"""simple docstring"""







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


_a					:					List[Any]=			logging.get_logger(__name__)

_a					:					Dict[Optional[str], Type[Formatter]]=			{}
_a					:					Dict[Optional[str], str]=			{}
_a					:					Dict[Optional[str], Exception]=			{}
def   __UpperCAmelCase    (   UpperCAmelCase_   :    type      ,							UpperCAmelCase_   :    Optional[str]      ,							UpperCAmelCase_   :    Optional[List[str]] = None      ,							)							->      Optional[int]:

	'''simple docstring'''





	__snake_case      :							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__})"  )
	__snake_case      :							List[Any]						=			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})"  )
		__snake_case      :							Tuple						=			format_type
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Exception      ,							UpperCAmelCase_   :    Optional[str]      ,							UpperCAmelCase_   :    Optional[List[str]] = None  )							->      Tuple:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			aliases if aliases is not None else []
	for alias in set(aliases + [format_type]  ):
		__snake_case      :							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:
	_a					:					Union[str, 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:
	_a					:					Optional[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:
	_a					:					Tuple=			ValueError("JAX needs to be installed to be able to return JAX arrays.")
	_register_unavailable_formatter(_jax_error, "jax", aliases=[])
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[str]  )							->      Optional[str]:

	'''simple docstring'''





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

def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[str]      ,							**UpperCAmelCase_   :    Dict  )							->      Formatter:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			get_format_type_from_alias(UpperCAmelCase_  )
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**UpperCAmelCase_  )
	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}'"  )



"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_a					:					Dict=			logging.get_logger(__name__)

class 		UpperCamelCase      (   lowercase						):



				def __init__(self    :				List[str]     ,  *_A    :				Dict     ,  **_A    :				Optional[Any])   ->     None:
					warnings.warn(
					    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
					    ' use CLIPImageProcessor instead.'     ,  _A     ,  )
					super().__init__(*_A     ,  **_A)





