



'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__lowerCAmelCase									=   logging.get_logger(__name__)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					'upernet'







							def __init__(							self					:       List[Any]	,_UpperCAmelCase					:       str=None	,_UpperCAmelCase					:       List[Any]=512	,_UpperCAmelCase					:       int=0.02	,_UpperCAmelCase					:       int=[1, 2, 3, 6]	,_UpperCAmelCase					:       int=True	,_UpperCAmelCase					:       Dict=0.4	,_UpperCAmelCase					:       Optional[int]=384	,_UpperCAmelCase					:       Optional[Any]=256	,_UpperCAmelCase					:       List[Any]=1	,_UpperCAmelCase					:       List[Any]=False	,_UpperCAmelCase					:       Tuple=255	,**_UpperCAmelCase					:       int	,):
									super().__init__(**_UpperCAmelCase		)

									if backbone_config is None:
											logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
											_a     :      List[str]        =							CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']		)
									elif isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
											_a     :      List[Any]        =							backbone_config.get('model_type'		)
											_a     :      List[str]        =							CONFIG_MAPPING[backbone_model_type]
											_a     :      Optional[int]        =							config_class.from_dict(_UpperCAmelCase		)

									_a     :      Optional[int]        =							backbone_config
									_a     :      Union[str, Any]        =							hidden_size
									_a     :      str        =							initializer_range
									_a     :      Any        =							pool_scales
									_a     :      str        =							use_auxiliary_head
									_a     :      Tuple        =							auxiliary_loss_weight
									_a     :      Optional[Any]        =							auxiliary_in_channels
									_a     :      Union[str, Any]        =							auxiliary_channels
									_a     :      List[str]        =							auxiliary_num_convs
									_a     :      List[str]        =							auxiliary_concat_input
									_a     :      Any        =							loss_ignore_index







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      int        =							copy.deepcopy(self.__dict__		)
									_a     :      List[Any]        =							self.backbone_config.to_dict()
									_a     :      Dict        =							self.__class__.model_type
									return output










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


__A        =    logging.get_logger(__name__)

__A        =    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

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

__A        =    {
    "gpt-neox-20b": 20_48,
}







class     __lowerCAmelCase (       __magic_name__ ):

			"""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			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__=False			,						**lowerCamelCase__			,						)    ->      int:



							'''simple docstring'''
							super().__init__(
							    lowerCamelCase__			,						lowerCamelCase__			,						tokenizer_file=lowerCamelCase__			,						unk_token=lowerCamelCase__			,						bos_token=lowerCamelCase__			,						eos_token=lowerCamelCase__			,						add_prefix_space=lowerCamelCase__			,						**lowerCamelCase__			,						)

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

							__lowerCamelCase		       =    add_prefix_space


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      Tuple[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self._tokenizer.model.save(lowerCamelCase__			,						name=lowerCamelCase__							)
							return tuple(lowerCamelCase__							)






			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							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:
											__lowerCamelCase		       =    input_ids[-self.model_max_length :]
							return input_ids


