






from typing import TYPE_CHECKING

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


__a   :							Union[str, Any]						= {
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							List[str]						= ["MobileViTFeatureExtractor"]
  __a   :							Tuple						= ["MobileViTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MobileViTForImageClassification",
      "MobileViTForSemanticSegmentation",
      "MobileViTModel",
      "MobileViTPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Union[str, Any]						= [
      "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFMobileViTForImageClassification",
      "TFMobileViTForSemanticSegmentation",
      "TFMobileViTModel",
      "TFMobileViTPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_mobilevit import MobileViTFeatureExtractor
    from .image_processing_mobilevit import MobileViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilevit import (
        MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTForImageClassification,
        MobileViTForSemanticSegmentation,
        MobileViTModel,
        MobileViTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilevit import (
        TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileViTForImageClassification,
        TFMobileViTForSemanticSegmentation,
        TFMobileViTModel,
        TFMobileViTPreTrainedModel,
    )


else:
  import sys

  __a   :							Optional[Any]						= _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
__a   :							Tuple						= "\nHuman: <<task>>\n\nAssistant: "


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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Optional[Any]      ,					__lowercase :      List[Any]="run"     )    ->   Optional[int]:





    """simple docstring"""

    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"""      ,					__lowercase     ) is not None:
        return prompt_or_repo_id

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




