





from typing import TYPE_CHECKING

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


UpperCamelCase__           =			{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__           =			["LxmertTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__           =			[
					    "LxmertEncoder",
					    "LxmertForPreTraining",
					    "LxmertForQuestionAnswering",
					    "LxmertModel",
					    "LxmertPreTrainedModel",
					    "LxmertVisualFeatureEncoder",
					    "LxmertXLayer",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__           =			[
					    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFLxmertForPreTraining",
					    "TFLxmertMainLayer",
					    "TFLxmertModel",
					    "TFLxmertPreTrainedModel",
					    "TFLxmertVisualFeatureEncoder",
					]


if TYPE_CHECKING:
					from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
					from .tokenization_lxmert import LxmertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_lxmert_fast import LxmertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lxmert import (
										    LxmertEncoder,
										    LxmertForPreTraining,
										    LxmertForQuestionAnswering,
										    LxmertModel,
										    LxmertPreTrainedModel,
										    LxmertVisualFeatureEncoder,
										    LxmertXLayer,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_lxmert import (
										    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLxmertForPreTraining,
										    TFLxmertMainLayer,
										    TFLxmertModel,
										    TFLxmertPreTrainedModel,
										    TFLxmertVisualFeatureEncoder,
										)

else:
					import sys

					UpperCamelCase__           =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











# 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
UpperCamelCase__           =			"\nHuman: <<task>>\n\nAssistant: "


UpperCamelCase__           =			"huggingface-tools/default-prompts"
UpperCamelCase__           =			{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :Optional[Any] ,      a__ :List[str]="run"							):






							"""simple docstring"""





							if prompt_or_repo_id is None:
														UpperCamelCase__					  =      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

							UpperCamelCase__					  =      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()




