



'''simple docstring'''





from typing import Any, Dict, Optional

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

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings






@maybe_allow_in_graph
class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	str			,	_a      :	int			,	_a      :	int			,	_a      :	int			,	_a      :	Tuple=0.0			,	_a      :	Optional[int] = None			,	_a      :	str = "geglu"			,	_a      :	Optional[int] = None			,	_a      :	bool = False			,	_a      :	bool = False			,	_a      :	bool = False			,	_a      :	bool = False			,	_a      :	bool = True			,	_a      :	str = "layer_norm"			,	_a      :	bool = False			,	)					->					Dict:
    super().__init__()
    __lowerCamelCase       :       Optional[Any]           = only_cross_attention

    __lowerCamelCase       :       List[str]           = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
    __lowerCamelCase       :       Tuple           = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'

    if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
      raise ValueError(
          f'`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'
          f' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'   )

    # Define 3 blocks. Each block has its own normalization layer.
    # 1. Self-Attn
    if self.use_ada_layer_norm:
      __lowerCamelCase       :       Any           = AdaLayerNorm(_a			,	_a   )
    elif self.use_ada_layer_norm_zero:
      __lowerCamelCase       :       Any           = AdaLayerNormZero(_a			,	_a   )
    else:
      __lowerCamelCase       :       str           = nn.LayerNorm(_a			,	elementwise_affine=_a   )
    __lowerCamelCase       :       Union[str, Any]           = Attention(
        query_dim=_a			,	heads=_a			,	dim_head=_a			,	dropout=_a			,	bias=_a			,	cross_attention_dim=cross_attention_dim if only_cross_attention else None			,	upcast_attention=_a			,	)

    # 2. Cross-Attn
    if cross_attention_dim is not None or double_self_attention:
      # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
      # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
      # the second cross attention block.
      __lowerCamelCase       :       List[str]           = (
          AdaLayerNorm(_a			,	_a   )
          if self.use_ada_layer_norm
          else nn.LayerNorm(_a			,	elementwise_affine=_a   )
      )
      __lowerCamelCase       :       Tuple           = Attention(
          query_dim=_a			,	cross_attention_dim=cross_attention_dim if not double_self_attention else None			,	heads=_a			,	dim_head=_a			,	dropout=_a			,	bias=_a			,	upcast_attention=_a			,	)  # is self-attn if encoder_hidden_states is none
    else:
      __lowerCamelCase       :       int           = None
      __lowerCamelCase       :       Dict           = None

    # 3. Feed-forward
    __lowerCamelCase       :       List[Any]           = nn.LayerNorm(_a			,	elementwise_affine=_a   )
    __lowerCamelCase       :       str           = FeedForward(_a			,	dropout=_a			,	activation_fn=_a			,	final_dropout=_a   )

    # let chunk size default to None
    __lowerCamelCase       :       Union[str, Any]           = None
    __lowerCamelCase       :       List[Any]           = 0




  def 						_lowercase     ( self      :	str			,	_a      :	Optional[int]			,	_a      :	int   )					->					int:
    # Sets chunk feed-forward
    __lowerCamelCase       :       Union[str, Any]           = chunk_size
    __lowerCamelCase       :       Union[str, Any]           = dim




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	torch.FloatTensor			,	_a      :	Optional[torch.FloatTensor] = None			,	_a      :	Optional[torch.FloatTensor] = None			,	_a      :	Optional[torch.FloatTensor] = None			,	_a      :	Optional[torch.LongTensor] = None			,	_a      :	Dict[str, Any] = None			,	_a      :	Optional[torch.LongTensor] = None			,	)					->					List[Any]:
    # Notice that normalization is always applied before the real computation in the following blocks.
    # 1. Self-Attention
    if self.use_ada_layer_norm:
      __lowerCamelCase       :       Any           = self.norma(_a			,	_a   )
    elif self.use_ada_layer_norm_zero:
      __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       str           = self.norma(
          _a			,	_a			,	_a			,	hidden_dtype=hidden_states.dtype   )
    else:
      __lowerCamelCase       :       Union[str, Any]           = self.norma(_a   )

    __lowerCamelCase       :       List[Any]           = cross_attention_kwargs if cross_attention_kwargs is not None else {}

    __lowerCamelCase       :       str           = self.attna(
        _a			,	encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None			,	attention_mask=_a			,	**_a			,	)
    if self.use_ada_layer_norm_zero:
      __lowerCamelCase       :       int           = gate_msa.unsqueeze(1   ) * attn_output
    __lowerCamelCase       :       Dict           = attn_output + hidden_states

