






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


__a:     str   					=					logging.get_logger(__name__)

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

__a:     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",
    },
}

__a:     Any   					=					{
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCamelCase__     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  ['''input_ids''', '''attention_mask''']
  _lowerCamelCase    =  GPTaTokenizer


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

         _UpperCAmelCase							=    kwargs.pop("""add_bos_token"""       ,				__snake_case							)

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

         _UpperCAmelCase							=    add_prefix_space


  def  lowerCamelCase       (      self		:			Dict       ,				*lowerCamelCase		:			List[Any]       ,				**lowerCamelCase		:			Dict							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			str       ,				*lowerCamelCase		:			List[str]       ,				**lowerCamelCase		:			Tuple							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Optional[str] = None							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self._tokenizer.model.save(__snake_case       ,				name=__snake_case							)
         return tuple(__snake_case							)





  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			"Conversation"							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    []
         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:
                _UpperCAmelCase							=    input_ids[-self.model_max_length :]
         return input_ids





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """xlm-roberta-xl"""

						def __init__(		self :Dict     ,			__snake_case :Optional[Any]=25_08_80     ,			__snake_case :List[Any]=25_60     ,			__snake_case :Optional[Any]=36     ,			__snake_case :Any=32     ,			__snake_case :int=1_02_40     ,			__snake_case :List[Any]="gelu"     ,			__snake_case :Union[str, Any]=0.1     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :str=5_14     ,			__snake_case :Union[str, Any]=1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :str=1E-05     ,			__snake_case :str=1     ,			__snake_case :int=0     ,			__snake_case :Tuple=2     ,			__snake_case :Optional[int]="absolute"     ,			__snake_case :str=True     ,			__snake_case :Any=None     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			**__snake_case			)
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						List[str]										=hidden_size
										__magic_name__						:						Union[str, Any]										=num_hidden_layers
										__magic_name__						:						Any										=num_attention_heads
										__magic_name__						:						Any										=hidden_act
										__magic_name__						:						List[str]										=intermediate_size
										__magic_name__						:						Any										=hidden_dropout_prob
										__magic_name__						:						Union[str, Any]										=attention_probs_dropout_prob
										__magic_name__						:						Any										=max_position_embeddings
										__magic_name__						:						Any										=type_vocab_size
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						Optional[int]										=layer_norm_eps
										__magic_name__						:						Dict										=position_embedding_type
										__magic_name__						:						Any										=use_cache
										__magic_name__						:						Dict										=classifier_dropout




class 		__A   (      UpperCamelCase__      ):

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										if self.task == "multiple-choice":
														__magic_name__						:						str										={0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
														__magic_name__						:						Optional[Any]										={0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]			)




