



"""simple docstring"""







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


lowercase__		:				Optional[Any]          =	logging.get_logger(__name__)

lowercase__		:				Any          =	{
    """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       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """gpt_neo"""
      _SCREAMING_SNAKE_CASE  				=     ["""past_key_values"""]
      _SCREAMING_SNAKE_CASE  				=     {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



      def __init__(      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      List[str]=5_0_2_5_7    ,			SCREAMING_SNAKE_CASE_				:      List[str]=2_0_4_8    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=2_0_4_8    ,			SCREAMING_SNAKE_CASE_				:      int=2_4    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=[[["global", "local"], 1_2]]    ,			SCREAMING_SNAKE_CASE_				:      Tuple=1_6    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=None    ,			SCREAMING_SNAKE_CASE_				:      Dict=2_5_6    ,			SCREAMING_SNAKE_CASE_				:      str="gelu_new"    ,			SCREAMING_SNAKE_CASE_				:      Any=0.0    ,			SCREAMING_SNAKE_CASE_				:      Tuple=0.0    ,			SCREAMING_SNAKE_CASE_				:      Any=0.0    ,			SCREAMING_SNAKE_CASE_				:      List[str]=0.1    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=1E-5    ,			SCREAMING_SNAKE_CASE_				:      Any=0.02    ,			SCREAMING_SNAKE_CASE_				:      Dict=True    ,			SCREAMING_SNAKE_CASE_				:      List[str]=5_0_2_5_6    ,			SCREAMING_SNAKE_CASE_				:      Tuple=5_0_2_5_6    ,			**SCREAMING_SNAKE_CASE_				:      Tuple    ,			):
        lowerCAmelCase_   :				Optional[int]								= vocab_size
        lowerCAmelCase_   :				Optional[Any]								= max_position_embeddings
        lowerCAmelCase_   :				Union[str, Any]								= hidden_size
        lowerCAmelCase_   :				Optional[int]								= num_layers
        lowerCAmelCase_   :				List[Any]								= num_heads
        lowerCAmelCase_   :				Any								= intermediate_size
        lowerCAmelCase_   :				Any								= window_size
        lowerCAmelCase_   :				Union[str, Any]								= activation_function
        lowerCAmelCase_   :				List[Any]								= resid_dropout
        lowerCAmelCase_   :				Dict								= embed_dropout
        lowerCAmelCase_   :				Tuple								= attention_dropout
        lowerCAmelCase_   :				Any								= classifier_dropout
        lowerCAmelCase_   :				Union[str, Any]								= layer_norm_epsilon
        lowerCAmelCase_   :				Optional[Any]								= initializer_range
        lowerCAmelCase_   :				Any								= use_cache

        lowerCAmelCase_   :				Union[str, Any]								= bos_token_id
        lowerCAmelCase_   :				Optional[Any]								= eos_token_id

        lowerCAmelCase_   :				Union[str, Any]								= attention_types
        lowerCAmelCase_   :				str								= 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      SCREAMING_SNAKE_CASE__ (      SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				Dict								= []
        for item in attention_types:
          for _ in range(item[1]       ):
            attentions.extend(item[0]       )
        return attentions




def 				UpperCamelCase_    (    lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Optional[int]						)						->   Dict:

  """simple docstring"""




  import torch

  lowerCAmelCase_   :				List[str]								= input.size()
  lowerCAmelCase_   :				Optional[int]								= len(lowerCAmelCase__						)
  lowerCAmelCase_   :				Optional[int]								= shape[dimension]

  lowerCAmelCase_   :				Dict								= torch.arange(0    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
  lowerCAmelCase_   :				Optional[Any]								= torch.div(sizedim - size    ,   lowerCAmelCase__    ,   rounding_mode='floor'						) + 1
  lowerCAmelCase_   :				str								= torch.arange(lowerCAmelCase__						) + low_indices[:min_length][:, None]

  lowerCAmelCase_   :				Tuple								= [slice(lowerCAmelCase__						)] * rank
  lowerCAmelCase_   :				Union[str, Any]								= indices
  lowerCAmelCase_   :				Optional[Any]								= input[s]

  lowerCAmelCase_   :				Any								= list(range(0    ,   rank + 1						)						)
  perm.append(perm.pop(dimension + 1						)						)

  return sliced.permute(lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Union[str, Any]						)						->   str:

  """simple docstring"""




  import torch

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







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				List[Any]								= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
        if self.use_past:
          self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_    ,			direction='inputs'       )
          lowerCAmelCase_   :				Dict								= {0: 'batch', 1: 'past_sequence + sequence'}
        else:
          lowerCAmelCase_   :				Optional[Any]								= {0: 'batch', 1: 'sequence'}

        return common_inputs



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        return self._config.num_heads



      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      PreTrainedTokenizer    ,			SCREAMING_SNAKE_CASE_				:      int = -1    ,			SCREAMING_SNAKE_CASE_				:      int = -1    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      Optional[TensorType] = None    ,			):
        lowerCAmelCase_   :				Dict								= 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()
        lowerCAmelCase_   :				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

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

        lowerCAmelCase_   :				Any								= common_inputs['attention_mask']
        if self.use_past:
          lowerCAmelCase_   :				int								= ordered_inputs['attention_mask'].dtype
          lowerCAmelCase_   :				Optional[int]								= 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      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        return 1_3










"""simple docstring"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= [0] * len(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[str]								= []
  lowerCAmelCase_   :				Tuple								= [1] * len(lowerCAmelCase__						)

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(lowerCAmelCase__						)						):
    if indegree[i] == 0:
      queue.append(lowerCAmelCase__						)

  while queue:
    lowerCAmelCase_   :				Union[str, Any]								= queue.pop(0						)
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        lowerCAmelCase_   :				Any								= long_dist[vertex] + 1

      if indegree[x] == 0:
        queue.append(lowerCAmelCase__						)

  print(max(lowerCAmelCase__						)						)


# Adjacency list of Graph
lowercase__		:				Any          =	{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)





