




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


a__		     =     logging.get_logger(__name__)

a__		     =     {
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase	,  _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Dict      	=		'dinat'

	__magic_name__   :       Union[str, Any]      	=		{
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}






	def __init__(					self		:						Any					,     lowerCAmelCase		:						int=4					,     lowerCAmelCase		:						Union[str, Any]=3					,     lowerCAmelCase		:						Dict=64					,     lowerCAmelCase		:						Tuple=[3, 4, 6, 5]					,     lowerCAmelCase		:						Union[str, Any]=[2, 4, 8, 16]					,     lowerCAmelCase		:						Any=7					,     lowerCAmelCase		:						int=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]					,     lowerCAmelCase		:						Optional[Any]=3.0					,     lowerCAmelCase		:						int=True					,     lowerCAmelCase		:						List[str]=0.0					,     lowerCAmelCase		:						int=0.0					,     lowerCAmelCase		:						List[str]=0.1					,     lowerCAmelCase		:						List[str]="gelu"					,     lowerCAmelCase		:						Optional[int]=0.02					,     lowerCAmelCase		:						Any=1E-5					,     lowerCAmelCase		:						str=0.0					,     lowerCAmelCase		:						List[str]=None					,     lowerCAmelCase		:						Any=None					,     **lowerCAmelCase		:						Tuple					,     )	->       Optional[int]:







						"""simple docstring"""







						super().__init__(**lowerCAmelCase	)

						__UpperCamelCase					:	Optional[int]             =      patch_size
						__UpperCamelCase					:	Optional[int]             =      num_channels
						__UpperCamelCase					:	List[str]             =      embed_dim
						__UpperCamelCase					:	int             =      depths
						__UpperCamelCase					:	Any             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      num_heads
						__UpperCamelCase					:	Union[str, Any]             =      kernel_size
						__UpperCamelCase					:	Any             =      dilations
						__UpperCamelCase					:	Optional[Any]             =      mlp_ratio
						__UpperCamelCase					:	Optional[int]             =      qkv_bias
						__UpperCamelCase					:	List[str]             =      hidden_dropout_prob
						__UpperCamelCase					:	List[Any]             =      attention_probs_dropout_prob
						__UpperCamelCase					:	List[str]             =      drop_path_rate
						__UpperCamelCase					:	Optional[Any]             =      hidden_act
						__UpperCamelCase					:	List[Any]             =      layer_norm_eps
						__UpperCamelCase					:	List[Any]             =      initializer_range
						# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
						# this indicates the channel dimension after the last stage of the model
						__UpperCamelCase					:	Optional[Any]             =      int(embed_dim * 2 ** (len(lowerCAmelCase	) - 1)	)
						__UpperCamelCase					:	Dict             =      layer_scale_init_value
						__UpperCamelCase					:	Optional[int]             =      ["""stem"""] + [F'''stage{idx}''' for idx in range(1					,     len(lowerCAmelCase	) + 1	)]
						__UpperCamelCase      ,			__UpperCamelCase					:	Dict             =      get_aligned_output_features_output_indices(
						    out_features=lowerCAmelCase					,     out_indices=lowerCAmelCase					,     stage_names=self.stage_names	)












# 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__		     =     logging.get_logger(__name__)

a__		     =     {}
a__		     =     {}
a__		     =     {}





def 				A__ (snake_case   :		type		,	snake_case   :		Optional[str]		,	snake_case   :		Optional[List[str]] = None		,	)     ->						str:
					__UpperCamelCase					:	int             =      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					:	Optional[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})'''				)
										__UpperCamelCase					:	Optional[int]             =      format_type





def 				A__ (snake_case   :		Exception		,	snake_case   :		Optional[str]		,	snake_case   :		Optional[List[str]] = None				)     ->						Tuple:
					__UpperCamelCase					:	List[Any]             =      aliases if aliases is not None else []
					for alias in set(aliases + [format_type]				):
										__UpperCamelCase					:	Optional[int]             =      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__		     =     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__		     =     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__		     =     ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])





def 				A__ (snake_case   :		Optional[str]				)     ->						Optional[str]:
					if format_type in _FORMAT_TYPES_ALIASES:
										return _FORMAT_TYPES_ALIASES[format_type]
					else:
										return format_type







def 				A__ (snake_case   :		Optional[str]		,	**snake_case   :		List[str]				)     ->						Formatter:
					__UpperCamelCase					:	int             =      get_format_type_from_alias(snake_case				)
					if format_type in _FORMAT_TYPES:
										return _FORMAT_TYPES[format_type](**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}\''''				)






