
'''simple docstring'''





from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

UpperCAmelCase    					=   TypeVar("KEY")
UpperCAmelCase    					=   TypeVar("VAL")

@dataclass(frozen=_UpperCamelCase				,slots=_UpperCamelCase    )
class 						a   (				Generic[KEY, VAL]    ):
    _snake_case		    = 42
    _snake_case		    = 42




class 						a   (				_Item    ):




    def __init__(							self	:				List[str]      ):
          super().__init__(__a,    __a      )




    def __bool__(							self	:				Dict      ):
          return False




UpperCAmelCase    					=   _DeletedItem()

class 						a   (				MutableMapping[KEY, VAL]    ):




    def __init__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				int = 8,    SCREAMING_SNAKE_CASE_	:				float = 0.75      ):
          snake_case       :     str =    initial_block_size
          snake_case       :     list[_Item | None] =    [None] * initial_block_size
          assert 0.0 < capacity_factor < 1.0
          snake_case       :     List[str] =    capacity_factor
          snake_case       :     Dict =    0




    def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				KEY      ):
          return hash(__a      ) % len(self._buckets      )




    def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				int      ):
          return (ind + 1) % len(self._buckets      )




    def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				KEY,    SCREAMING_SNAKE_CASE_	:				VAL      ):
          snake_case       :     List[Any] =    self._buckets[ind]
          if not stored:
                snake_case       :     Tuple =    _Item(__a,    __a      )
                self._len += 1
                return True
          elif stored.key == key:
                snake_case       :     Union[str, Any] =    _Item(__a,    __a      )
                return True
          else:
                return False




    def 	__snake_case							(							self	:				List[str]      ):
          snake_case       :     Any =    len(self._buckets      ) * self._capacity_factor
          return len(self      ) >= int(__a      )




    def 	__snake_case							(							self	:				str      ):
          if len(self._buckets      ) <= self._initial_block_size:
                return False
          snake_case       :     List[str] =    len(self._buckets      ) * self._capacity_factor / 2
          return len(self      ) < limit




    def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				int      ):
          snake_case       :     Any =    self._buckets
          snake_case       :     List[Any] =    [None] * new_size
          snake_case       :     List[str] =    0
          for item in old_buckets:
                if item:
                      self._add_item(item.key,    item.val      )




    def 	__snake_case							(							self	:				int      ):
          self._resize(len(self._buckets      ) * 2      )




    def 	__snake_case							(							self	:				Union[str, Any]      ):
          self._resize(len(self._buckets      ) // 2      )




    def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				KEY      ):
          snake_case       :     str =    self._get_bucket_index(__a      )
          for _ in range(len(self._buckets      )      ):
                yield ind
                snake_case       :     Tuple =    self._get_next_ind(__a      )




    def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				KEY,    SCREAMING_SNAKE_CASE_	:				VAL      ):
          for ind in self._iterate_buckets(__a      ):
                if self._try_set(__a,    __a,    __a      ):
                      break




    def __setitem__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				KEY,    SCREAMING_SNAKE_CASE_	:				VAL      ):
          if self._is_full():
                self._size_up()

          self._add_item(__a,    __a      )




    def __delitem__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				KEY      ):
          for ind in self._iterate_buckets(__a      ):
                snake_case       :     Tuple =    self._buckets[ind]
                if item is None:
                      raise KeyError(__a      )
                if item is _deleted:
                      continue
                if item.key == key:
                      snake_case       :     List[Any] =    _deleted
                      self._len -= 1
                      break
          if self._is_sparse():
                self._size_down()




    def __getitem__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				KEY      ):
          for ind in self._iterate_buckets(__a      ):
                snake_case       :     Tuple =    self._buckets[ind]
                if item is None:
                      break
                if item is _deleted:
                      continue
                if item.key == key:
                      return item.val
          raise KeyError(__a      )




    def __len__(							self	:				List[Any]      ):
          return self._len




    def __iter__(							self	:				List[str]      ):
          yield from (item.key for item in self._buckets if item)




    def __repr__(							self	:				List[str]      ):
          snake_case       :     Optional[int] =    " ,".join(
              F"""{item.key}: {item.val}""" for item in self._buckets if item      )
          return F"""HashMap({val_string})"""



'''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


UpperCAmelCase    					=   logging.get_logger(__name__)

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

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

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = GPTaTokenizer




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

										snake_case       :     Optional[Any] =    kwargs.pop('''add_bos_token''',    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Dict =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     List[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Dict =    add_prefix_space
																snake_case       :     List[Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    add_prefix_space




				def 	__snake_case							(							self	:				Optional[Any],    *SCREAMING_SNAKE_CASE_	:				Dict,    **SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     str =    kwargs.get('''is_split_into_words''',    SCREAMING_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(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     int =    kwargs.get('''is_split_into_words''',    SCREAMING_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(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     List[Any] =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				"Conversation"      ):
										snake_case       :     int =    []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_      ) + [self.eos_token_id]      )

										if len(SCREAMING_SNAKE_CASE_      ) > self.model_max_length:
																snake_case       :     str =    input_ids[-self.model_max_length :]
										return input_ids

