





import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class 			A__   (							__UpperCAmelCase			,  __UpperCAmelCase     ):




   """simple docstring"""



   @register_to_config
   def __init__( self ,  lowercase = 128 ,  lowercase = 256 ,  lowercase = 20_00.0 ,  lowercase = 768 ,  lowercase = 12 ,  lowercase = 12 ,  lowercase = 64 ,  lowercase = 2048 ,  lowercase = 0.1 ,  )      ->						Optional[Any]:







          '''simple docstring'''

          super().__init__()

          a__						:     Any			=    nn.Sequential(
              nn.Linear(lowercase ,  d_model * 4 ,  bias=lowercase) ,  nn.SiLU() ,  nn.Linear(d_model * 4 ,  d_model * 4 ,  bias=lowercase) ,  nn.SiLU() ,  )

          a__						:     Union[str, Any]			=    nn.Embedding(lowercase ,  lowercase)
          a__						:     Any			=    False

          a__						:     Dict			=    nn.Linear(lowercase ,  lowercase ,  bias=lowercase)

          a__						:     Any			=    nn.Dropout(p=lowercase)

          a__						:     Union[str, Any]			=    nn.ModuleList()
          for lyr_num in range(lowercase):
                 # FiLM conditional T5 decoder
                 a__						:     Any			=    DecoderLayer(d_model=lowercase ,  d_kv=lowercase ,  num_heads=lowercase ,  d_ff=lowercase ,  dropout_rate=lowercase)
                 self.decoders.append(lowercase)

          a__						:     List[str]			=    TaLayerNorm(lowercase)

          a__						:     int			=    nn.Dropout(p=lowercase)
          a__						:     Optional[int]			=    nn.Linear(lowercase ,  lowercase ,  bias=lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						int:







          '''simple docstring'''

          a__						:     Dict			=    torch.mul(query_input.unsqueeze(-1) ,  key_input.unsqueeze(-2))
          return mask.unsqueeze(-3)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          a__			,							a__			,							a__						:     Optional[int]			=    decoder_input_tokens.shape
          assert decoder_noise_time.shape == (batch,)

          # decoder_noise_time is in [0, 1), so rescale to expected timing range.
          a__						:     Any			=    get_timestep_embedding(
              decoder_noise_time * self.config.max_decoder_noise_time ,  embedding_dim=self.config.d_model ,  max_period=self.config.max_decoder_noise_time ,  ).to(dtype=self.dtype)

          a__						:     Any			=    self.conditioning_emb(lowercase).unsqueeze(1)

          assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

          a__						:     Optional[Any]			=    decoder_input_tokens.shape[1]

          # If we want to use relative positions for audio context, we can just offset
          # this sequence by the length of encodings_and_masks.
          a__						:     Tuple			=    torch.broadcast_to(
              torch.arange(lowercase ,  device=decoder_input_tokens.device) ,  (batch, seq_length) ,  )

          a__						:     Tuple			=    self.position_encoding(lowercase)

          a__						:     Optional[Any]			=    self.continuous_inputs_projection(lowercase)
          inputs += position_encodings
          a__						:     Optional[Any]			=    self.dropout(lowercase)

          # decoder: No padding present.
          a__						:     Union[str, Any]			=    torch.ones(
              decoder_input_tokens.shape[:2] ,  device=decoder_input_tokens.device ,  dtype=inputs.dtype)

          # Translate encoding masks to encoder-decoder masks.
          a__						:     Union[str, Any]			=    [(x, self.encoder_decoder_mask(lowercase ,  lowercase)) for x, y in encodings_and_masks]

          # cross attend style: concat encodings
          a__						:     Dict			=    torch.cat([x[0] for x in encodings_and_encdec_masks] ,  dim=1)
          a__						:     Optional[Any]			=    torch.cat([x[1] for x in encodings_and_encdec_masks] ,  dim=-1)

          for lyr in self.decoders:
                 a__						:     List[str]			=    lyr(
                     lowercase ,  conditioning_emb=lowercase ,  encoder_hidden_states=lowercase ,  encoder_attention_mask=lowercase ,  )[0]

          a__						:     Dict			=    self.decoder_norm(lowercase)
          a__						:     List[str]			=    self.post_dropout(lowercase)

          a__						:     List[str]			=    self.spec_out(lowercase)
          return spec_out





class 			A__   (							nn.Module     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase=1e-6)      ->						int:







