





'''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							:     int		     =logging.get_logger(__name__)

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

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






class 					UpperCAmelCase     (	a__    ):
 __lowercase       		=    VOCAB_FILES_NAMES
 __lowercase       		=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase       		=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase       		=    ["""input_ids""", """attention_mask"""]
 __lowercase       		=    GPTaTokenizer
 def __init__(    self				:Any						,	lowercase_				:Optional[Any]=None						,	lowercase_				:Optional[int]=None						,	lowercase_				:List[Any]=None						,	lowercase_				:List[Any]="<|endoftext|>"						,	lowercase_				:Any="<|endoftext|>"						,	lowercase_				:Tuple="<|endoftext|>"						,	lowercase_				:Optional[int]=False						,	**lowercase_				:Optional[int]						,	)->							Tuple:
     super().__init__(
         lowercase_						,	lowercase_						,	tokenizer_file=lowercase_						,	unk_token=lowercase_						,	bos_token=lowercase_						,	eos_token=lowercase_						,	add_prefix_space=lowercase_						,	**lowercase_						,	)

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

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

     A__    =      add_prefix_space
 def 					UpperCAmelCase_							(    self				:List[str]						,	*lowercase_				:Tuple						,	**lowercase_				:Union[str, Any]							)->							List[Any]:
     A__    =      kwargs.get("is_split_into_words"						,	lowercase_							)
     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(*lowercase_						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Any						,	*lowercase_				:Tuple						,	**lowercase_				:Any							)->							Tuple:
     A__    =      kwargs.get("is_split_into_words"						,	lowercase_							)

     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(*lowercase_						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:str						,	lowercase_				:Optional[str] = None							)->							Optional[Any]:
     A__    =      self._tokenizer.model.save(lowercase_						,	name=lowercase_							)
     return tuple(lowercase_							)



 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:"Conversation"							)->							Dict:
     A__    =      []
     for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(lowercase_						,	add_special_tokens=lowercase_							) + [self.eos_token_id]							)

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










'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase							:     Any		     ={
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:     Tuple		     =[
      "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Swinv2ForImageClassification",
      "Swinv2ForMaskedImageModeling",
      "Swinv2Model",
      "Swinv2PreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swinva import (
        SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinvaForImageClassification,
        SwinvaForMaskedImageModeling,
        SwinvaModel,
        SwinvaPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase							:     List[Any]		     =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



