



'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowerCAmelCase									=   {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   ['''ConvBertTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ConvBertForMaskedLM''',
							    '''ConvBertForMultipleChoice''',
							    '''ConvBertForQuestionAnswering''',
							    '''ConvBertForSequenceClassification''',
							    '''ConvBertForTokenClassification''',
							    '''ConvBertLayer''',
							    '''ConvBertModel''',
							    '''ConvBertPreTrainedModel''',
							    '''load_tf_weights_in_convbert''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFConvBertForMaskedLM''',
							    '''TFConvBertForMultipleChoice''',
							    '''TFConvBertForQuestionAnswering''',
							    '''TFConvBertForSequenceClassification''',
							    '''TFConvBertForTokenClassification''',
							    '''TFConvBertLayer''',
							    '''TFConvBertModel''',
							    '''TFConvBertPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
							from .tokenization_convbert import ConvBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_convbert_fast import ConvBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_convbert import (
														    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ConvBertForMaskedLM,
														    ConvBertForMultipleChoice,
														    ConvBertForQuestionAnswering,
														    ConvBertForSequenceClassification,
														    ConvBertForTokenClassification,
														    ConvBertLayer,
														    ConvBertModel,
														    ConvBertPreTrainedModel,
														    load_tf_weights_in_convbert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_convbert import (
														    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFConvBertForMaskedLM,
														    TFConvBertForMultipleChoice,
														    TFConvBertForQuestionAnswering,
														    TFConvBertForSequenceClassification,
														    TFConvBertForTokenClassification,
														    TFConvBertLayer,
														    TFConvBertModel,
														    TFConvBertPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






"""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
_A			:       Optional[int]   		=       """
Human: <<task>>

Assistant: """


_A			:       List[Any]   		=       """huggingface-tools/default-prompts"""
_A			:       Optional[int]   		=       {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}


def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       List[Any]     , __snake_case				:       Dict="run"  ) ->				Union[str, Any]:
       if prompt_or_repo_id is None:
              lowercase    :				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

       lowercase    :				Optional[int]       		=							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()


