
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCamelCase__    :		int          =   logging.get_logger(__name__)

lowerCamelCase__    :		Optional[int]          =   {
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			'gpt_neo'
 __lowerCAmelCase  :     Optional[int]        =			['past_key_values']
 __lowerCAmelCase  :     str        =			{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=5_02_57					,  SCREAMING_SNAKE_CASE_=20_48					,  SCREAMING_SNAKE_CASE_=20_48					,  SCREAMING_SNAKE_CASE_=24					,  SCREAMING_SNAKE_CASE_=[[["global", "local"], 12]]					,  SCREAMING_SNAKE_CASE_=16					,  SCREAMING_SNAKE_CASE_=None					,  SCREAMING_SNAKE_CASE_=2_56					,  SCREAMING_SNAKE_CASE_="gelu_new"					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=1E-5					,  SCREAMING_SNAKE_CASE_=0.0_2					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=5_02_56					,  SCREAMING_SNAKE_CASE_=5_02_56					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      vocab_size
   lowercase__  :   Optional[Any]    		=      max_position_embeddings
   lowercase__  :   Union[str, Any]    		=      hidden_size
   lowercase__  :   Union[str, Any]    		=      num_layers
   lowercase__  :   Optional[Any]    		=      num_heads
   lowercase__  :   Optional[int]    		=      intermediate_size
   lowercase__  :   List[Any]    		=      window_size
   lowercase__  :   Union[str, Any]    		=      activation_function
   lowercase__  :   int    		=      resid_dropout
   lowercase__  :   Union[str, Any]    		=      embed_dropout
   lowercase__  :   str    		=      attention_dropout
   lowercase__  :   str    		=      classifier_dropout
   lowercase__  :   Tuple    		=      layer_norm_epsilon
   lowercase__  :   Optional[int]    		=      initializer_range
   lowercase__  :   Optional[Any]    		=      use_cache

   lowercase__  :   Tuple    		=      bos_token_id
   lowercase__  :   Dict    		=      eos_token_id

   lowercase__  :   Dict    		=      attention_types
   lowercase__  :   Union[str, Any]    		=      self.expand_attention_types_params(SCREAMING_SNAKE_CASE_)

   if len(self.attention_layers) != self.num_layers:
     raise ValueError(
         """Configuration for convolutional module is incorrect. """
         """It is required that `len(config.attention_layers)` == `config.num_layers` """
         f'but is `len(config.attention_layers) = {len(self.attention_layers)}`, '
         f'`config.num_layers = {self.num_layers}`. '
         """`config.attention_layers` is prepared using `config.attention_types`. """
         """Please verify the value of `config.attention_types` argument.""")

   super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_					,  eos_token_id=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)



 @staticmethod
 def      lowercase__     (					SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   str    		=      []
   for item in attention_types:
     for _ in range(item[1]):
       attentions.extend(item[0])
   return attentions

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	Any:


  '''simple docstring'''






  import torch

  lowercase__  :   str    		=      input.size()
  lowercase__  :   int    		=      len(lowercase_   )
  lowercase__  :   Dict    		=      shape[dimension]

  lowercase__  :   Union[str, Any]    		=      torch.arange(0 , lowercase_ , lowercase_   )
  lowercase__  :   Optional[Any]    		=      torch.div(sizedim - size , lowercase_ , rounding_mode="""floor"""   ) + 1
  lowercase__  :   Optional[Any]    		=      torch.arange(lowercase_   ) + low_indices[:min_length][:, None]

  lowercase__  :   Optional[Any]    		=      [slice(lowercase_   )] * rank
  lowercase__  :   Optional[Any]    		=      indices
  lowercase__  :   List[Any]    		=      input[s]

  lowercase__  :   List[Any]    		=      list(range(0 , rank + 1   )   )
  perm.append(perm.pop(dimension + 1   )   )

  return sliced.permute(lowercase_   )

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Any:


