'''simple docstring'''

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase_ : Dict						  =      TypeVar("T")


def __a (	__lowerCamelCase			:						int				)	->							int:
      '''simple docstring'''


      return (position - 1) // 2


def __a (	__lowerCamelCase			:						int				)	->							int:
      '''simple docstring'''


      return (2 * position) + 1


def __a (	__lowerCamelCase			:						int				)	->							int:
      '''simple docstring'''


      return (2 * position) + 2





class 							lowercase						(						Generic[T] ):


   def __init__(   self				:	List[str])      ->					None:
         lowercase_     =      []
         lowercase_     =      {}
         lowercase_     =      0


   def __len__(   self				:	Dict)      ->					int:
         return self.elements


   def __repr__(   self				:	Optional[int])      ->					str:
         return str(self.heap)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					bool:
         # Check if the priority queue is empty
         return self.elements == 0


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	T   ,		__lowerCAmelCase				:	int)      ->					None:
         # Add an element with given priority to the queue
         self.heap.append((elem, weight))
         lowercase_     =      self.elements
         self.elements += 1
         self._bubble_up(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	str)      ->					T:
         # Remove and return the element with lowest weight (highest priority)
         if self.elements > 1:
               self._swap_nodes(0   ,		self.elements - 1)
         lowercase_ ,							lowercase_     =      self.heap.pop()
         del self.position_map[elem]
         self.elements -= 1
         if self.elements > 0:
               lowercase_ ,							lowercase_     =      self.heap[0]
               self._bubble_down(__lowerCAmelCase)
         return elem


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	T   ,		__lowerCAmelCase				:	int)      ->					None:
         # Update the weight of the given key
         lowercase_     =      self.position_map[elem]
         lowercase_     =      (elem, weight)
         if position > 0:
               lowercase_     =      get_parent_position(__lowerCAmelCase)
               lowercase_ ,							lowercase_     =      self.heap[parent_position]
               if parent_weight > weight:
                     self._bubble_up(__lowerCAmelCase)
               else:
                     self._bubble_down(__lowerCAmelCase)
         else:
               self._bubble_down(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	T)      ->					None:
         # Place a node at the proper position (upward movement) [to be used internally
         # only]
         lowercase_     =      self.position_map[elem]
         if curr_pos == 0:
               return None
         lowercase_     =      get_parent_position(__lowerCAmelCase)
         lowercase_ ,							lowercase_     =      self.heap[curr_pos]
         lowercase_ ,							lowercase_     =      self.heap[parent_position]
         if parent_weight > weight:
               self._swap_nodes(__lowerCAmelCase   ,		__lowerCAmelCase)
               return self._bubble_up(__lowerCAmelCase)
         return None


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	T)      ->					None:
         # Place a node at the proper position (downward movement) [to be used
         # internally only]
         lowercase_     =      self.position_map[elem]
         lowercase_ ,							lowercase_     =      self.heap[curr_pos]
         lowercase_     =      get_child_left_position(__lowerCAmelCase)
         lowercase_     =      get_child_right_position(__lowerCAmelCase)
         if child_left_position < self.elements and child_right_position < self.elements:
               lowercase_ ,							lowercase_     =      self.heap[child_left_position]
               lowercase_ ,							lowercase_     =      self.heap[child_right_position]
               if child_right_weight < child_left_weight and child_right_weight < weight:
                     self._swap_nodes(__lowerCAmelCase   ,		__lowerCAmelCase)
                     return self._bubble_down(__lowerCAmelCase)
         if child_left_position < self.elements:
               lowercase_ ,							lowercase_     =      self.heap[child_left_position]
               if child_left_weight < weight:
                     self._swap_nodes(__lowerCAmelCase   ,		__lowerCAmelCase)
                     return self._bubble_down(__lowerCAmelCase)
         else:
               return None
         if child_right_position < self.elements:
               lowercase_ ,							lowercase_     =      self.heap[child_right_position]
               if child_right_weight < weight:
                     self._swap_nodes(__lowerCAmelCase   ,		__lowerCAmelCase)
                     return self._bubble_down(__lowerCAmelCase)
         return None


