


"""simple docstring"""



from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin







@dataclass
class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			torch.FloatTensor







class _lowerCAmelCase							( snake_case_       ,				snake_case_      ):







   @register_to_config
   def __init__(      self		,      UpperCamelCase__ = 32		,      UpperCamelCase__ = 64		,      UpperCamelCase__ = 20		,      UpperCamelCase__ = 768		,      UpperCamelCase__=77		,      UpperCamelCase__=4		,      UpperCamelCase__ = 0.0		,      UpperCamelCase__ = "silu"		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = "linear"		,      UpperCamelCase__ = "prd"		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      )      ->						Optional[Any]:

       '''simple docstring'''




       super().__init__()
       snake_case       : Union[str, Any]    =       num_attention_heads
       snake_case       : int    =       attention_head_dim
       snake_case       : Union[str, Any]    =       num_attention_heads * attention_head_dim
       snake_case       : Tuple    =       additional_embeddings

       snake_case       : Tuple    =       time_embed_dim or inner_dim
       snake_case       : str    =       embedding_proj_dim or embedding_dim
       snake_case       : Any    =       clip_embed_dim or embedding_dim

       snake_case       : List[str]    =       Timesteps(UpperCamelCase__		,      UpperCamelCase__		,      0		)
       snake_case       : Union[str, Any]    =       TimestepEmbedding(UpperCamelCase__		,      UpperCamelCase__		,      out_dim=UpperCamelCase__		,      act_fn=UpperCamelCase__		)

       snake_case       : Optional[int]    =       nn.Linear(UpperCamelCase__		,      UpperCamelCase__		)

       if embedding_proj_norm_type is None:
           snake_case       : str    =       None
       elif embedding_proj_norm_type == "layer":
           snake_case       : List[str]    =       nn.LayerNorm(UpperCamelCase__		)
       else:
           raise ValueError(F'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'		)

       snake_case       : Optional[Any]    =       nn.Linear(UpperCamelCase__		,      UpperCamelCase__		)

       if encoder_hid_proj_type is None:
           snake_case       : Any    =       None
       elif encoder_hid_proj_type == "linear":
           snake_case       : Tuple    =       nn.Linear(UpperCamelCase__		,      UpperCamelCase__		)
       else:
           raise ValueError(F'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'		)

       snake_case       : Optional[int]    =       nn.Parameter(torch.zeros(1		,      num_embeddings + additional_embeddings		,      UpperCamelCase__		)		)

       if added_emb_type == "prd":
           snake_case       : Tuple    =       nn.Parameter(torch.zeros(1		,      1		,      UpperCamelCase__		)		)
       elif added_emb_type is None:
           snake_case       : List[str]    =       None
       else:
           raise ValueError(
               F'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'		)

       snake_case       : Tuple    =       nn.ModuleList(
           [
               BasicTransformerBlock(
                   UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      dropout=UpperCamelCase__		,      activation_fn="gelu"		,      attention_bias=UpperCamelCase__		,      )
               for d in range(UpperCamelCase__		)
           ]		)

       if norm_in_type == "layer":
           snake_case       : Dict    =       nn.LayerNorm(UpperCamelCase__		)
       elif norm_in_type is None:
           snake_case       : Dict    =       None
       else:
           raise ValueError(F'Unsupported norm_in_type: {norm_in_type}.'		)

       snake_case       : Tuple    =       nn.LayerNorm(UpperCamelCase__		)

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

       snake_case       : Any    =       torch.full(
           [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]		,      -10000.0		)
       causal_attention_mask.triu_(1		)
       snake_case       : Tuple    =       causal_attention_mask[None, ...]
       self.register_buffer("causal_attention_mask"		,      UpperCamelCase__		,      persistent=UpperCamelCase__		)

       snake_case       : Optional[int]    =       nn.Parameter(torch.zeros(1		,      UpperCamelCase__		)		)
       snake_case       : List[Any]    =       nn.Parameter(torch.zeros(1		,      UpperCamelCase__		)		)







   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   def 					lowerCamelCase						(      self		)      ->						Dict[str, AttentionProcessor]:

       '''simple docstring'''




       snake_case       : List[Any]    =       {}

       def fn_recursive_add_processors(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		):
           if hasattr(UpperCamelCase__		,      "set_processor"		):
               snake_case       : str    =       module.processor

           for sub_name, child in module.named_children():
               fn_recursive_add_processors(F'{name}.{sub_name}'		,      UpperCamelCase__		,      UpperCamelCase__		)

           return processors

       for name, module in self.named_children():
           fn_recursive_add_processors(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)

       return processors







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       len(self.attn_processors.keys()		)

       if isinstance(UpperCamelCase__		,      UpperCamelCase__		) and len(UpperCamelCase__		) != count:
           raise ValueError(
               F'A dict of processors was passed, but the number of processors {len(UpperCamelCase__		)} does not match the'
               F' number of attention layers: {count}. Please make sure to pass {count} processor classes.'		)

       def fn_recursive_attn_processor(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		):
           if hasattr(UpperCamelCase__		,      "set_processor"		):
               if not isinstance(UpperCamelCase__		,      UpperCamelCase__		):
                   module.set_processor(UpperCamelCase__		)
               else:
                   module.set_processor(processor.pop(F'{name}.processor'		)		)

           for sub_name, child in module.named_children():
               fn_recursive_attn_processor(F'{name}.{sub_name}'		,      UpperCamelCase__		,      UpperCamelCase__		)

       for name, module in self.named_children():
           fn_recursive_attn_processor(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       self.set_attn_processor(AttnProcessor()		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = True		,      )      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : Tuple    =       hidden_states.shape[0]

       snake_case       : Dict    =       timestep
       if not torch.is_tensor(UpperCamelCase__		):
           snake_case       : Tuple    =       torch.tensor([timesteps]		,      dtype=torch.long		,      device=hidden_states.device		)
       elif torch.is_tensor(UpperCamelCase__		) and len(timesteps.shape		) == 0:
           snake_case       : int    =       timesteps[None].to(hidden_states.device		)

