

'''simple docstring'''






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


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}






class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str							=			'bit'
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			['preactivation', 'bottleneck']
  SCREAMING_SNAKE_CASE    :  str							=			['SAME', 'VALID']






  def __init__(      self   :     Optional[Any]					,lowercase__   :     Any=3					,lowercase__   :     Tuple=6_4					,lowercase__   :     List[str]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]					,lowercase__   :     Any=[3, 4, 6, 3]					,lowercase__   :     str="preactivation"					,lowercase__   :     Dict="relu"					,lowercase__   :     Optional[int]=None					,lowercase__   :     str=3_2					,lowercase__   :     int=0.0					,lowercase__   :     List[Any]=False					,lowercase__   :     Optional[Any]=3_2					,lowercase__   :     Any=1					,lowercase__   :     Any=None					,lowercase__   :     Dict=None					,**lowercase__   :     Union[str, Any]					,):
   super().__init__(**lowercase__			)
   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:
     __lowercase							  = global_padding.upper()
    else:
     raise ValueError(F"Padding strategy {global_padding} not supported"			)
   __lowercase							  = num_channels
   __lowercase							  = embedding_size
   __lowercase							  = hidden_sizes
   __lowercase							  = depths
   __lowercase							  = layer_type
   __lowercase							  = hidden_act
   __lowercase							  = global_padding
   __lowercase							  = num_groups
   __lowercase							  = drop_path_rate
   __lowercase							  = embedding_dynamic_padding
   __lowercase							  = output_stride
   __lowercase							  = width_factor

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_ :						str      =      {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Tuple      =      [
	    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''OPTForCausalLM''',
	    '''OPTModel''',
	    '''OPTPreTrainedModel''',
	    '''OPTForSequenceClassification''',
	    '''OPTForQuestionAnswering''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Any      =      ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Optional[Any]      =      [
	    '''FlaxOPTForCausalLM''',
	    '''FlaxOPTModel''',
	    '''FlaxOPTPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

	UpperCAmelCase_ :						List[Any]      =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
