"""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


SCREAMING_SNAKE_CASE__					  =     logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE__					  =     "ResNetConfig"

# Base docstring
SCREAMING_SNAKE_CASE__					  =     "microsoft/resnet-50"
SCREAMING_SNAKE_CASE__					  =     [1, 2_048, 7, 7]

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

SCREAMING_SNAKE_CASE__					  =     [
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]



class      lowerCAmelCase_			(	nn.Module    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase = 3						,			lowerCAmelCase = 1						,			lowerCAmelCase = "relu"			):






										"""simple docstring"""

										super().__init__()
										snake_case	    =	nn.Convad(
										    lowerCAmelCase						,			lowerCAmelCase						,			kernel_size=lowerCAmelCase						,			stride=lowerCAmelCase						,			padding=kernel_size // 2						,			bias=lowerCAmelCase			)
										snake_case	    =	nn.BatchNormad(lowerCAmelCase			)
										snake_case	    =	ACTaFN[activation] if activation is not None else nn.Identity()

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	self.convolution(lowerCAmelCase			)
										snake_case	    =	self.normalization(lowerCAmelCase			)
										snake_case	    =	self.activation(lowerCAmelCase			)
										return hidden_state





class      lowerCAmelCase_			(	nn.Module    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase			):






										"""simple docstring"""

										super().__init__()
										snake_case	    =	ResNetConvLayer(
										    config.num_channels						,			config.embedding_size						,			kernel_size=7						,			stride=2						,			activation=config.hidden_act			)
										snake_case	    =	nn.MaxPoolad(kernel_size=3						,			stride=2						,			padding=1			)
										snake_case	    =	config.num_channels

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	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.'			)
										snake_case	    =	self.embedder(lowerCAmelCase			)
										snake_case	    =	self.pooler(lowerCAmelCase			)
										return embedding





class      lowerCAmelCase_			(	nn.Module    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase = 2			):






										"""simple docstring"""

										super().__init__()
										snake_case	    =	nn.Convad(lowerCAmelCase						,			lowerCAmelCase						,			kernel_size=1						,			stride=lowerCAmelCase						,			bias=lowerCAmelCase			)
										snake_case	    =	nn.BatchNormad(lowerCAmelCase			)

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	self.convolution(lowerCAmelCase			)
										snake_case	    =	self.normalization(lowerCAmelCase			)
										return hidden_state





class      lowerCAmelCase_			(	nn.Module    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase = 1						,			lowerCAmelCase = "relu"			):






										"""simple docstring"""

										super().__init__()
										snake_case	    =	in_channels != out_channels or stride != 1
										snake_case	    =	(
										    ResNetShortCut(lowerCAmelCase						,			lowerCAmelCase						,			stride=lowerCAmelCase			) if should_apply_shortcut else nn.Identity()
										)
										snake_case	    =	nn.Sequential(
										    ResNetConvLayer(lowerCAmelCase						,			lowerCAmelCase						,			stride=lowerCAmelCase			)						,			ResNetConvLayer(lowerCAmelCase						,			lowerCAmelCase						,			activation=lowerCAmelCase			)						,			)
										snake_case	    =	ACTaFN[activation]

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	hidden_state
										snake_case	    =	self.layer(lowerCAmelCase			)
										snake_case	    =	self.shortcut(lowerCAmelCase			)
										hidden_state += residual
										snake_case	    =	self.activation(lowerCAmelCase			)
										return hidden_state





class      lowerCAmelCase_			(	nn.Module    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase = 1						,			lowerCAmelCase = "relu"						,			lowerCAmelCase = 4			):






										"""simple docstring"""

										super().__init__()
										snake_case	    =	in_channels != out_channels or stride != 1
										snake_case	    =	out_channels // reduction
										snake_case	    =	(
										    ResNetShortCut(lowerCAmelCase						,			lowerCAmelCase						,			stride=lowerCAmelCase			) if should_apply_shortcut else nn.Identity()
										)
										snake_case	    =	nn.Sequential(
										    ResNetConvLayer(lowerCAmelCase						,			lowerCAmelCase						,			kernel_size=1			)						,			ResNetConvLayer(lowerCAmelCase						,			lowerCAmelCase						,			stride=lowerCAmelCase			)						,			ResNetConvLayer(lowerCAmelCase						,			lowerCAmelCase						,			kernel_size=1						,			activation=lowerCAmelCase			)						,			)
										snake_case	    =	ACTaFN[activation]

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	hidden_state
										snake_case	    =	self.layer(lowerCAmelCase			)
										snake_case	    =	self.shortcut(lowerCAmelCase			)
										hidden_state += residual
										snake_case	    =	self.activation(lowerCAmelCase			)
										return hidden_state





class      lowerCAmelCase_			(	nn.Module    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase = 2						,			lowerCAmelCase = 2						,			):






										"""simple docstring"""

										super().__init__()

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

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

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	input
										for layer in self.layers:
																snake_case	    =	layer(lowerCAmelCase			)
										return hidden_state





class      lowerCAmelCase_			(	nn.Module    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase			):






										"""simple docstring"""

										super().__init__()
										snake_case	    =	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(
										        lowerCAmelCase						,			config.embedding_size						,			config.hidden_sizes[0]						,			stride=2 if config.downsample_in_first_stage else 1						,			depth=config.depths[0]						,			)			)
										snake_case	    =	zip(config.hidden_sizes						,			config.hidden_sizes[1:]			)
										for (in_channels, out_channels), depth in zip(lowerCAmelCase						,			config.depths[1:]			):
																self.stages.append(ResNetStage(lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase						,			depth=lowerCAmelCase			)			)

