

'''simple docstring'''


lowerCAmelCase__										= 0  # The first color of the flag.
lowerCAmelCase__										= 1  # The second color of the flag.
lowerCAmelCase__										= 2  # The third color of the flag.
lowerCAmelCase__										= (red, white, blue)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    list:
	if not sequence:
		return []
	if len(UpperCamelCase  ) == 1:
		return list(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					0
	UpperCAmelCase_		:      Optional[Any]       	=					len(UpperCamelCase  ) - 1
	UpperCAmelCase_		:      int       	=					0
	while mid <= high:
		if sequence[mid] == colors[0]:
			UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					sequence[mid], sequence[low]
			low += 1
			mid += 1
		elif sequence[mid] == colors[1]:
			mid += 1
		elif sequence[mid] == colors[2]:
			UpperCAmelCase_	,    UpperCAmelCase_		:      str       	=					sequence[high], sequence[mid]
			high -= 1
		else:
			UpperCAmelCase_		:      List[Any]       	=					f"""The elements inside the sequence must contains only {colors} values"""
			raise ValueError(UpperCamelCase  )
	return sequence


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						lowerCAmelCase__										= input("Enter numbers separated by commas:\n").strip()
						lowerCAmelCase__										= [int(item.strip()) for item in user_input.split(",")]
						print(F'{dutch_national_flag_sort(unsorted)}')









'''simple docstring'''


from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCAmelCase__										= logging.get_logger(__name__)

# General docstring
lowerCAmelCase__										= "RegNetConfig"

# Base docstring
lowerCAmelCase__										= "facebook/regnet-y-040"
lowerCAmelCase__										= [1, 1088, 7, 7]

# Image classification docstring
lowerCAmelCase__										= "facebook/regnet-y-040"
lowerCAmelCase__										= "tabby, tabby cat"

lowerCAmelCase__										= [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case = 3    ,			_snake_case = 1    ,			_snake_case = 1    ,			_snake_case = "relu"    ,			)							->				int:
								super().__init__()
								UpperCAmelCase_		:      str       	=					nn.Convad(
								    _snake_case    ,			_snake_case    ,			kernel_size=_snake_case    ,			stride=_snake_case    ,			padding=kernel_size // 2    ,			groups=_snake_case    ,			bias=_snake_case    ,			)
								UpperCAmelCase_		:      List[Any]       	=					nn.BatchNormad(_snake_case)
								UpperCAmelCase_		:      Tuple       	=					ACTaFN[activation] if activation is not None else nn.Identity()





							def 						_snake_case							(			self    ,			_snake_case)							->				Tuple:
								UpperCAmelCase_		:      Optional[int]       	=					self.convolution(_snake_case)
								UpperCAmelCase_		:      int       	=					self.normalization(_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case)							->				List[Any]:
								super().__init__()
								UpperCAmelCase_		:      Tuple       	=					RegNetConvLayer(
								    config.num_channels    ,			config.embedding_size    ,			kernel_size=3    ,			stride=2    ,			activation=config.hidden_act)
								UpperCAmelCase_		:      Optional[Any]       	=					config.num_channels





							def 						_snake_case							(			self    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      List[Any]       	=					pixel_values.shape[1]
								if num_channels != self.num_channels:
									raise ValueError(
									    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.')
								UpperCAmelCase_		:      Any       	=					self.embedder(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case = 2)							->				Optional[Any]:
								super().__init__()
								UpperCAmelCase_		:      Any       	=					nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			stride=_snake_case    ,			bias=_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					nn.BatchNormad(_snake_case)





							def 						_snake_case							(			self    ,			_snake_case)							->				Tensor:
								UpperCAmelCase_		:      Optional[Any]       	=					self.convolution(_snake_case)
								UpperCAmelCase_		:      Dict       	=					self.normalization(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				Any:
								super().__init__()

								UpperCAmelCase_		:      Tuple       	=					nn.AdaptiveAvgPoolad((1, 1))
								UpperCAmelCase_		:      int       	=					nn.Sequential(
								    nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1)    ,			nn.ReLU()    ,			nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1)    ,			nn.Sigmoid()    ,			)





							def 						_snake_case							(			self    ,			_snake_case)							->				Any:
								# b c h w -> b c 1 1
								UpperCAmelCase_		:      Union[str, Any]       	=					self.pooler(_snake_case)
								UpperCAmelCase_		:      Any       	=					self.attention(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					hidden_state * attention
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 1)							->				str:
								super().__init__()
								UpperCAmelCase_		:      Optional[Any]       	=					in_channels != out_channels or stride != 1
								UpperCAmelCase_		:      Any       	=					max(1    ,			out_channels // config.groups_width)
								UpperCAmelCase_		:      str       	=					(
								    RegNetShortCut(_snake_case    ,			_snake_case    ,			stride=_snake_case) if should_apply_shortcut else nn.Identity()
								)
								UpperCAmelCase_		:      Optional[int]       	=					nn.Sequential(
								    RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			stride=_snake_case    ,			groups=_snake_case    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=_snake_case)    ,			)
								UpperCAmelCase_		:      int       	=					ACTaFN[config.hidden_act]