          '''simple docstring'''

          super().__init__()
          a__						:     Tuple			=    nn.ModuleList()

          # cond self attention: layer 0
          self.layer.append(
              TaLayerSelfAttentionCond(d_model=lowercase ,  d_kv=lowercase ,  num_heads=lowercase ,  dropout_rate=lowercase))

          # cross attention: layer 1
          self.layer.append(
              TaLayerCrossAttention(
                  d_model=lowercase ,  d_kv=lowercase ,  num_heads=lowercase ,  dropout_rate=lowercase ,  layer_norm_epsilon=lowercase ,  ))

          # Film Cond MLP + dropout: last layer
          self.layer.append(
              TaLayerFFCond(d_model=lowercase ,  d_ff=lowercase ,  dropout_rate=lowercase ,  layer_norm_epsilon=lowercase))

   def 							__lowercase		( self ,  lowercase ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  )      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Dict			=    self.layer[0](
              lowercase ,  conditioning_emb=lowercase ,  attention_mask=lowercase ,  )

          if encoder_hidden_states is not None:
                 a__						:     Tuple			=    torch.where(encoder_attention_mask > 0 ,  0 ,  -1e10).to(
                     encoder_hidden_states.dtype)

                 a__						:     Dict			=    self.layer[1](
                     lowercase ,  key_value_states=lowercase ,  attention_mask=lowercase ,  )

          # Apply Film Conditional Feed Forward layer
          a__						:     Optional[int]			=    self.layer[-1](lowercase ,  lowercase)

          return (hidden_states,)





class 			A__   (							nn.Module     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

          super().__init__()
          a__						:     int			=    TaLayerNorm(lowercase)
          a__						:     Union[str, Any]			=    TaFiLMLayer(in_features=d_model * 4 ,  out_features=lowercase)
          a__						:     Dict			=    Attention(query_dim=lowercase ,  heads=lowercase ,  dim_head=lowercase ,  out_bias=lowercase ,  scale_qk=lowercase)
          a__						:     int			=    nn.Dropout(lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase=None ,  lowercase=None ,  )      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.layer_norm(lowercase)

          if conditioning_emb is not None:
                 a__						:     str			=    self.FiLMLayer(lowercase ,  lowercase)

          # Self-attention block
          a__						:     int			=    self.attention(lowercase)

          a__						:     List[Any]			=    hidden_states + self.dropout(lowercase)

          return hidden_states





class 			A__   (							nn.Module     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						str:







          '''simple docstring'''

          super().__init__()
          a__						:     List[Any]			=    Attention(query_dim=lowercase ,  heads=lowercase ,  dim_head=lowercase ,  out_bias=lowercase ,  scale_qk=lowercase)
          a__						:     Union[str, Any]			=    TaLayerNorm(lowercase ,  eps=lowercase)
          a__						:     Optional[int]			=    nn.Dropout(lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase=None ,  lowercase=None ,  )      ->						int:







          '''simple docstring'''

          a__						:     Tuple			=    self.layer_norm(lowercase)
          a__						:     List[str]			=    self.attention(
              lowercase ,  encoder_hidden_states=lowercase ,  attention_mask=attention_mask.squeeze(1) ,  )
          a__						:     Union[str, Any]			=    hidden_states + self.dropout(lowercase)
          return layer_output





class 			A__   (							nn.Module     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						str:







