





from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]    ):
		for param in module.parameters():
				SCREAMING_SNAKE_CASE__			=      False






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      """cuda""" if torch.cuda.is_available() else """cpu"""
		if torch.backends.mps.is_available() and torch.backends.mps.is_built():
				SCREAMING_SNAKE_CASE__			=      """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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[Any]    ):
		SCREAMING_SNAKE_CASE__			=      plt.imshow(UpperCamelCase__    )
		fig.axes.get_xaxis().set_visible(UpperCamelCase__    )
		fig.axes.get_yaxis().set_visible(UpperCamelCase__    )
		plt.show()






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      datetime.now()
		SCREAMING_SNAKE_CASE__			=      current_time.strftime("""%H:%M:%S"""    )
		return timestamp






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




class   UpperCamelCase_       (     UpperCamelCase__				,  UpperCamelCase__    ):
							lowerCamelCase_           =					"nat"

							lowerCamelCase_           =					{
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(							self    :List[Any]							,				__A    :Optional[Any]=4							,				__A    :Any=3							,				__A    :Optional[int]=64							,				__A    :Optional[int]=[3, 4, 6, 5]							,				__A    :Union[str, Any]=[2, 4, 8, 16]							,				__A    :Optional[Any]=7							,				__A    :Optional[Any]=3.0							,				__A    :List[Any]=True							,				__A    :int=0.0							,				__A    :Dict=0.0							,				__A    :Optional[Any]=0.1							,				__A    :str="gelu"							,				__A    :Optional[Any]=0.0_2							,				__A    :Optional[int]=1E-5							,				__A    :Optional[int]=0.0							,				__A    :Optional[Any]=None							,				__A    :Union[str, Any]=None							,				**__A    :Union[str, Any]							,				)   ->   Optional[int]:



									"""simple docstring"""



									super().__init__(**__A		)

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