


'''simple docstring'''





# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__UpperCamelCase       :       List[str]						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Dict[Optional[str], Type[Formatter]]						       =				{}
__UpperCamelCase       :       Dict[Optional[str], str]						       =				{}
__UpperCamelCase       :       Dict[Optional[str], Exception]						       =				{}

def 			_a  (				SCREAMING_SNAKE_CASE	:				type    , SCREAMING_SNAKE_CASE	:				Optional[str]    , SCREAMING_SNAKE_CASE	:				Optional[List[str]] = None    , ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   aliases if aliases is not None else []
 if format_type in _FORMAT_TYPES:
  logger.warning(
      F"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"     )
 UpperCamelCase__					:    str          =   formatter_cls
 for alias in set(aliases + [format_type]     ):
  if alias in _FORMAT_TYPES_ALIASES:
   logger.warning(
       F"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"     )
  UpperCamelCase__					:    Union[str, Any]          =   format_type

def 			_a  (				SCREAMING_SNAKE_CASE	:				Exception    , SCREAMING_SNAKE_CASE	:				Optional[str]    , SCREAMING_SNAKE_CASE	:				Optional[List[str]] = None     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   aliases if aliases is not None else []
 for alias in set(aliases + [format_type]     ):
  UpperCamelCase__					:    str          =   unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["python"])
_register_formatter(ArrowFormatter, "arrow", aliases=["pa", "pyarrow"])
_register_formatter(NumpyFormatter, "numpy", aliases=["np"])
_register_formatter(PandasFormatter, "pandas", aliases=["pd"])
_register_formatter(CustomFormatter, "custom")

if config.TORCH_AVAILABLE:
       from .torch_formatter import TorchFormatter

       _register_formatter(TorchFormatter, "torch", aliases=["pt", "pytorch"])
else:
       __UpperCamelCase       :       Optional[Any]						       =				ValueError("PyTorch needs to be installed to be able to return PyTorch tensors.")
       _register_unavailable_formatter(_torch_error, "torch", aliases=["pt", "pytorch"])

if config.TF_AVAILABLE:
       from .tf_formatter import TFFormatter

       _register_formatter(TFFormatter, "tensorflow", aliases=["tf"])
else:
       __UpperCamelCase       :       Any						       =				ValueError("Tensorflow needs to be installed to be able to return Tensorflow tensors.")
       _register_unavailable_formatter(_tf_error, "tensorflow", aliases=["tf"])

if config.JAX_AVAILABLE:
       from .jax_formatter import JaxFormatter

       _register_formatter(JaxFormatter, "jax", aliases=[])
else:
       __UpperCamelCase       :       List[str]						       =				ValueError("JAX needs to be installed to be able to return JAX arrays.")
       _register_unavailable_formatter(_jax_error, "jax", aliases=[])

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[str]     ):







 """simple docstring"""



 if format_type in _FORMAT_TYPES_ALIASES:
  return _FORMAT_TYPES_ALIASES[format_type]
 else:
  return format_type






def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[str]    , **SCREAMING_SNAKE_CASE	:				List[Any]     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   get_format_type_from_alias(SCREAMING_SNAKE_CASE     )
 if format_type in _FORMAT_TYPES:
  return _FORMAT_TYPES[format_type](**SCREAMING_SNAKE_CASE     )
 if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
  raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
 else:
  raise ValueError(
      F"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None     )}, but got '{format_type}'"     )






from typing import TYPE_CHECKING

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


__UpperCamelCase       :       Dict						       =				{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       str						       =				["MobileViTFeatureExtractor"]
       __UpperCamelCase       :       Optional[Any]						       =				["MobileViTImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Union[str, Any]						       =				[
           "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MobileViTForImageClassification",
           "MobileViTForSemanticSegmentation",
           "MobileViTModel",
           "MobileViTPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "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

       __UpperCamelCase       :       str						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


