




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__A   :  int					=      {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A   :  Any					=      ['BridgeTowerImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A   :  List[Any]					=      [
				    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BridgeTowerForContrastiveLearning',
				    'BridgeTowerForImageAndTextRetrieval',
				    'BridgeTowerForMaskedLM',
				    'BridgeTowerModel',
				    'BridgeTowerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_bridgetower import (
				    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BridgeTowerConfig,
				    BridgeTowerTextConfig,
				    BridgeTowerVisionConfig,
				)
				from .processing_bridgetower import BridgeTowerProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_bridgetower import BridgeTowerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bridgetower import (
								    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BridgeTowerForContrastiveLearning,
								    BridgeTowerForImageAndTextRetrieval,
								    BridgeTowerForMaskedLM,
								    BridgeTowerModel,
								    BridgeTowerPreTrainedModel,
								)


else:
				import sys

				__A   :  List[str]					=      _LazyModule(__name__, globals()['__file__'], _import_structure)






"""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   :  int					=      '\nHuman: <<task>>\n\nAssistant: '


__A   :  List[str]					=      'huggingface-tools/default-prompts'
__A   :  Tuple					=      {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase="run"				)			->Optional[int]:






		"""simple docstring"""


		if prompt_or_repo_id is None:
				__lowercase				:		Any												=				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

		__lowercase				:		Optional[Any]												=				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()
