"""simple docstring"""




from bisect import bisect
from itertools import accumulate






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							sorted(zip(UpperCamelCase__				,			UpperCamelCase__ )				,			key=lambda UpperCamelCase__ : x[0] / x[1]				,			reverse=UpperCamelCase__ )
							A__   ,     A__		   =							[i[0] for i in r], [i[1] for i in r]
							A__		   =							list(accumulate(UpperCamelCase__ ) )
							A__		   =							bisect(UpperCamelCase__				,			UpperCamelCase__ )
							return (
							    0
							    if k == 0
							    else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
							    if k != n
							    else sum(vl[:k] )
							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






"""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 ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


__lowerCamelCase							 =     logging.get_logger(__name__)

# General docstring
__lowerCamelCase							 =     "ResNetConfig"

# Base docstring
__lowerCamelCase							 =     "microsoft/resnet-50"
__lowerCamelCase							 =     [1, 20_48, 7, 7]

# Image classification docstring
__lowerCamelCase							 =     "microsoft/resnet-50"
__lowerCamelCase							 =     "tiger cat"

__lowerCamelCase							 =     [
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]
class 			UpperCamelCase__(	nn.Module       ):





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





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													A__		   =							self.convolution(__UpperCAmelCase   )
													A__		   =							self.normalization(__UpperCAmelCase   )
													A__		   =							self.activation(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Any:
													super().__init__()
													A__		   =							ResNetConvLayer(
													    config.num_channels						,config.embedding_size						,kernel_size=7						,stride=2						,activation=config.hidden_act   )
													A__		   =							nn.MaxPoolad(kernel_size=3						,stride=2						,padding=1   )
													A__		   =							config.num_channels





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													A__		   =							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.'   )
													A__		   =							self.embedder(__UpperCAmelCase   )
													A__		   =							self.pooler(__UpperCAmelCase   )
													return embedding






class 			UpperCamelCase__(	nn.Module       ):





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





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													A__		   =							self.convolution(__UpperCAmelCase   )
													A__		   =							self.normalization(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 1						,__UpperCAmelCase = "relu"   )    -> int:
													super().__init__()
													A__		   =							in_channels != out_channels or stride != 1
													A__		   =							(
													    ResNetShortCut(__UpperCAmelCase						,__UpperCAmelCase						,stride=__UpperCAmelCase   ) if should_apply_shortcut else nn.Identity()
													)
													A__		   =							nn.Sequential(
													    ResNetConvLayer(__UpperCAmelCase						,__UpperCAmelCase						,stride=__UpperCAmelCase   )						,ResNetConvLayer(__UpperCAmelCase						,__UpperCAmelCase						,activation=__UpperCAmelCase   )						,)
													A__		   =							ACTaFN[activation]





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							hidden_state
													A__		   =							self.layer(__UpperCAmelCase   )
													A__		   =							self.shortcut(__UpperCAmelCase   )
													hidden_state += residual
													A__		   =							self.activation(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 1						,__UpperCAmelCase = "relu"						,__UpperCAmelCase = 4   )    -> int:
													super().__init__()
													A__		   =							in_channels != out_channels or stride != 1
													A__		   =							out_channels // reduction
													A__		   =							(
													    ResNetShortCut(__UpperCAmelCase						,__UpperCAmelCase						,stride=__UpperCAmelCase   ) if should_apply_shortcut else nn.Identity()
													)
													A__		   =							nn.Sequential(
													    ResNetConvLayer(__UpperCAmelCase						,__UpperCAmelCase						,kernel_size=1   )						,ResNetConvLayer(__UpperCAmelCase						,__UpperCAmelCase						,stride=__UpperCAmelCase   )						,ResNetConvLayer(__UpperCAmelCase						,__UpperCAmelCase						,kernel_size=1						,activation=__UpperCAmelCase   )						,)
													A__		   =							ACTaFN[activation]





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							hidden_state
													A__		   =							self.layer(__UpperCAmelCase   )
													A__		   =							self.shortcut(__UpperCAmelCase   )
													hidden_state += residual
													A__		   =							self.activation(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,)    -> Any:
													super().__init__()

													A__		   =							ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

													A__		   =							nn.Sequential(
													    # downsampling is done in the first layer with stride of 2
													    layer(__UpperCAmelCase						,__UpperCAmelCase						,stride=__UpperCAmelCase						,activation=config.hidden_act   )						,*[layer(__UpperCAmelCase						,__UpperCAmelCase						,activation=config.hidden_act   ) for _ in range(depth - 1   )]						,)





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													A__		   =							input
													for layer in self.layers:
																				A__		   =							layer(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													super().__init__()
													A__		   =							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(
													    ResNetStage(
													        __UpperCAmelCase						,config.embedding_size						,config.hidden_sizes[0]						,stride=2 if config.downsample_in_first_stage else 1						,depth=config.depths[0]						,)   )
													A__		   =							zip(config.hidden_sizes						,config.hidden_sizes[1:]   )
													for (in_channels, out_channels), depth in zip(__UpperCAmelCase						,config.depths[1:]   ):
																				self.stages.append(ResNetStage(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,depth=__UpperCAmelCase   )   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = False						,__UpperCAmelCase = True   )    -> BaseModelOutputWithNoAttention:
													A__		   =							() if output_hidden_states else None

