


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  SCREAMING_SNAKE_CASE__  (				lowercase__						,    lowercase__			):
      @register_to_config
      def __init__(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       int = 1_2_8     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       float = 2000.0     ,							SCREAMING_SNAKE_CASE__				:       int = 7_6_8     ,							SCREAMING_SNAKE_CASE__				:       int = 1_2     ,							SCREAMING_SNAKE_CASE__				:       int = 1_2     ,							SCREAMING_SNAKE_CASE__				:       int = 6_4     ,							SCREAMING_SNAKE_CASE__				:       int = 2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       float = 0.1     ,							)		->			str:
             super().__init__()

             a_					:		List[Any]							=  nn.Sequential(
                 nn.Linear(A_     ,							d_model * 4     ,							bias=A_     )     ,							nn.SiLU()     ,							nn.Linear(d_model * 4     ,							d_model * 4     ,							bias=A_     )     ,							nn.SiLU()     ,							)

             a_					:		List[Any]							=  nn.Embedding(A_     ,							A_     )
             a_					:		Tuple							=  False

             a_					:		str							=  nn.Linear(A_     ,							A_     ,							bias=A_     )

             a_					:		Any							=  nn.Dropout(p=A_     )

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

             a_					:		str							=  TaLayerNorm(A_     )

             a_					:		List[Any]							=  nn.Dropout(p=A_     )
             a_					:		Optional[Any]							=  nn.Linear(A_     ,							A_     ,							bias=A_     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Dict     )		->			Optional[Any]:
             a_					:		Union[str, Any]							=  torch.mul(query_input.unsqueeze(-1     )     ,							key_input.unsqueeze(-2     )     )
             return mask.unsqueeze(-3     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			Optional[int]:
             a_							,		a_							,		a_					:		List[str]							=  decoder_input_tokens.shape
             assert decoder_noise_time.shape == (batch,)

             # decoder_noise_time is in [0, 1), so rescale to expected timing range.
             a_					:		Tuple							=  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_					:		Optional[int]							=  self.conditioning_emb(A_     ).unsqueeze(1     )

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

             a_					:		Union[str, 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_					:		int							=  torch.broadcast_to(
                 torch.arange(A_     ,							device=decoder_input_tokens.device     )     ,							(batch, seq_length)     ,							)

             a_					:		int							=  self.position_encoding(A_     )

             a_					:		Any							=  self.continuous_inputs_projection(A_     )
             inputs += position_encodings
             a_					:		Tuple							=  self.dropout(A_     )

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

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

             # cross attend style: concat encodings
             a_					:		Tuple							=  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_					:		Dict							=  lyr(
                        A_     ,							conditioning_emb=A_     ,							encoder_hidden_states=A_     ,							encoder_attention_mask=A_     ,							)[0]

             a_					:		Tuple							=  self.decoder_norm(A_     )
             a_					:		int							=  self.post_dropout(A_     )

             a_					:		Any							=  self.spec_out(A_     )
             return spec_out
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1E-6     )		->			Dict:
             super().__init__()
             a_					:		Optional[int]							=  nn.ModuleList()

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

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

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

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Any=None     ,							SCREAMING_SNAKE_CASE__				:       Dict=None     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=None     ,							SCREAMING_SNAKE_CASE__				:       Dict=None     ,							SCREAMING_SNAKE_CASE__				:       int=None     ,							)		->			Optional[int]:
             a_					:		Tuple							=  self.layer[0](
                 A_     ,							conditioning_emb=A_     ,							attention_mask=A_     ,							)

             if encoder_hidden_states is not None:
                    a_					:		Union[str, Any]							=  torch.where(encoder_attention_mask > 0     ,							0     ,							-1E10     ).to(
                        encoder_hidden_states.dtype     )

                    a_					:		Optional[int]							=  self.layer[1](
                        A_     ,							key_value_states=A_     ,							attention_mask=A_     ,							)

             # Apply Film Conditional Feed Forward layer
             a_					:		Union[str, Any]							=  self.layer[-1](A_     ,							A_     )

             return (hidden_states,)
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Dict     )		->			int:
             super().__init__()
             a_					:		Dict							=  TaLayerNorm(A_     )
             a_					:		List[str]							=  TaFiLMLayer(in_features=d_model * 4     ,							out_features=A_     )
             a_					:		int							=  Attention(query_dim=A_     ,							heads=A_     ,							dim_head=A_     ,							out_bias=A_     ,							scale_qk=A_     )
             a_					:		Dict							=  nn.Dropout(A_     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Dict=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							)		->			Dict:
             a_					:		Union[str, Any]							=  self.layer_norm(A_     )

             if conditioning_emb is not None:
                    a_					:		List[str]							=  self.FiLMLayer(A_     ,							A_     )

             # Self-attention block
             a_					:		Optional[int]							=  self.attention(A_     )

             a_					:		List[str]							=  hidden_states + self.dropout(A_     )

