



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


_lowerCAmelCase	:   Any    =   logging.get_logger(__name__)

_lowerCAmelCase	:   Optional[int]    =   {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}


class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	'bit'
		SCREAMING_SNAKE_CASE__								=	['preactivation', 'bottleneck']
		SCREAMING_SNAKE_CASE__								=	['SAME', 'VALID']
		def __init__(							self    ,a_=3    ,a_=64    ,a_=[256, 512, 1024, 2048]    ,a_=[3, 4, 6, 3]    ,a_="preactivation"    ,a_="relu"    ,a_=None    ,a_=32    ,a_=0.0    ,a_=False    ,a_=32    ,a_=1    ,a_=None    ,a_=None    ,**a_    ,):







						"""simple docstring"""
						super().__init__(**a_						)
						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:
														lowerCAmelCase__		  =  global_padding.upper()
										else:
														raise ValueError(f'Padding strategy {global_padding} not supported'						)
						lowerCAmelCase__		  =  num_channels
						lowerCAmelCase__		  =  embedding_size
						lowerCAmelCase__		  =  hidden_sizes
						lowerCAmelCase__		  =  depths
						lowerCAmelCase__		  =  layer_type
						lowerCAmelCase__		  =  hidden_act
						lowerCAmelCase__		  =  global_padding
						lowerCAmelCase__		  =  num_groups
						lowerCAmelCase__		  =  drop_path_rate
						lowerCAmelCase__		  =  embedding_dynamic_padding
						lowerCAmelCase__		  =  output_stride
						lowerCAmelCase__		  =  width_factor

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





from typing import Dict

from .base import GenericTensor, Pipeline


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_=None    ,a_=None    ,a_=None    ,**a_						):







						"""simple docstring"""
						if tokenize_kwargs is None:
										lowerCAmelCase__		  =  {}

						if truncation is not None:
										if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'						)
										lowerCAmelCase__		  =  truncation

						lowerCAmelCase__		  =  tokenize_kwargs

						lowerCAmelCase__		  =  {}
						if return_tensors is not None:
										lowerCAmelCase__		  =  return_tensors

						return preprocess_params, {}, postprocess_params
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,**a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.framework
						lowerCAmelCase__		  =  self.tokenizer(a_    ,return_tensors=a_    ,**a_						)
						return model_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model(**a_						)
						return model_outputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=False						):







						"""simple docstring"""
						# [0] is the first available tensor, logits or last_hidden_state.
						if return_tensors:
										return model_outputs[0]
						if self.framework == "pt":
										return model_outputs[0].tolist()
						elif self.framework == "tf":
										return model_outputs[0].numpy().tolist()



		def __call__(							self    ,*a_    ,**a_						):







						"""simple docstring"""
						return super().__call__(*a_    ,**a_						)
