





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 				=     logging.get_logger(__name__)

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

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

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							VOCAB_FILES_NAMES
      a						       =							PRETRAINED_VOCAB_FILES_MAP
      a						       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						       =							['''input_ids''', '''attention_mask''']
      a						       =							GPTaTokenizer
      def __init__(		self      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__="<|endoftext|>"      ,       a__="<|endoftext|>"      ,       a__="<|endoftext|>"      ,       a__=False      ,       **a__      ,       ):
         super().__init__(
             a__      ,       a__      ,       tokenizer_file=a__      ,       unk_token=a__      ,       bos_token=a__      ,       eos_token=a__      ,       add_prefix_space=a__      ,       **a__      ,       )

         A_     :				Tuple			      =	kwargs.pop("""add_bos_token"""      ,       a__       )

         A_     :				Tuple			      =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
         if pre_tok_state.get("""add_prefix_space"""      ,       a__       ) != add_prefix_space:
            A_     :				str			      =	getattr(a__      ,       pre_tok_state.pop("""type"""       )       )
            A_     :				Union[str, Any]			      =	add_prefix_space
            A_     :				List[Any]			      =	pre_tok_class(**a__       )

         A_     :				List[str]			      =	add_prefix_space
      def 		_lowerCamelCase      (		self      ,       *a__      ,       **a__       ):
         A_     :				Any			      =	kwargs.get("""is_split_into_words"""      ,       a__       )
         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(*a__      ,       **a__       )
      def 		_lowerCamelCase      (		self      ,       *a__      ,       **a__       ):
         A_     :				Tuple			      =	kwargs.get("""is_split_into_words"""      ,       a__       )

         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(*a__      ,       **a__       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         A_     :				Optional[Any]			      =	self._tokenizer.model.save(a__      ,       name=a__       )
         return tuple(a__       )






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				int			      =	[]
         for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(a__      ,       add_special_tokens=a__       ) + [self.eos_token_id]       )

         if len(a__       ) > self.model_max_length:
            A_     :				Any			      =	input_ids[-self.model_max_length :]
         return input_ids













def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	[0] * len(_lowerCAmelCase     )
   A_     :				Optional[int]			      =	[]
   A_     :				str			      =	[]
   A_     :				Dict			      =	0

   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:
      A_     :				List[str]			      =	queue.pop(0     )
      cnt += 1
      topo.append(_lowerCAmelCase     )
      for x in graph[vertex]:
         indegree[x] -= 1
         if indegree[x] == 0:
            queue.append(_lowerCAmelCase     )

   if cnt != len(_lowerCAmelCase     ):
      print("""Cycle exists"""     )
   else:
      print(_lowerCAmelCase     )


# Adjacency List of Graph
_lowerCAmelCase 				=     {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)