  '''simple docstring'''






  import torch

  lowercase__  :   int    		=      torch.arange(1 , lowercase_   )
  lowercase__  :   Optional[Any]    		=      torch.remainder(lowercase_ , lowercase_   )
  lowercase__  :   List[Any]    		=      remainders == 0
  lowercase__  :   Tuple    		=      candidates[divisor_indices]
  lowercase__  :   Union[str, Any]    		=      torch.max(lowercase_   )
  return largest_divisor, torch.div(lowercase_ , lowercase_ , rounding_mode="""floor"""   )



class       _snake_case   (	UpperCAmelCase_							):


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   str    		=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}})
   if self.use_past:
     self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_					,  direction="""inputs""")
     lowercase__  :   int    		=      {0: """batch""", 1: """past_sequence + sequence"""}
   else:
     lowercase__  :   Union[str, Any]    		=      {0: """batch""", 1: """sequence"""}

   return common_inputs


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return self._config.num_heads


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = -1					,  SCREAMING_SNAKE_CASE_ = -1					,  SCREAMING_SNAKE_CASE_ = False					,  SCREAMING_SNAKE_CASE_ = None					,  ):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      super(SCREAMING_SNAKE_CASE_					,  self).generate_dummy_inputs(
       SCREAMING_SNAKE_CASE_					,  batch_size=SCREAMING_SNAKE_CASE_					,  seq_length=SCREAMING_SNAKE_CASE_					,  is_pair=SCREAMING_SNAKE_CASE_					,  framework=SCREAMING_SNAKE_CASE_)

   # We need to order the input in the way they appears in the forward()
   lowercase__  :   Optional[int]    		=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]})

   # Need to add the past_keys
   if self.use_past:
     if not is_torch_available():
       raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""")
     else:
       import torch

       lowercase__		,    lowercase__  :   List[str]    		=      common_inputs["""input_ids"""].shape
       # Not using the same length for past_key_values
       lowercase__  :   int    		=      seqlen + 2
       lowercase__  :   Optional[int]    		=      (
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       lowercase__  :   Optional[Any]    		=      [
           (torch.zeros(SCREAMING_SNAKE_CASE_), torch.zeros(SCREAMING_SNAKE_CASE_)) for _ in range(self.num_layers)
       ]

   lowercase__  :   Tuple    		=      common_inputs["""attention_mask"""]
   if self.use_past:
     lowercase__  :   Tuple    		=      ordered_inputs["""attention_mask"""].dtype
     lowercase__  :   Optional[Any]    		=      torch.cat(
         [ordered_inputs["""attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  dtype=SCREAMING_SNAKE_CASE_)]					,  dim=1)

   return ordered_inputs



 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return 13








lowerCamelCase__    :		str          =   [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






  lowercase__  :   Any    		=      [False] * len(lowercase_   )
  lowercase__  :   List[Any]    		=      [s]
  lowercase__  :   int    		=      True

  while queue:
    lowercase__  :   Dict    		=      queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowercase_   )
        lowercase__  :   Optional[Any]    		=      True
        lowercase__  :   int    		=      u

  return visited[t]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	int:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      [-1] * (len(lowercase_   ))
  lowercase__  :   Optional[Any]    		=      0
  lowercase__  :   List[Any]    		=      []
  lowercase__  :   Optional[Any]    		=      [i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase_ , lowercase_ , lowercase_ , lowercase_   ):
    lowercase__  :   Optional[int]    		=      float("""Inf"""   )
    lowercase__  :   Tuple    		=      sink

    while s != source:
      # Find the minimum value in select path
      lowercase__  :   List[str]    		=      min(lowercase_ , graph[parent[s]][s]   )
      lowercase__  :   List[str]    		=      parent[s]

    max_flow += path_flow
    lowercase__  :   str    		=      sink

    while v != source:
      lowercase__  :   int    		=      parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowercase__  :   Union[str, Any]    		=      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))






