
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


lowercase  : int						   =						logging.get_logger(__name__)

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

lowercase  : 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",
    },
}

lowercase  : Optional[int]						   =						{
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						Any=    VOCAB_FILES_NAMES
    _a		:						List[Any]=    PRETRAINED_VOCAB_FILES_MAP
    _a		:						Optional[Any]=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						int=    ["input_ids", "attention_mask"]
    _a		:						Optional[Any]=    GPTaTokenizer
    def __init__(  self		,snake_case=None		,snake_case=None		,snake_case=None		,snake_case="<|endoftext|>"		,snake_case="<|endoftext|>"		,snake_case="<|endoftext|>"		,snake_case=False		,**snake_case		,):



           '''simple docstring'''







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

           lowercase							:					Dict	     =				kwargs.pop("""add_bos_token"""		,_a				)

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

           lowercase							:					str	     =				add_prefix_space
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				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 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           lowercase							:					Union[str, 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()._encode_plus(*_a		,**_a				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self._tokenizer.model.save(_a		,name=_a				)
           return tuple(_a				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				[]
           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:
                  lowercase							:					str	     =				input_ids[-self.model_max_length :]
           return input_ids


import math
from datetime import datetime, timedelta







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   datetime:
       lowercase							:					Any	     =				year % 19
       lowercase							:					Optional[int]	     =				year % 4
       lowercase							:					Any	     =				year % 7
       lowercase							:					str	     =				math.floor(year / 100    )
       lowercase							:					List[str]	     =				math.floor((13 + 8 * leap_day_inhibits) / 25    )
       lowercase							:					Tuple	     =				leap_day_inhibits / 4
       lowercase							:					Any	     =				(
           15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
       ) % 30
       lowercase							:					Union[str, Any]	     =				(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

       # days to be added to March 21
       lowercase							:					str	     =				(19 * metonic_cycle + secular_moon_shift) % 30

       # PHM -> Paschal Full Moon
       lowercase							:					List[Any]	     =				(
           2 * julian_leap_year
           + 4 * non_leap_year
           + 6 * days_to_add
           + century_starting_point
       ) % 7

       if days_to_add == 29 and days_from_phm_to_sunday == 6:
              return datetime(SCREAMING_SNAKE_CASE__ ,     4 ,     19    )
       elif days_to_add == 28 and days_from_phm_to_sunday == 6:
              return datetime(SCREAMING_SNAKE_CASE__ ,     4 ,     18    )
       else:
              return datetime(SCREAMING_SNAKE_CASE__ ,     3 ,     22    ) + timedelta(
                  days=int(days_to_add + days_from_phm_to_sunday    )    )


if __name__ == "__main__":
  for year in (1994, 2000, 2010, 2021, 2023):
    lowercase  : List[str]						   =							"""will be""" if year > datetime.now().year else """was"""
    print(F'''Easter in {year} {tense} {gauss_easter(year)}''')
