



"""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


__A   :							int										=   logging.get_logger(__name__)

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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						type					,     _SCREAMING_SNAKE_CASE   :						Optional[str]					,     _SCREAMING_SNAKE_CASE   :						Optional[List[str]] = None					,     ):






				'''simple docstring'''



				_UpperCAmelCase					=    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					=    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					=    format_type







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Exception					,     _SCREAMING_SNAKE_CASE   :						Optional[str]					,     _SCREAMING_SNAKE_CASE   :						Optional[List[str]] = None				):






				'''simple docstring'''



				_UpperCAmelCase					=    aliases if aliases is not None else []
				for alias in set(aliases + [format_type]				):
								_UpperCAmelCase					=    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   :							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   :							int										=   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   :							Union[str, Any]										=   ValueError("JAX needs to be installed to be able to return JAX arrays.")
					_register_unavailable_formatter(_jax_error, "jax", aliases=[])







def  lowercase	(      _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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[str]					,     **_SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    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}\''				)








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A   :							Optional[int]										=   logging.get_logger(__name__)

__A   :							Optional[Any]										=   {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 			_a (					lowerCAmelCase			,							lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""bit"""
			UpperCamelCase__			      =	["""preactivation""", """bottleneck"""]
			UpperCamelCase__			      =	["""SAME""", """VALID"""]



			def __init__(		self     :    List[Any]				,   __UpperCamelCase     :    Optional[int]=3				,   __UpperCamelCase     :    Optional[int]=6_4				,   __UpperCamelCase     :    List[Any]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,   __UpperCamelCase     :    Any=[3, 4, 6, 3]				,   __UpperCamelCase     :    str="preactivation"				,   __UpperCamelCase     :    Union[str, Any]="relu"				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Dict=3_2				,   __UpperCamelCase     :    List[str]=0.0				,   __UpperCamelCase     :    Any=False				,   __UpperCamelCase     :    Union[str, Any]=3_2				,   __UpperCamelCase     :    List[str]=1				,   __UpperCamelCase     :    List[str]=None				,   __UpperCamelCase     :    Optional[Any]=None				,   **__UpperCamelCase     :    List[str]				,   )->List[Any]:
							super().__init__(**__UpperCamelCase   )
							if layer_type not in self.layer_types:
											raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
							if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
															_UpperCAmelCase					=    global_padding.upper()
											else:
															raise ValueError(F'Padding strategy {global_padding} not supported'   )
							_UpperCAmelCase					=    num_channels
							_UpperCAmelCase					=    embedding_size
							_UpperCAmelCase					=    hidden_sizes
							_UpperCAmelCase					=    depths
							_UpperCAmelCase					=    layer_type
							_UpperCAmelCase					=    hidden_act
							_UpperCAmelCase					=    global_padding
							_UpperCAmelCase					=    num_groups
							_UpperCAmelCase					=    drop_path_rate
							_UpperCAmelCase					=    embedding_dynamic_padding
							_UpperCAmelCase					=    output_stride
							_UpperCAmelCase					=    width_factor

							_UpperCAmelCase					=    ['''stem'''] + [F'stage{idx}' for idx in range(1				,   len(__UpperCamelCase   ) + 1   )]
							_UpperCAmelCase		,						_UpperCAmelCase					=    get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase				,   out_indices=__UpperCamelCase				,   stage_names=self.stage_names   )



