


"""simple docstring"""





# 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__		:     str    				=				'''
Human: <<task>>

Assistant: '''


snake_case__		:     int    				=				'''huggingface-tools/default-prompts'''
snake_case__		:     int    				=				{'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}


def      _snake_case						(						_snake_case				:			Optional[int] ,   _snake_case				:			Dict ,   _snake_case				:			Any="run"			):
      if prompt_or_repo_id is None:
            lowerCAmelCase					:	List[Any]       =   DEFAULT_PROMPTS_REPO

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

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






"""simple docstring"""





from torch import nn



class 		snake_case_(       nn.Module			):






     def __init__(	self  :					int					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					int				):
           super().__init__()
           lowerCAmelCase					:	str       =   class_size
           lowerCAmelCase					:	Dict       =   embed_size
           # self.mlp1 = nn.Linear(embed_size, embed_size)
           # self.mlp2 = (nn.Linear(embed_size, class_size))
           lowerCAmelCase					:	Any       =   nn.Linear(UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					Tuple				):
           # hidden_state = nn.functional.relu(self.mlp1(hidden_state))
           # hidden_state = self.mlp2(hidden_state)
           lowerCAmelCase					:	int       =   self.mlp(UpperCamelCase_				)
           return logits


