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


lowerCamelCase_    :		str							=	logging.get_logger(__name__)

lowerCamelCase_    :		Any							=	{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}


class 						a__							(       __snake_case				,						__snake_case		):
			A__    :       Optional[Any]        =      'bit'
			A__    :       Dict        =      ['preactivation', 'bottleneck']
			A__    :       Dict        =      ['SAME', 'VALID']



			def __init__(						self				,						UpperCAmelCase=3				,						UpperCAmelCase=6_4				,						UpperCAmelCase=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,						UpperCAmelCase=[3, 4, 6, 3]				,						UpperCAmelCase="preactivation"				,						UpperCAmelCase="relu"				,						UpperCAmelCase=None				,						UpperCAmelCase=3_2				,						UpperCAmelCase=0.0				,						UpperCAmelCase=False				,						UpperCAmelCase=3_2				,						UpperCAmelCase=1				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						**UpperCAmelCase				,						)			->   Union[str, 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:
																		__a		 =       global_padding.upper()
													else:
																		raise ValueError(f'''Padding strategy {global_padding} not supported'''   )
								__a		 =       num_channels
								__a		 =       embedding_size
								__a		 =       hidden_sizes
								__a		 =       depths
								__a		 =       layer_type
								__a		 =       hidden_act
								__a		 =       global_padding
								__a		 =       num_groups
								__a		 =       drop_path_rate
								__a		 =       embedding_dynamic_padding
								__a		 =       output_stride
								__a		 =       width_factor

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





from datetime import datetime

import matplotlib.pyplot as plt
import torch
def 							lowerCAmelCase(     __lowerCamelCase						):
					for param in module.parameters():
										__a		 =       False
def 							lowerCAmelCase(     ):
					__a		 =       'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										__a		 =       'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'						)
					return device
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       plt.imshow(__lowerCamelCase						)
					fig.axes.get_xaxis().set_visible(__lowerCamelCase						)
					fig.axes.get_yaxis().set_visible(__lowerCamelCase						)
					plt.show()



def 							lowerCAmelCase(     ):
					__a		 =       datetime.now()
					__a		 =       current_time.strftime('%H:%M:%S'						)
					return timestamp