       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       snake_case       : str    =       timesteps * torch.ones(UpperCamelCase__		,      dtype=timesteps.dtype		,      device=timesteps.device		)

       snake_case       : Dict    =       self.time_proj(UpperCamelCase__		)

       # timesteps does not contain any weights and will always return f32 tensors
       # but time_embedding might be fp16, so we need to cast here.
       snake_case       : Optional[Any]    =       timesteps_projected.to(dtype=self.dtype		)
       snake_case       : List[Any]    =       self.time_embedding(UpperCamelCase__		)

       if self.embedding_proj_norm is not None:
           snake_case       : str    =       self.embedding_proj_norm(UpperCamelCase__		)

       snake_case       : Optional[Any]    =       self.embedding_proj(UpperCamelCase__		)
       if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
           snake_case       : Any    =       self.encoder_hidden_states_proj(UpperCamelCase__		)
       elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
           raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"		)

       snake_case       : Union[str, Any]    =       self.proj_in(UpperCamelCase__		)

       snake_case       : Any    =       self.positional_embedding.to(hidden_states.dtype		)

       snake_case       : str    =       []
       snake_case       : Optional[int]    =       0

       if encoder_hidden_states is not None:
           additional_embeds.append(UpperCamelCase__		)
           additional_embeddings_len += encoder_hidden_states.shape[1]

       if len(proj_embeddings.shape		) == 2:
           snake_case       : Tuple    =       proj_embeddings[:, None, :]

       if len(hidden_states.shape		) == 2:
           snake_case       : Any    =       hidden_states[:, None, :]

       snake_case       : Any    =       additional_embeds + [
           proj_embeddings,
           time_embeddings[:, None, :],
           hidden_states,
       ]

       if self.prd_embedding is not None:
           snake_case       : int    =       self.prd_embedding.to(hidden_states.dtype		).expand(UpperCamelCase__		,      -1		,      -1		)
           additional_embeds.append(UpperCamelCase__		)

       snake_case       : List[str]    =       torch.cat(
           UpperCamelCase__		,      dim=1		,      )

       # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
       snake_case       : str    =       additional_embeddings_len + proj_embeddings.shape[1] + 1
       if positional_embeddings.shape[1] < hidden_states.shape[1]:
           snake_case       : List[Any]    =       F.pad(
               UpperCamelCase__		,      (
                   0,
                   0,
                   additional_embeddings_len,
                   self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
               )		,      value=0.0		,      )

       snake_case       : List[Any]    =       hidden_states + positional_embeddings

       if attention_mask is not None:
           snake_case       : Optional[Any]    =       (1 - attention_mask.to(hidden_states.dtype		)) * -10000.0
           snake_case       : List[str]    =       F.pad(UpperCamelCase__		,      (0, self.additional_embeddings)		,      value=0.0		)
           snake_case       : Any    =       (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype		)
           snake_case       : Union[str, Any]    =       attention_mask.repeat_interleave(self.config.num_attention_heads		,      dim=0		)

       if self.norm_in is not None:
           snake_case       : Union[str, Any]    =       self.norm_in(UpperCamelCase__		)

       for block in self.transformer_blocks:
           snake_case       : Any    =       block(UpperCamelCase__		,      attention_mask=UpperCamelCase__		)

       snake_case       : int    =       self.norm_out(UpperCamelCase__		)

       if self.prd_embedding is not None:
           snake_case       : List[str]    =       hidden_states[:, -1]
       else:
           snake_case       : Any    =       hidden_states[:, additional_embeddings_len:]

       snake_case       : List[Any]    =       self.proj_to_clip_embeddings(UpperCamelCase__		)

       if not return_dict:
           return (predicted_image_embedding,)

       return PriorTransformerOutput(predicted_image_embedding=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       (prior_latents * self.clip_std) + self.clip_mean
       return prior_latents









"""simple docstring"""



import math
from collections.abc import Iterator
from itertools import takewhile







def     __lowerCAmelCase		(		lowercase    :    int					)      ->		bool:


    """simple docstring"""





    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5		,				int(math.sqrt(lowercase					) + 1					)		,				6					):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True







def     __lowerCAmelCase		(		)      ->		Iterator[int]:


    """simple docstring"""





    snake_case       : Optional[int]    =       2
    while True:
        if is_prime(lowercase					):
            yield num
        num += 1







def     __lowerCAmelCase		(		lowercase    :    int = 200_0000					)      ->		int:


    """simple docstring"""





    return sum(takewhile(lambda lowercase					: x < n		,				prime_generator()					)					)


if __name__ == "__main__":
  print(F'''{solution() = }''')





