


from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							: Dict      =   logging.get_logger(__name__)

snake_case							: List[str]      =   {
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}







class  snake_case_     (UpperCAmelCase__   ):
					UpperCAmelCase__      :					Tuple					   =    '''roc_bert'''
					def __init__(   self     :Tuple    ,__snake_case     :Optional[int]=3_05_22    ,__snake_case     :int=7_68    ,__snake_case     :Dict=12    ,__snake_case     :List[Any]=12    ,__snake_case     :List[Any]=30_72    ,__snake_case     :int="gelu"    ,__snake_case     :List[str]=0.1    ,__snake_case     :List[Any]=0.1    ,__snake_case     :List[str]=5_12    ,__snake_case     :Union[str, Any]=2    ,__snake_case     :Union[str, Any]=0.02    ,__snake_case     :Tuple=1E-12    ,__snake_case     :Tuple=True    ,__snake_case     :Union[str, Any]=0    ,__snake_case     :Optional[int]="absolute"    ,__snake_case     :int=None    ,__snake_case     :List[Any]=True    ,__snake_case     :Union[str, Any]=True    ,__snake_case     :Optional[Any]=7_68    ,__snake_case     :Optional[int]=9_10    ,__snake_case     :List[Any]=5_12    ,__snake_case     :Any=2_48_58    ,__snake_case     :int=True    ,**__snake_case     :int    ,)      ->					List[str]:
												a__    =  vocab_size
												a__    =  max_position_embeddings
												a__    =  hidden_size
												a__    =  num_hidden_layers
												a__    =  num_attention_heads
												a__    =  intermediate_size
												a__    =  hidden_act
												a__    =  hidden_dropout_prob
												a__    =  attention_probs_dropout_prob
												a__    =  initializer_range
												a__    =  type_vocab_size
												a__    =  layer_norm_eps
												a__    =  use_cache
												a__    =  enable_pronunciation
												a__    =  enable_shape
												a__    =  pronunciation_embed_dim
												a__    =  pronunciation_vocab_size
												a__    =  shape_embed_dim
												a__    =  shape_vocab_size
												a__    =  concat_input
												a__    =  position_embedding_type
												a__    =  classifier_dropout
												super().__init__(pad_token_id=__lowerCAmelCase    ,**__lowerCAmelCase   )








# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
snake_case							: Dict      =   '''
Human: <<task>>

Assistant: '''


snake_case							: Optional[int]      =   '''huggingface-tools/default-prompts'''
snake_case							: Tuple      =   {'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}






def 							__lowercase     (				__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       Union[str, Any]      ,					__lowerCAmelCase							:       Any="run"     ):
							if prompt_or_repo_id is None:
														a__    =  DEFAULT_PROMPTS_REPO

							# prompt is considered a repo ID when it does not contain any kind of space
							if re.search('\\s'      ,					__lowerCAmelCase     ) is not None:
														return prompt_or_repo_id

							a__    =  cached_file(
							    __lowerCAmelCase      ,					PROMPT_FILES[mode]      ,					repo_type='dataset'      ,					user_agent={'agent': agent_name}     )
							with open(__lowerCAmelCase      ,					'r'      ,					encoding='utf-8'     ) as f:
														return f.read()




