
"""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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE_					     =	{
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE_					     =	{
    '''gpt-neox-20b''': 2_048,
}
class       lowerCAmelCase_		(				_UpperCamelCase ):



							'''simple docstring'''





							_snake_case						      =		VOCAB_FILES_NAMES
							_snake_case						      =		PRETRAINED_VOCAB_FILES_MAP
							_snake_case						      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_snake_case						      =		['input_ids', 'attention_mask']




							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_    ,				)       ->  List[Any]:
													super().__init__(
													    _UpperCAmelCase    ,				_UpperCAmelCase    ,				tokenizer_file=_UpperCAmelCase    ,				unk_token=_UpperCAmelCase    ,				bos_token=_UpperCAmelCase    ,				eos_token=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase    ,				**_UpperCAmelCase    ,				)

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

													__lowerCAmelCase     =  add_prefix_space




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None				)       ->  Optional[int]:
													__lowerCAmelCase     =  self._tokenizer.model.save(_UpperCAmelCase    ,				name=_UpperCAmelCase				)
													return tuple(_UpperCAmelCase				)




							def    A__						( self    ,				snake_case_				)       ->  Union[str, Any]:
													__lowerCAmelCase     =  []
													for is_user, text in conversation.iter_texts():
																			input_ids.extend(self.encode(_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase				) + [self.eos_token_id]				)

													if len(_UpperCAmelCase				) > self.model_max_length:
																			__lowerCAmelCase     =  input_ids[-self.model_max_length :]
													return input_ids





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__lowerCAmelCase		     =  {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		     =  [
							    '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ErnieForCausalLM''',
							    '''ErnieForMaskedLM''',
							    '''ErnieForMultipleChoice''',
							    '''ErnieForNextSentencePrediction''',
							    '''ErnieForPreTraining''',
							    '''ErnieForQuestionAnswering''',
							    '''ErnieForSequenceClassification''',
							    '''ErnieForTokenClassification''',
							    '''ErnieModel''',
							    '''ErniePreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ernie import (
														    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ErnieForCausalLM,
														    ErnieForMaskedLM,
														    ErnieForMultipleChoice,
														    ErnieForNextSentencePrediction,
														    ErnieForPreTraining,
														    ErnieForQuestionAnswering,
														    ErnieForSequenceClassification,
														    ErnieForTokenClassification,
														    ErnieModel,
														    ErniePreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase		     =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




