

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase      :					Union[str, Any]					    =      logging.get_logger(__name__)

_lowerCamelCase      :					List[str]					    =      {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    """camembert"""

       def __init__(							self ,   lowercase__=3_0_5_2_2 ,   lowercase__=7_6_8 ,   lowercase__=1_2 ,   lowercase__=1_2 ,   lowercase__=3_0_7_2 ,   lowercase__="gelu" ,   lowercase__=0.1 ,   lowercase__=0.1 ,   lowercase__=5_1_2 ,   lowercase__=2 ,   lowercase__=0.02 ,   lowercase__=1E-12 ,   lowercase__=1 ,   lowercase__=0 ,   lowercase__=2 ,   lowercase__="absolute" ,   lowercase__=True ,   lowercase__=None ,   **lowercase__ ,   ):







             '''simple docstring'''



             super().__init__(pad_token_id=lowercase__ ,   bos_token_id=lowercase__ ,   eos_token_id=lowercase__ ,   **lowercase__     )

             __A    =vocab_size
             __A    =hidden_size
             __A    =num_hidden_layers
             __A    =num_attention_heads
             __A    =hidden_act
             __A    =intermediate_size
             __A    =hidden_dropout_prob
             __A    =attention_probs_dropout_prob
             __A    =max_position_embeddings
             __A    =type_vocab_size
             __A    =initializer_range
             __A    =layer_norm_eps
             __A    =position_embedding_type
             __A    =use_cache
             __A    =classifier_dropout





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             if self.task == "multiple-choice":
                   __A    ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                   __A    ={0: '''batch''', 1: '''sequence'''}
             return OrderedDict(
                 [
                     ('''input_ids''', dynamic_axis),
                     ('''attention_mask''', dynamic_axis),
                 ]     )





# 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
_lowerCamelCase      :					Optional[int]					    =      '''
Human: <<task>>

Assistant: '''


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





def      A__					(			__A  :						Dict     ,				__A  :						Dict     ,				__A  :						Optional[Any]="run"			)					->Any:
      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'''     ,				__A			) is not None:
            return prompt_or_repo_id

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