							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					hidden_state
								UpperCAmelCase_		:      List[Any]       	=					self.layer(_snake_case)
								UpperCAmelCase_		:      Dict       	=					self.shortcut(_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:      Any       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 1)							->				int:
								super().__init__()
								UpperCAmelCase_		:      List[Any]       	=					in_channels != out_channels or stride != 1
								UpperCAmelCase_		:      Optional[Any]       	=					max(1    ,			out_channels // config.groups_width)
								UpperCAmelCase_		:      Optional[int]       	=					(
								    RegNetShortCut(_snake_case    ,			_snake_case    ,			stride=_snake_case) if should_apply_shortcut else nn.Identity()
								)
								UpperCAmelCase_		:      Optional[Any]       	=					nn.Sequential(
								    RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			stride=_snake_case    ,			groups=_snake_case    ,			activation=config.hidden_act)    ,			RegNetSELayer(_snake_case    ,			reduced_channels=int(round(in_channels / 4)))    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=_snake_case)    ,			)
								UpperCAmelCase_		:      Any       	=					ACTaFN[config.hidden_act]





							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      List[str]       	=					hidden_state
								UpperCAmelCase_		:      int       	=					self.layer(_snake_case)
								UpperCAmelCase_		:      Any       	=					self.shortcut(_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:      Any       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 2    ,			_snake_case = 2    ,			)							->				Optional[int]:
								super().__init__()

								UpperCAmelCase_		:      str       	=					RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

								UpperCAmelCase_		:      Dict       	=					nn.Sequential(
								    # downsampling is done in the first layer with stride of 2
								    layer(
								        _snake_case    ,			_snake_case    ,			_snake_case    ,			stride=_snake_case    ,			)    ,			*[layer(_snake_case    ,			_snake_case    ,			_snake_case) for _ in range(depth - 1)]    ,			)





							def 						_snake_case							(			self    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      Optional[Any]       	=					self.layers(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case)							->				List[Any]:
								super().__init__()
								UpperCAmelCase_		:      List[str]       	=					nn.ModuleList([])
								# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
								self.stages.append(
								    RegNetStage(
								        _snake_case    ,			config.embedding_size    ,			config.hidden_sizes[0]    ,			stride=2 if config.downsample_in_first_stage else 1    ,			depth=config.depths[0]    ,			))
								UpperCAmelCase_		:      Dict       	=					zip(config.hidden_sizes    ,			config.hidden_sizes[1:])
								for (in_channels, out_channels), depth in zip(_snake_case    ,			config.depths[1:]):
									self.stages.append(RegNetStage(_snake_case    ,			_snake_case    ,			_snake_case    ,			depth=_snake_case))





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = False    ,			_snake_case = True)							->				BaseModelOutputWithNoAttention:
								UpperCAmelCase_		:      List[Any]       	=					() if output_hidden_states else None

								for stage_module in self.stages:
									if output_hidden_states:
										UpperCAmelCase_		:      int       	=					hidden_states + (hidden_state,)

									UpperCAmelCase_		:      Tuple       	=					stage_module(_snake_case)

								if output_hidden_states:
									UpperCAmelCase_		:      Union[str, Any]       	=					hidden_states + (hidden_state,)

								if not return_dict:
									return tuple(v for v in [hidden_state, hidden_states] if v is not None)

								return BaseModelOutputWithNoAttention(last_hidden_state=_snake_case    ,			hidden_states=_snake_case)



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      str=   RegNetConfig
							_lowerCamelCase		:      Optional[int]=   "regnet"
							_lowerCamelCase		:      Union[str, Any]=   "pixel_values"
							_lowerCamelCase		:      List[str]=   True


							def 						_snake_case							(			self    ,			_snake_case)							->				List[Any]:
								if isinstance(_snake_case    ,			nn.Convad):
									nn.init.kaiming_normal_(module.weight    ,			mode='fan_out'    ,			nonlinearity='relu')
								elif isinstance(_snake_case    ,			(nn.BatchNormad, nn.GroupNorm)):
									nn.init.constant_(module.weight    ,			1)
									nn.init.constant_(module.bias    ,			0)





