



'''simple docstring'''






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


UpperCamelCase__:     int										=    logging.get_logger(__name__)

UpperCamelCase__:     Optional[int]										=    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__:     List[Any]										=    {
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

UpperCamelCase__:     int										=    {
    "gpt-neox-20b": 2048,
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= VOCAB_FILES_NAMES
	lowerCamelCase__    				= PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__    				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__    				= ["""input_ids""", """attention_mask"""]







	def __init__(		self    :  int	,    __snake_case    :  Tuple=None	,    __snake_case    :  Optional[Any]=None	,    __snake_case    :  Dict=None	,    __snake_case    :  int="<|endoftext|>"	,    __snake_case    :  List[str]="<|endoftext|>"	,    __snake_case    :  Optional[Any]="<|endoftext|>"	,    __snake_case    :  List[Any]=False	,    **__snake_case    :  Any	,    )    ->	Dict:
								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				: Tuple											=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
								if pre_tok_state.get('''add_prefix_space'''	,    __snake_case	) != add_prefix_space:
															UpperCAmelCase				: Optional[Any]											=	getattr(__snake_case	,    pre_tok_state.pop('''type'''	)	)
															UpperCAmelCase				: List[Any]											=	add_prefix_space
															UpperCAmelCase				: Optional[Any]											=	pre_tok_class(**__snake_case	)

								UpperCAmelCase				: Tuple											=	add_prefix_space







	def   A			(		self    :  Tuple	,    __snake_case    :  str	,    __snake_case    :  Optional[str] = None	)    ->	Tuple[str]:
								UpperCAmelCase				: str											=	self._tokenizer.model.save(__snake_case	,    name=__snake_case	)
								return tuple(__snake_case	)







	def   A			(		self    :  int	,    __snake_case    :  "Conversation"	)    ->	List[int]:
								UpperCAmelCase				: int											=	[]
								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				: List[Any]											=	input_ids[-self.model_max_length :]
								return input_ids










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__:     Union[str, Any]										=    {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:     List[str]										=    [
						    "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LiltForQuestionAnswering",
						    "LiltForSequenceClassification",
						    "LiltForTokenClassification",
						    "LiltModel",
						    "LiltPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

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