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase = False						,			lowerCAmelCase = True			):






										"""simple docstring"""

										snake_case	    =	() if output_hidden_states else None

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

																snake_case	    =	stage_module(lowerCAmelCase			)

										if output_hidden_states:
																snake_case	    =	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=lowerCAmelCase						,			hidden_states=lowerCAmelCase						,			)





class      lowerCAmelCase_			(	__snake_case    ):



				"""simple docstring"""
				_lowerCAmelCase   :			Union[str, Any]	     =  ResNetConfig
				_lowerCAmelCase   :			Tuple	     =  """resnet"""
				_lowerCAmelCase   :			Union[str, Any]	     =  """pixel_values"""
				_lowerCAmelCase   :			int	     =  True

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										if isinstance(lowerCAmelCase						,			nn.Convad			):
																nn.init.kaiming_normal_(module.weight						,			mode='fan_out'						,			nonlinearity='relu'			)
										elif isinstance(lowerCAmelCase						,			(nn.BatchNormad, nn.GroupNorm)			):
																nn.init.constant_(module.weight						,			1			)
																nn.init.constant_(module.bias						,			0			)

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase=False			):






										"""simple docstring"""

										if isinstance(lowerCAmelCase						,			lowerCAmelCase			):
																snake_case	    =	value





SCREAMING_SNAKE_CASE__					  =     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"

SCREAMING_SNAKE_CASE__					  =     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."""   ,			__snake_case   ,			)
class      lowerCAmelCase_			(	__snake_case    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase			):






										"""simple docstring"""

										super().__init__(lowerCAmelCase			)
										snake_case	    =	config
										snake_case	    =	ResNetEmbeddings(lowerCAmelCase			)
										snake_case	    =	ResNetEncoder(lowerCAmelCase			)
										snake_case	    =	nn.AdaptiveAvgPoolad((1, 1)			)
										# Initialize weights and apply final processing
										self.post_init()

				@add_start_docstrings_to_model_forward(lowerCAmelCase			)
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC						,			output_type=lowerCAmelCase						,			config_class=_CONFIG_FOR_DOC						,			modality='vision'						,			expected_output=_EXPECTED_OUTPUT_SHAPE						,			)
				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase = None						,			lowerCAmelCase = None			):






										"""simple docstring"""

										snake_case	    =	(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										snake_case	    =	return_dict if return_dict is not None else self.config.use_return_dict

										snake_case	    =	self.embedder(lowerCAmelCase			)

										snake_case	    =	self.encoder(
										    lowerCAmelCase						,			output_hidden_states=lowerCAmelCase						,			return_dict=lowerCAmelCase			)

										snake_case	    =	encoder_outputs[0]

										snake_case	    =	self.pooler(lowerCAmelCase			)

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

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





@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """   ,			__snake_case   ,			)
class      lowerCAmelCase_			(	__snake_case    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase			):






										"""simple docstring"""

										super().__init__(lowerCAmelCase			)
										snake_case	    =	config.num_labels
										snake_case	    =	ResNetModel(lowerCAmelCase			)
										# classification head
										snake_case	    =	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(lowerCAmelCase			)
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT						,			output_type=lowerCAmelCase						,			config_class=_CONFIG_FOR_DOC						,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,			)
				def 						snake_case       ( self						,			lowerCAmelCase = None						,			lowerCAmelCase = None						,			lowerCAmelCase = None						,			lowerCAmelCase = None						,			):






										"""simple docstring"""

										snake_case	    =	return_dict if return_dict is not None else self.config.use_return_dict

										snake_case	    =	self.resnet(lowerCAmelCase						,			output_hidden_states=lowerCAmelCase						,			return_dict=lowerCAmelCase			)

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

										snake_case	    =	self.classifier(lowerCAmelCase			)

										snake_case	    =	None

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

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

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





@add_start_docstrings(
    """
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    """   ,			__snake_case   ,			)
class      lowerCAmelCase_			(	__snake_case   ,			__snake_case    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase			):






										"""simple docstring"""

										super().__init__(lowerCAmelCase			)
										super()._init_backbone(lowerCAmelCase			)

										snake_case	    =	[config.embedding_size] + config.hidden_sizes
										snake_case	    =	ResNetEmbeddings(lowerCAmelCase			)
										snake_case	    =	ResNetEncoder(lowerCAmelCase			)

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

				@add_start_docstrings_to_model_forward(lowerCAmelCase			)
				@replace_return_docstrings(output_type=lowerCAmelCase						,			config_class=_CONFIG_FOR_DOC			)
				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase = None						,			lowerCAmelCase = None			):






										"""simple docstring"""

										snake_case	    =	return_dict if return_dict is not None else self.config.use_return_dict
										snake_case	    =	(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)

										snake_case	    =	self.embedder(lowerCAmelCase			)

										snake_case	    =	self.encoder(lowerCAmelCase						,			output_hidden_states=lowerCAmelCase						,			return_dict=lowerCAmelCase			)

										snake_case	    =	outputs.hidden_states

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

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

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





from __future__ import annotations

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    dict	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__      , lowercase__    :   List[str]      =       set(SCREAMING_SNAKE_CASE_			), [start]

     while stack:
          lowercase__    :   Union[str, Any]      =       stack.pop()
          explored.add(SCREAMING_SNAKE_CASE_			)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]			):
               if adj not in explored:
                    stack.append(SCREAMING_SNAKE_CASE_			)
     return explored


snake_case_			  = {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, '''A'''))