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=False)							->				List[Any]:
								if isinstance(_snake_case    ,			_snake_case):
									UpperCAmelCase_		:      str       	=					value


lowerCAmelCase__										= r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

lowerCAmelCase__										= r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n"



@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top.",					a_,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case)							->				List[str]:
								super().__init__(_snake_case)
								UpperCAmelCase_		:      str       	=					config
								UpperCAmelCase_		:      Optional[int]       	=					RegNetEmbeddings(_snake_case)
								UpperCAmelCase_		:      int       	=					RegNetEncoder(_snake_case)
								UpperCAmelCase_		:      Any       	=					nn.AdaptiveAvgPoolad((1, 1))
								# Initialize weights and apply final processing
								self.post_init()





							@add_start_docstrings_to_model_forward(_snake_case)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC    ,			output_type=_snake_case    ,			config_class=_CONFIG_FOR_DOC    ,			modality='vision'    ,			expected_output=_EXPECTED_OUTPUT_SHAPE    ,			)
							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None    ,			_snake_case = None)							->				BaseModelOutputWithPoolingAndNoAttention:
								UpperCAmelCase_		:      List[Any]       	=					(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase_		:      Optional[Any]       	=					return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:      int       	=					self.embedder(_snake_case)

								UpperCAmelCase_		:      str       	=					self.encoder(
								    _snake_case    ,			output_hidden_states=_snake_case    ,			return_dict=_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					encoder_outputs[0]

								UpperCAmelCase_		:      str       	=					self.pooler(_snake_case)

								if not return_dict:
									return (last_hidden_state, pooled_output) + encoder_outputs[1:]

								return BaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=_snake_case    ,			pooler_output=_snake_case    ,			hidden_states=encoder_outputs.hidden_states    ,			)



@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    ",					a_,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case)							->				Union[str, Any]:
								super().__init__(_snake_case)
								UpperCAmelCase_		:      List[str]       	=					config.num_labels
								UpperCAmelCase_		:      Optional[int]       	=					RegNetModel(_snake_case)
								# classification head
								UpperCAmelCase_		:      Optional[Any]       	=					nn.Sequential(
								    nn.Flatten()    ,			nn.Linear(config.hidden_sizes[-1]    ,			config.num_labels) if config.num_labels > 0 else nn.Identity()    ,			)
								# initialize weights and apply final processing
								self.post_init()





							@add_start_docstrings_to_model_forward(_snake_case)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,			output_type=_snake_case    ,			config_class=_CONFIG_FOR_DOC    ,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,			)
							def 						_snake_case							(			self    ,			_snake_case = None    ,			_snake_case = None    ,			_snake_case = None    ,			_snake_case = None    ,			)							->				ImageClassifierOutputWithNoAttention:
								UpperCAmelCase_		:      str       	=					return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:      Optional[Any]       	=					self.regnet(_snake_case    ,			output_hidden_states=_snake_case    ,			return_dict=_snake_case)

								UpperCAmelCase_		:      Dict       	=					outputs.pooler_output if return_dict else outputs[1]

								UpperCAmelCase_		:      int       	=					self.classifier(_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					None

								if labels is not None:
									if self.config.problem_type is None:
										if self.num_labels == 1:
											UpperCAmelCase_		:      Optional[int]       	=					'regression'
										elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
											UpperCAmelCase_		:      Union[str, Any]       	=					'single_label_classification'
										else:
											UpperCAmelCase_		:      Tuple       	=					'multi_label_classification'
									if self.config.problem_type == "regression":
										UpperCAmelCase_		:      int       	=					MSELoss()
										if self.num_labels == 1:
											UpperCAmelCase_		:      Dict       	=					loss_fct(logits.squeeze()    ,			labels.squeeze())
										else:
											UpperCAmelCase_		:      Union[str, Any]       	=					loss_fct(_snake_case    ,			_snake_case)
									elif self.config.problem_type == "single_label_classification":
										UpperCAmelCase_		:      Optional[int]       	=					CrossEntropyLoss()
										UpperCAmelCase_		:      Union[str, Any]       	=					loss_fct(logits.view(-1    ,			self.num_labels)    ,			labels.view(-1))
									elif self.config.problem_type == "multi_label_classification":
										UpperCAmelCase_		:      int       	=					BCEWithLogitsLoss()
										UpperCAmelCase_		:      Tuple       	=					loss_fct(_snake_case    ,			_snake_case)

								if not return_dict:
									UpperCAmelCase_		:      Union[str, Any]       	=					(logits,) + outputs[2:]
									return (loss,) + output if loss is not None else output

								return ImageClassifierOutputWithNoAttention(loss=_snake_case    ,			logits=_snake_case    ,			hidden_states=outputs.hidden_states)