    # 2. Cross-Attention
    if self.attna is not None:
      __lowerCamelCase       :       str           = (
          self.norma(_a			,	_a   ) if self.use_ada_layer_norm else self.norma(_a   )
      )

      __lowerCamelCase       :       Optional[int]           = self.attna(
          _a			,	encoder_hidden_states=_a			,	attention_mask=_a			,	**_a			,	)
      __lowerCamelCase       :       Optional[Any]           = attn_output + hidden_states

    # 3. Feed-forward
    __lowerCamelCase       :       str           = self.norma(_a   )

    if self.use_ada_layer_norm_zero:
      __lowerCamelCase       :       Optional[int]           = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

    if self._chunk_size is not None:
      # "feed_forward_chunk_size" can be used to save memory
      if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
        raise ValueError(
            f'`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'   )

      __lowerCamelCase       :       Optional[int]           = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
      __lowerCamelCase       :       str           = torch.cat(
          [self.ff(_a   ) for hid_slice in norm_hidden_states.chunk(_a			,	dim=self._chunk_dim   )]			,	dim=self._chunk_dim			,	)
    else:
      __lowerCamelCase       :       int           = self.ff(_a   )

    if self.use_ada_layer_norm_zero:
      __lowerCamelCase       :       Optional[Any]           = gate_mlp.unsqueeze(1   ) * ff_output

    __lowerCamelCase       :       List[Any]           = ff_output + hidden_states

    return hidden_states






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	Optional[int]			,	_a      :	int			,	_a      :	Optional[int] = None			,	_a      :	int = 4			,	_a      :	float = 0.0			,	_a      :	str = "geglu"			,	_a      :	bool = False			,	)					->					Dict:
    super().__init__()
    __lowerCamelCase       :       List[Any]           = int(dim * mult   )
    __lowerCamelCase       :       str           = dim_out if dim_out is not None else dim

    if activation_fn == "gelu":
      __lowerCamelCase       :       Any           = GELU(_a			,	_a   )
    if activation_fn == "gelu-approximate":
      __lowerCamelCase       :       Any           = GELU(_a			,	_a			,	approximate='tanh'   )
    elif activation_fn == "geglu":
      __lowerCamelCase       :       List[Any]           = GEGLU(_a			,	_a   )
    elif activation_fn == "geglu-approximate":
      __lowerCamelCase       :       Any           = ApproximateGELU(_a			,	_a   )

    __lowerCamelCase       :       int           = nn.ModuleList([]   )
    # project in
    self.net.append(_a   )
    # project dropout
    self.net.append(nn.Dropout(_a   )   )
    # project out
    self.net.append(nn.Linear(_a			,	_a   )   )
    # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
    if final_dropout:
      self.net.append(nn.Dropout(_a   )   )




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Tuple   )					->					Dict:
    for module in self.net:
      __lowerCamelCase       :       List[Any]           = module(_a   )
    return hidden_states






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	Dict			,	_a      :	int			,	_a      :	int			,	_a      :	str = "none"   )					->					Any:
    super().__init__()
    __lowerCamelCase       :       str           = nn.Linear(_a			,	_a   )
    __lowerCamelCase       :       Union[str, Any]           = approximate




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Union[str, Any]   )					->					Union[str, Any]:
    if gate.device.type != "mps":
      return F.gelu(_a			,	approximate=self.approximate   )
    # mps: gelu is not implemented for float16
    return F.gelu(gate.to(dtype=torch.floataa   )			,	approximate=self.approximate   ).to(dtype=gate.dtype   )




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	str   )					->					List[Any]:
    __lowerCamelCase       :       Dict           = self.proj(_a   )
    __lowerCamelCase       :       Dict           = self.gelu(_a   )
    return hidden_states






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	Optional[Any]			,	_a      :	int			,	_a      :	int   )					->					str:
    super().__init__()
    __lowerCamelCase       :       Optional[int]           = nn.Linear(_a			,	dim_out * 2   )




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	int   )					->					Union[str, Any]:
    if gate.device.type != "mps":
      return F.gelu(_a   )
    # mps: gelu is not implemented for float16
    return F.gelu(gate.to(dtype=torch.floataa   )   ).to(dtype=gate.dtype   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	List[Any]   )					->					str:
    __lowerCamelCase       ,__lowerCamelCase       :       Optional[int]           = self.proj(_a   ).chunk(2			,	dim=-1   )
    return hidden_states * self.gelu(_a   )