             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			int:
             super().__init__()
             a_					:		Any							=  Attention(query_dim=A_     ,							heads=A_     ,							dim_head=A_     ,							out_bias=A_     ,							scale_qk=A_     )
             a_					:		Dict							=  TaLayerNorm(A_     ,							eps=A_     )
             a_					:		List[Any]							=  nn.Dropout(A_     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=None     ,							SCREAMING_SNAKE_CASE__				:       Any=None     ,							)		->			Optional[Any]:
             a_					:		List[Any]							=  self.layer_norm(A_     )
             a_					:		Dict							=  self.attention(
                 A_     ,							encoder_hidden_states=A_     ,							attention_mask=attention_mask.squeeze(1     )     ,							)
             a_					:		List[Any]							=  hidden_states + self.dropout(A_     )
             return layer_output
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[str]     )		->			Optional[Any]:
             super().__init__()
             a_					:		str							=  TaDenseGatedActDense(d_model=A_     ,							d_ff=A_     ,							dropout_rate=A_     )
             a_					:		Tuple							=  TaFiLMLayer(in_features=d_model * 4     ,							out_features=A_     )
             a_					:		Union[str, Any]							=  TaLayerNorm(A_     ,							eps=A_     )
             a_					:		List[str]							=  nn.Dropout(A_     )

      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     )		->			Optional[Any]:
             a_					:		Dict							=  self.layer_norm(A_     )
             if conditioning_emb is not None:
                    a_					:		str							=  self.film(A_     ,							A_     )

             a_					:		str							=  self.DenseReluDense(A_     )
             a_					:		Union[str, Any]							=  hidden_states + self.dropout(A_     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Tuple:
             super().__init__()
             a_					:		int							=  nn.Linear(A_     ,							A_     ,							bias=A_     )
             a_					:		Tuple							=  nn.Linear(A_     ,							A_     ,							bias=A_     )
             a_					:		Any							=  nn.Linear(A_     ,							A_     ,							bias=A_     )
             a_					:		Optional[int]							=  nn.Dropout(A_     )
             a_					:		List[str]							=  NewGELUActivation()

      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			str:
             a_					:		Any							=  self.act(self.wi_a(A_     )     )
             a_					:		str							=  self.wi_a(A_     )
             a_					:		Tuple							=  hidden_gelu * hidden_linear
             a_					:		List[Any]							=  self.dropout(A_     )

             a_					:		Dict							=  self.wo(A_     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[str]=1E-6     )		->			Optional[int]:
             super().__init__()
             a_					:		Union[str, Any]							=  nn.Parameter(torch.ones(A_     )     )
             a_					:		Tuple							=  eps

      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     )		->			Optional[int]:
             a_					:		str							=  hidden_states.to(torch.floataa     ).pow(2     ).mean(-1     ,							keepdim=A_     )
             a_					:		List[Any]							=  hidden_states * torch.rsqrt(variance + self.variance_epsilon     )

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

             return self.weight * hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       torch.Tensor     )		->			torch.Tensor:
             return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi     ) * (input + 0.044715 * torch.pow(A_     ,							3.0     ))     ))
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       List[str]     )		->			Dict:
             super().__init__()
             a_					:		Tuple							=  nn.Linear(A_     ,							out_features * 2     ,							bias=A_     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     )		->			Any:
             a_					:		Tuple							=  self.scale_bias(A_     )
             a_							,		a_					:		List[Any]							=  torch.chunk(A_     ,							2     ,							-1     )
             a_					:		Union[str, Any]							=  x * (1 + scale) + shift
             return x








lowerCamelCase				:		Tuple     =   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]	,							lowercase	:       int	,							lowercase	:       Optional[Any]	,							lowercase	:       List[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    [False] * len(lowercase     )
      lowerCamelCase_		     =    [s]
      lowerCamelCase_		     =    True

      while queue:
            lowerCamelCase_		     =    queue.pop(0     )
            for ind in range(len(graph[u]     )     ):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(lowercase     )
                        lowerCamelCase_		     =    True
                        lowerCamelCase_		     =    u

      return visited[t]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]	,							lowercase	:       Tuple	,							lowercase	:       Tuple     ):


      '''simple docstring'''



      lowerCamelCase_		     =    [-1] * (len(lowercase     ))
      lowerCamelCase_		     =    0
      lowerCamelCase_		     =    []
      lowerCamelCase_		     =    [i[:] for i in graph]  # Record original cut, copy.
      while bfs(lowercase	,							lowercase	,							lowercase	,							lowercase     ):
            lowerCamelCase_		     =    float('Inf'     )
            lowerCamelCase_		     =    sink

            while s != source:
                  # Find the minimum value in select path
                  lowerCamelCase_		     =    min(lowercase	,							graph[parent[s]][s]     )
                  lowerCamelCase_		     =    parent[s]

            max_flow += path_flow
            lowerCamelCase_		     =    sink

            while v != source:
                  lowerCamelCase_		     =    parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  lowerCamelCase_		     =    parent[v]

      for i in range(len(lowercase     )     ):
            for j in range(len(graph[0]     )     ):
                  if graph[i][j] == 0 and temp[i][j] > 0:
                        res.append((i, j)     )

      return res


if __name__ == "__main__":
 print(mincut(test_graph, source=0, sink=5))