          '''simple docstring'''

          super().__init__()
          a__						:     List[str]			=    TaDenseGatedActDense(d_model=lowercase ,  d_ff=lowercase ,  dropout_rate=lowercase)
          a__						:     Union[str, Any]			=    TaFiLMLayer(in_features=d_model * 4 ,  out_features=lowercase)
          a__						:     Union[str, Any]			=    TaLayerNorm(lowercase ,  eps=lowercase)
          a__						:     Tuple			=    nn.Dropout(lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase=None)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.layer_norm(lowercase)
          if conditioning_emb is not None:
                 a__						:     Optional[Any]			=    self.film(lowercase ,  lowercase)

          a__						:     int			=    self.DenseReluDense(lowercase)
          a__						:     List[str]			=    hidden_states + self.dropout(lowercase)
          return hidden_states





class 			A__   (							nn.Module     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          super().__init__()
          a__						:     Optional[int]			=    nn.Linear(lowercase ,  lowercase ,  bias=lowercase)
          a__						:     Union[str, Any]			=    nn.Linear(lowercase ,  lowercase ,  bias=lowercase)
          a__						:     Union[str, Any]			=    nn.Linear(lowercase ,  lowercase ,  bias=lowercase)
          a__						:     Optional[int]			=    nn.Dropout(lowercase)
          a__						:     Tuple			=    NewGELUActivation()

   def 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.act(self.wi_a(lowercase))
          a__						:     str			=    self.wi_a(lowercase)
          a__						:     List[Any]			=    hidden_gelu * hidden_linear
          a__						:     Tuple			=    self.dropout(lowercase)

          a__						:     int			=    self.wo(lowercase)
          return hidden_states





class 			A__   (							nn.Module     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase=1e-6)      ->						str:







          '''simple docstring'''

          super().__init__()
          a__						:     Tuple			=    nn.Parameter(torch.ones(lowercase))
          a__						:     Union[str, Any]			=    eps

   def 							__lowercase		( self ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          a__						:     int			=    hidden_states.to(torch.floataa).pow(2).mean(-1 ,  keepdim=lowercase)
          a__						:     int			=    hidden_states * torch.rsqrt(variance + self.variance_epsilon)

          # convert into half-precision if necessary
          if self.weight.dtype in [torch.floataa, torch.bfloataa]:
                 a__						:     Tuple			=    hidden_states.to(self.weight.dtype)

          return self.weight * hidden_states





class 			A__   (							nn.Module     ):




   """simple docstring"""



   def 							__lowercase		( self ,  lowercase)      ->						torch.Tensor:







          '''simple docstring'''

          return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.04_47_15 * torch.pow(lowercase ,  3.0))))





class 			A__   (							nn.Module     ):




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          super().__init__()
          a__						:     Tuple			=    nn.Linear(lowercase ,  out_features * 2 ,  bias=lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						Tuple:







          '''simple docstring'''

          a__						:     List[str]			=    self.scale_bias(lowercase)
          a__			,							a__						:     Any			=    torch.chunk(lowercase ,  2 ,  -1)
          a__						:     Union[str, Any]			=    x * (1 + scale) + shift
          return x









def A_     (		A__    )  ->		List[str]:  # noqa: E741
       a__						:     Dict			=    len(A__    )
       a__						:     str			=    0
       a__						:     Any			=    [0] * n
       a__						:     int			=    [False] * n
       a__						:     Optional[Any]			=    [False] * n

       def dfs(A__		,				A__		,				A__		,				A__    ):
              if parent == root:
                     out_edge_count += 1
              a__						:     Union[str, Any]			=    True
              a__						:     Optional[Any]			=    at

              for to in l[at]:
                     if to == parent:
                            pass
                     elif not visited[to]:
                            a__						:     List[Any]			=    dfs(A__		,				A__		,				A__		,				A__    )
                            a__						:     Dict			=    min(low[at]		,				low[to]    )

                            # AP found via bridge
                            if at < low[to]:
                                   a__						:     Dict			=    True
                            # AP found via cycle
                            if at == low[to]:
                                   a__						:     List[Any]			=    True
                     else:
                            a__						:     Optional[int]			=    min(low[at]		,				A__    )
              return out_edge_count

       for i in range(A__    ):
              if not visited[i]:
                     a__						:     Tuple			=    0
                     a__						:     Any			=    dfs(A__		,				A__		,				-1		,				A__    )
                     a__						:     List[Any]			=    out_edge_count > 1

       for x in range(len(A__    )    ):
              if is_art[x] is True:
                     print(A__    )


# Adjacency list of graph
lowercase				:	List[Any]      						=  {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)