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

																				A__		   =							stage_module(__UpperCAmelCase   )

													if output_hidden_states:
																				A__		   =							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=__UpperCAmelCase						,hidden_states=__UpperCAmelCase						,)






class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  str  							=						ResNetConfig
						lowerCAmelCase__     :  str  							=						'resnet'
						lowerCAmelCase__     :  int  							=						'pixel_values'
						lowerCAmelCase__     :  Any  							=						True





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





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=False   )    -> Any:
													if isinstance(__UpperCAmelCase						,__UpperCAmelCase   ):
																				A__		   =							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 ([`ResNetConfig`]): 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 [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.'						,  __A						,  )
class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Union[str, Any]:
													super().__init__(__UpperCAmelCase   )
													A__		   =							config
													A__		   =							ResNetEmbeddings(__UpperCAmelCase   )
													A__		   =							ResNetEncoder(__UpperCAmelCase   )
													A__		   =							nn.AdaptiveAvgPoolad((1, 1)   )
													# Initialize weights and apply final processing
													self.post_init()





						@add_start_docstrings_to_model_forward(__UpperCAmelCase   )
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC						,output_type=__UpperCAmelCase						,config_class=_CONFIG_FOR_DOC						,modality='vision'						,expected_output=_EXPECTED_OUTPUT_SHAPE						,)
						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None   )    -> BaseModelOutputWithPoolingAndNoAttention:
													A__		   =							(
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													A__		   =							return_dict if return_dict is not None else self.config.use_return_dict

													A__		   =							self.embedder(__UpperCAmelCase   )

													A__		   =							self.encoder(
													    __UpperCAmelCase						,output_hidden_states=__UpperCAmelCase						,return_dict=__UpperCAmelCase   )

													A__		   =							encoder_outputs[0]

													A__		   =							self.pooler(__UpperCAmelCase   )

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

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






@add_start_docstrings(
    '\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '						,  __A						,  )
class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Tuple:
													super().__init__(__UpperCAmelCase   )
													A__		   =							config.num_labels
													A__		   =							ResNetModel(__UpperCAmelCase   )
													# classification head
													A__		   =							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(__UpperCAmelCase   )
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT						,output_type=__UpperCAmelCase						,config_class=_CONFIG_FOR_DOC						,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,)
						def  snake_case__		(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,)    -> ImageClassifierOutputWithNoAttention:
													A__		   =							return_dict if return_dict is not None else self.config.use_return_dict

													A__		   =							self.resnet(__UpperCAmelCase						,output_hidden_states=__UpperCAmelCase						,return_dict=__UpperCAmelCase   )

													A__		   =							outputs.pooler_output if return_dict else outputs[1]

													A__		   =							self.classifier(__UpperCAmelCase   )

													A__		   =							None

													if labels is not None:
																				if self.config.problem_type is None:
																											if self.num_labels == 1:
																																		A__		   =							'regression'
																											elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																																		A__		   =							'single_label_classification'
																											else:
																																		A__		   =							'multi_label_classification'
																				if self.config.problem_type == "regression":
																											A__		   =							MSELoss()
																											if self.num_labels == 1:
																																		A__		   =							loss_fct(logits.squeeze()						,labels.squeeze()   )
																											else:
																																		A__		   =							loss_fct(__UpperCAmelCase						,__UpperCAmelCase   )
																				elif self.config.problem_type == "single_label_classification":
																											A__		   =							CrossEntropyLoss()
																											A__		   =							loss_fct(logits.view(-1						,self.num_labels   )						,labels.view(-1   )   )
																				elif self.config.problem_type == "multi_label_classification":
																											A__		   =							BCEWithLogitsLoss()
																											A__		   =							loss_fct(__UpperCAmelCase						,__UpperCAmelCase   )

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

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






@add_start_docstrings(
    '\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    '						,  __A						,  )
class 			UpperCamelCase__(	__A						,  __A       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													super().__init__(__UpperCAmelCase   )
													super()._init_backbone(__UpperCAmelCase   )

													A__		   =							[config.embedding_size] + config.hidden_sizes
													A__		   =							ResNetEmbeddings(__UpperCAmelCase   )
													A__		   =							ResNetEncoder(__UpperCAmelCase   )

													# initialize weights and apply final processing
													self.post_init()





						@add_start_docstrings_to_model_forward(__UpperCAmelCase   )
						@replace_return_docstrings(output_type=__UpperCAmelCase						,config_class=_CONFIG_FOR_DOC   )
						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None   )    -> BackboneOutput:
													A__		   =							return_dict if return_dict is not None else self.config.use_return_dict
													A__		   =							(
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)

													A__		   =							self.embedder(__UpperCAmelCase   )

													A__		   =							self.encoder(__UpperCAmelCase						,output_hidden_states=__UpperCAmelCase						,return_dict=__UpperCAmelCase   )

													A__		   =							outputs.hidden_states

													A__		   =							()
													for idx, stage in enumerate(self.stage_names   ):
																				if stage in self.out_features:
																											feature_maps += (hidden_states[idx],)

													if not return_dict:
																				A__		   =							(feature_maps,)
																				if output_hidden_states:
																											output += (outputs.hidden_states,)
																				return output

													return BackboneOutput(
													    feature_maps=__UpperCAmelCase						,hidden_states=outputs.hidden_states if output_hidden_states else None						,attentions=__UpperCAmelCase						,)