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	int)      ->					None:
         # Swap the nodes at the given positions
         lowercase_     =      self.heap[nodea_pos][0]
         lowercase_     =      self.heap[nodea_pos][0]
         lowercase_ ,							lowercase_     =      (
             self.heap[nodea_pos],
             self.heap[nodea_pos],
         )
         lowercase_     =      nodea_pos
         lowercase_     =      nodea_pos





class 							lowercase						(						Generic[T] ):


   def __init__(   self				:	Dict)      ->					None:
         lowercase_     =      {}
         lowercase_     =      0


   def __repr__(   self				:	str)      ->					str:
         return str(self.connections)


   def __len__(   self				:	List[str])      ->					int:
         return self.nodes


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	T)      ->					None:
         # Add a node in the graph if it is not in the graph
         if node not in self.connections:
               lowercase_     =      {}
               self.nodes += 1


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	T   ,		__lowerCAmelCase				:	T   ,		__lowerCAmelCase				:	int)      ->					None:
         # Add an edge between 2 nodes in the graph
         self.add_node(__lowerCAmelCase)
         self.add_node(__lowerCAmelCase)
         lowercase_     =      weight
         lowercase_     =      weight




def __a (	__lowerCamelCase			:						GraphUndirectedWeighted[T] ,		)	->							tuple[dict[T, int], dict[T, T | None]]:
      '''simple docstring'''


      lowercase_     =      {node: maxsize for node in graph.connections}
      lowercase_     =      {node: None for node in graph.connections}

      lowercase_     =      MinPriorityQueue()
      for node, weight in dist.items():
            priority_queue.push(__lowerCamelCase ,		__lowerCamelCase				)

      if priority_queue.is_empty():
            return dist, parent

      # initialization
      lowercase_     =      priority_queue.extract_min()
      lowercase_     =      0
      for neighbour in graph.connections[node]:
            if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                  lowercase_     =      dist[node] + graph.connections[node][neighbour]
                  priority_queue.update_key(__lowerCamelCase ,		dist[neighbour]				)
                  lowercase_     =      node

    # running prim's algorithm
      while not priority_queue.is_empty():
            lowercase_     =      priority_queue.extract_min()
            for neighbour in graph.connections[node]:
                  if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                        lowercase_     =      dist[node] + graph.connections[node][neighbour]
                        priority_queue.update_key(__lowerCamelCase ,		dist[neighbour]				)
                        lowercase_     =      node
      return dist, parent







'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


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

lowerCAmelCase_ : Any						  =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_ : Tuple						  =      {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_ : Dict						  =      {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']
   lowerCamelCase_    					=GPTaTokenizer


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Any="<|endoftext|>"   ,		__lowerCAmelCase				:	Union[str, Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	List[Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	Optional[Any]=False   ,		**__lowerCAmelCase				:	Dict   ,		)      ->					int:
         super().__init__(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		tokenizer_file=__lowerCAmelCase   ,		unk_token=__lowerCAmelCase   ,		bos_token=__lowerCAmelCase   ,		eos_token=__lowerCAmelCase   ,		add_prefix_space=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

         lowercase_     =      kwargs.pop("add_bos_token"   ,		__lowerCAmelCase)

         lowercase_     =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get("add_prefix_space"   ,		__lowerCAmelCase) != add_prefix_space:
               lowercase_     =      getattr(__lowerCAmelCase   ,		pre_tok_state.pop("type"))
               lowercase_     =      add_prefix_space
               lowercase_     =      pre_tok_class(**__lowerCAmelCase)

         lowercase_     =      add_prefix_space


   def __UpperCAmelCase       (   self				:	List[str]   ,		*__lowerCAmelCase				:	List[str]   ,		**__lowerCAmelCase				:	str)      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)
         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		*__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)

         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         lowercase_     =      self._tokenizer.model.save(__lowerCAmelCase   ,		name=__lowerCAmelCase)
         return tuple(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	"Conversation")      ->					List[int]:
         lowercase_     =      []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase) + [self.eos_token_id])

         if len(__lowerCAmelCase) > self.model_max_length:
               lowercase_     =      input_ids[-self.model_max_length :]
         return input_ids






