"""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


lowerCAmelCase__  :  Tuple						     =	logging.get_logger(__name__)

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

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

lowerCAmelCase__  :  Any						     =	{
    'gpt-neox-20b': 2_048,
}
class 				snake_case       ( __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   :	Dict    ,lowerCamelCase__   :	Union[str, Any]=None    ,lowerCamelCase__   :	Tuple=None    ,lowerCamelCase__   :	str=None    ,lowerCamelCase__   :	int="<|endoftext|>"    ,lowerCamelCase__   :	Dict="<|endoftext|>"    ,lowerCamelCase__   :	int="<|endoftext|>"    ,lowerCamelCase__   :	Any=False    ,**lowerCamelCase__   :	List[str]    ,):
									super().__init__(
									    lowerCamelCase__    ,lowerCamelCase__    ,tokenizer_file=lowerCamelCase__    ,unk_token=lowerCamelCase__    ,bos_token=lowerCamelCase__    ,eos_token=lowerCamelCase__    ,add_prefix_space=lowerCamelCase__    ,**lowerCamelCase__    ,)

									UpperCAmelCase__             =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
									if pre_tok_state.get('add_prefix_space'    ,lowerCamelCase__			) != add_prefix_space:
													UpperCAmelCase__             =							getattr(lowerCamelCase__    ,pre_tok_state.pop('type'			)			)
													UpperCAmelCase__             =							add_prefix_space
													UpperCAmelCase__             =							pre_tok_class(**lowerCamelCase__			)

									UpperCAmelCase__             =							add_prefix_space







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Optional[str] = None			):
									UpperCAmelCase__             =							self._tokenizer.model.save(lowerCamelCase__    ,name=lowerCamelCase__			)
									return tuple(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	"Conversation"			):
									UpperCAmelCase__             =							[]
									for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(lowerCamelCase__    ,add_special_tokens=lowerCamelCase__			) + [self.eos_token_id]			)

									if len(lowerCamelCase__			) > self.model_max_length:
													UpperCAmelCase__             =							input_ids[-self.model_max_length :]
									return input_ids




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__  :  Tuple						     =	{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  List[Any]						     =	[
						    'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMAEForPreTraining',
						    'ViTMAELayer',
						    'ViTMAEModel',
						    'ViTMAEPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  int						     =	[
						    'TFViTMAEForPreTraining',
						    'TFViTMAEModel',
						    'TFViTMAEPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						lowerCAmelCase__  :  int						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



