase )
     SCREAMING_SNAKE_CASE__     				= LabelEmbedding(_lowercase			,      _lowercase			,      _lowercase )




   def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict			,      _lowercase     :							Dict=None ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.time_proj(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.timestep_embedder(timesteps_proj.to(dtype=_lowercase ) )  # (N, D)

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

     SCREAMING_SNAKE_CASE__     				= timesteps_emb + class_labels  # (N, D)

     return conditioning







class __snake_case							(		nn.Module						):




   def __init__(       self     :							List[Any]			,      _lowercase     :							int			,      _lowercase     :							int			,      _lowercase     :							int = 64 ):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__     				= nn.LayerNorm(_lowercase )
     SCREAMING_SNAKE_CASE__     				= AttentionPooling(_lowercase			,      _lowercase )
     SCREAMING_SNAKE_CASE__     				= nn.Linear(_lowercase			,      _lowercase )
     SCREAMING_SNAKE_CASE__     				= nn.LayerNorm(_lowercase )




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.norma(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.pool(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.proj(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.norma(_lowercase )
     return hidden_states







class __snake_case							(		nn.Module						):




   def __init__(       self     :							Optional[Any]			,      _lowercase     :							int = 7_68			,      _lowercase     :							int = 7_68			,      _lowercase     :							int = 15_36 ):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__     				= nn.Linear(_lowercase			,      _lowercase )
     SCREAMING_SNAKE_CASE__     				= nn.LayerNorm(_lowercase )
     SCREAMING_SNAKE_CASE__     				= nn.Linear(_lowercase			,      _lowercase )




   def       __a		(       self     :							int			,      _lowercase     :							torch.FloatTensor			,      _lowercase     :							torch.FloatTensor ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.text_proj(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.text_norm(_lowercase )

     # image
     SCREAMING_SNAKE_CASE__     				= self.image_proj(_lowercase )

     return time_image_embeds + time_text_embeds







class __snake_case							(		nn.Module						):




   def __init__(       self     :							Tuple			,      _lowercase     :							int = 7_68			,      _lowercase     :							int = 15_36 ):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__     				= nn.Linear(_lowercase			,      _lowercase )
     SCREAMING_SNAKE_CASE__     				= nn.LayerNorm(_lowercase )




   def       __a		(       self     :							str			,      _lowercase     :							torch.FloatTensor ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.image_proj(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.image_norm(_lowercase )
     return time_image_embeds







class __snake_case							(		nn.Module						):




   def __init__(       self     :							List[Any]			,      _lowercase     :							int = 7_68			,      _lowercase     :							int = 15_36 ):



     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE__     				= nn.Linear(_lowercase			,      _lowercase )
     SCREAMING_SNAKE_CASE__     				= nn.LayerNorm(_lowercase )
     SCREAMING_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			,      2_56			,      3			,      padding=1			,      stride=2 )			,      nn.SiLU()			,      nn.Convad(2_56			,      4			,      3			,      padding=1 )			,      )




   def       __a		(       self     :							int			,      _lowercase     :							torch.FloatTensor			,      _lowercase     :							torch.FloatTensor ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.image_proj(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.image_norm(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.input_hint_block(_lowercase )
     return time_image_embeds, hint







class __snake_case							(		nn.Module						):




   def __init__(       self     :							Dict			,      _lowercase     :							Any			,      _lowercase     :							Any			,      _lowercase     :							str=None ):



     """simple docstring"""






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




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= x.size()

     def shape(_lowercase     :							Union[str, Any] ):
       # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
       SCREAMING_SNAKE_CASE__     				= x.view(_lowercase			,      -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__     				= x.transpose(1			,      2 )
       # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
       SCREAMING_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)
       SCREAMING_SNAKE_CASE__     				= x.transpose(1			,      2 )
       return x

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

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

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

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

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

     return a[:, 0, :]  # cls_token

# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__lowerCamelCase     :					List[str]				  =						logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer







   class __snake_case							(		lowerCamelCase_						):




      def __init__(       self     :							Tuple			,      _lowercase     :							int = 1_01 ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__     				= length




      def __len__(       self     :							str ):



        """simple docstring"""






        return self.length




      def __getitem__(       self     :							Optional[Any]			,      _lowercase     :							Union[str, Any] ):



        """simple docstring"""






        return i







   class __snake_case							:




      def __call__(       self     :							Dict			,      _lowercase     :							Dict ):



        """simple docstring"""






        return {"input_ids": torch.tensor(_lowercase ), "labels": torch.tensor(_lowercase )}







   class __snake_case							(		nn.Module						):




      def __init__(       self     :							Tuple ):



        """simple docstring"""






        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        SCREAMING_SNAKE_CASE__     				= nn.Linear(1_20			,      80 )




      def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict=None ):



        """simple docstring"""






        if labels is not None:
          return torch.tensor(0.0			,      device=input_ids.device ), input_ids
        else:
          return input_ids



def       __SCREAMING_SNAKE_CASE    (   )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= HfArgumentParser((TrainingArguments,)      )
  sys.argv += ["--output_dir", "./examples"]
  SCREAMING_SNAKE_CASE__     				= parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
      f"""tpu_num_cores: {training_args.tpu_num_cores}"""					,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [10_01, 2_56, 15]:
    SCREAMING_SNAKE_CASE__     				= DummyDataset(__UpperCamelCase      )

    def compute_metrics(__UpperCamelCase       :			EvalPrediction      ) -> Dict:
      SCREAMING_SNAKE_CASE__     				= list(range(len(__UpperCamelCase      )      )      )
      SCREAMING_SNAKE_CASE__     				= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    SCREAMING_SNAKE_CASE__     				= Trainer(
        model=DummyModel()					,	args=__UpperCamelCase					,	data_collator=DummyDataCollator()					,	eval_dataset=__UpperCamelCase					,	compute_metrics=__UpperCamelCase					,	)
    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= 2

    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= None

  logger.info("""🔥 All distributed tests successful"""      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]      )				->   Dict:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()