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	Any			,	_a      :	int			,	_a      :	int   )					->					List[Any]:
    super().__init__()
    __lowerCamelCase       :       int           = nn.Linear(_a			,	_a   )




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	Union[str, Any]   )					->					Optional[int]:
    __lowerCamelCase       :       Dict           = self.proj(_a   )
    return x * torch.sigmoid(1.702 * x   )






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	Optional[int]			,	_a      :	Optional[Any]			,	_a      :	Union[str, Any]   )					->					str:
    super().__init__()
    __lowerCamelCase       :       Optional[int]           = nn.Embedding(_a			,	_a   )
    __lowerCamelCase       :       Dict           = nn.SiLU()
    __lowerCamelCase       :       List[str]           = nn.Linear(_a			,	embedding_dim * 2   )
    __lowerCamelCase       :       List[Any]           = nn.LayerNorm(_a			,	elementwise_affine=_a   )




  def 						_lowercase     ( self      :	int			,	_a      :	Optional[Any]			,	_a      :	Optional[int]   )					->					int:
    __lowerCamelCase       :       str           = self.linear(self.silu(self.emb(_a   )   )   )
    __lowerCamelCase       ,__lowerCamelCase       :       Dict           = torch.chunk(_a			,	2   )
    __lowerCamelCase       :       str           = self.norm(_a   ) * (1 + scale) + shift
    return x






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




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

    __lowerCamelCase       :       Optional[Any]           = CombinedTimestepLabelEmbeddings(_a			,	_a   )

    __lowerCamelCase       :       Optional[Any]           = nn.SiLU()
    __lowerCamelCase       :       int           = nn.Linear(_a			,	6 * embedding_dim			,	bias=_a   )
    __lowerCamelCase       :       str           = nn.LayerNorm(_a			,	elementwise_affine=_a			,	eps=1e-6   )




  def 						_lowercase     ( self      :	Any			,	_a      :	Optional[int]			,	_a      :	Tuple			,	_a      :	Optional[int]			,	_a      :	Tuple=None   )					->					Optional[int]:
    __lowerCamelCase       :       int           = self.linear(self.silu(self.emb(_a			,	_a			,	hidden_dtype=_a   )   )   )
    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       Tuple           = emb.chunk(6			,	dim=1   )
    __lowerCamelCase       :       Union[str, Any]           = self.norm(_a   ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
    return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class 					lowerCamelCase_    (				nn.Module     ):
  """simple docstring"""




  def __init__( self      :	Union[str, Any]			,	_a      :	int			,	_a      :	int			,	_a      :	int			,	_a      :	Optional[str] = None			,	_a      :	float = 1e-5   )					->					List[str]:
    super().__init__()
    __lowerCamelCase       :       Dict           = num_groups
    __lowerCamelCase       :       int           = eps

    if act_fn is None:
      __lowerCamelCase       :       Dict           = None
    else:
      __lowerCamelCase       :       Optional[Any]           = get_activation(_a   )

    __lowerCamelCase       :       Dict           = nn.Linear(_a			,	out_dim * 2   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	Optional[int]			,	_a      :	List[Any]   )					->					Union[str, Any]:
    if self.act:
      __lowerCamelCase       :       Any           = self.act(_a   )
    __lowerCamelCase       :       str           = self.linear(_a   )
    __lowerCamelCase       :       Optional[int]           = emb[:, :, None, None]
    __lowerCamelCase       ,__lowerCamelCase       :       Union[str, Any]           = emb.chunk(2			,	dim=1   )

    __lowerCamelCase       :       Dict           = F.group_norm(_a			,	self.num_groups			,	eps=self.eps   )
    __lowerCamelCase       :       List[Any]           = x * (1 + scale) + shift
    return x











'''simple docstring'''





def        a_     (							_lowerCAmelCase       )      ->      list:
  if n_term == "":
    return []
  __lowerCamelCase       :       list           = []
  for temp in range(int(_lowerCAmelCase       )       ):
    series.append(F'1/{temp + 1}' if series else '1'       )
  return series


if __name__ == "__main__":
   _UpperCamelCase											=						input('Enter the last number (nth term) of the Harmonic Series')
   print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
   print(harmonic_series(nth_term))






