




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


UpperCAmelCase_		:					str          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_		:					Union[str, Any]          =  {
    "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",
    },
}

UpperCAmelCase_		:					Any          =  {
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       VOCAB_FILES_NAMES
						UpperCamelCase      =       PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      =       ["""input_ids""", """attention_mask"""]
						UpperCamelCase      =       GPTaTokenizer

						def __init__(		self :Dict     ,			__snake_case :int=None     ,			__snake_case :Union[str, Any]=None     ,			__snake_case :Any=None     ,			__snake_case :List[str]="<|endoftext|>"     ,			__snake_case :int="<|endoftext|>"     ,			__snake_case :Union[str, Any]="<|endoftext|>"     ,			__snake_case :Optional[int]=False     ,			**__snake_case :Optional[Any]     ,			):
										'''simple docstring'''

										super().__init__(
										    __snake_case     ,			__snake_case     ,			tokenizer_file=__snake_case     ,			unk_token=__snake_case     ,			bos_token=__snake_case     ,			eos_token=__snake_case     ,			add_prefix_space=__snake_case     ,			**__snake_case     ,			)

										__magic_name__						:						Union[str, Any]										=kwargs.pop("""add_bos_token"""     ,			__snake_case			)

										__magic_name__						:						Dict										=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
										if pre_tok_state.get("""add_prefix_space"""     ,			__snake_case			) != add_prefix_space:
														__magic_name__						:						Dict										=getattr(__snake_case     ,			pre_tok_state.pop("""type"""			)			)
														__magic_name__						:						Dict										=add_prefix_space
														__magic_name__						:						List[str]										=pre_tok_class(**__snake_case			)

										__magic_name__						:						Union[str, Any]										=add_prefix_space

						def      A__    (		self :Dict     ,			*__snake_case :List[Any]     ,			**__snake_case :Dict			):
										'''simple docstring'''

										__magic_name__						:						int										=kwargs.get("""is_split_into_words"""     ,			__snake_case			)
										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(*__snake_case     ,			**__snake_case			)

						def      A__    (		self :str     ,			*__snake_case :List[str]     ,			**__snake_case :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Any										=kwargs.get("""is_split_into_words"""     ,			__snake_case			)

										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(*__snake_case     ,			**__snake_case			)

						def      A__    (		self :Dict     ,			__snake_case :str     ,			__snake_case :Optional[str] = None			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self._tokenizer.model.save(__snake_case     ,			name=__snake_case			)
										return tuple(__snake_case			)


						def      A__    (		self :Optional[Any]     ,			__snake_case :"Conversation"			):
										'''simple docstring'''

										__magic_name__						:						Dict										=[]
										for is_user, text in conversation.iter_texts():
														input_ids.extend(self.encode(__snake_case     ,			add_special_tokens=__snake_case			) + [self.eos_token_id]			)

										if len(__snake_case			) > self.model_max_length:
														__magic_name__						:						Union[str, Any]										=input_ids[-self.model_max_length :]
										return input_ids










import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						list[list]										=[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase						,  [-1 * len(lowerCamelCase ), (key, value)] )

				# chosen_vertices = set of chosen vertices
				__magic_name__						:						Tuple										=set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								__magic_name__						:						Tuple										=heapq.heappop(lowerCamelCase )[1][0]
								chosen_vertices.add(lowerCamelCase )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																__magic_name__						:						Tuple										=elem[1][1].index(lowerCamelCase )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase )
				return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					Optional[int]          =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




