



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


a_						:     Union[str, Any]	     =		logging.get_logger(__name__)

# General docstring
a_						:     Union[str, Any]	     =		'ResNetConfig'

# Base docstring
a_						:     List[Any]	     =		'microsoft/resnet-50'
a_						:     Union[str, Any]	     =		[1, 2_0_4_8, 7, 7]

# Image classification docstring
a_						:     List[Any]	     =		'microsoft/resnet-50'
a_						:     List[Any]	     =		'tiger cat'

a_						:     List[str]	     =		[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



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






           '''simple docstring'''



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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.convolution(__a    )
           lowerCamelCase								=		self.normalization(__a    )
           lowerCamelCase								=		self.activation(__a    )
           return hidden_state





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		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."    )
           lowerCamelCase								=		self.embedder(__a    )
           lowerCamelCase								=		self.pooler(__a    )
           return embedding





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



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






           '''simple docstring'''



           super().__init__()
           lowerCamelCase								=		nn.Convad(__a				,			__a				,			kernel_size=1				,			stride=__a				,			bias=__a    )
           lowerCamelCase								=		nn.BatchNormad(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.convolution(__a    )
           lowerCamelCase								=		self.normalization(__a    )
           return hidden_state





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



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






           '''simple docstring'''



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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		hidden_state
           lowerCamelCase								=		self.layer(__a    )
           lowerCamelCase								=		self.shortcut(__a    )
           hidden_state += residual
           lowerCamelCase								=		self.activation(__a    )
           return hidden_state





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



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






           '''simple docstring'''



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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		hidden_state
           lowerCamelCase								=		self.layer(__a    )
           lowerCamelCase								=		self.shortcut(__a    )
           hidden_state += residual
           lowerCamelCase								=		self.activation(__a    )
           return hidden_state





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



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






           '''simple docstring'''



           super().__init__()

           lowerCamelCase								=		ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer

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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		input
           for layer in self.layers:
                 lowerCamelCase								=		layer(__a    )
           return hidden_state





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			__a				,			__a = False				,			__a = True    ):






           '''simple docstring'''



           lowerCamelCase								=		() if output_hidden_states else None

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

                 lowerCamelCase								=		stage_module(__a    )

           if output_hidden_states:
                 lowerCamelCase								=		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=__a				,			hidden_states=__a				,			)





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			ResNetConfig
     _A       =			'resnet'
     _A       =			'pixel_values'
     _A       =			True







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			__a				,			__a=False    ):






           '''simple docstring'''



           if isinstance(__a				,			__a    ):
                 lowerCamelCase								=		value



a_						:     Union[str, Any]	     =		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'

a_						:     Union[str, Any]	     =		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.'       ,    UpperCAmelCase_       ,    )
class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



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







     @add_start_docstrings_to_model_forward(__a    )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC				,			output_type=__a				,			config_class=_CONFIG_FOR_DOC				,			modality="vision"				,			expected_output=_EXPECTED_OUTPUT_SHAPE				,			)
     def 						_a      (self				,			__a				,			__a = None				,			__a = None    ):






           '''simple docstring'''



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

           lowerCamelCase								=		self.embedder(__a    )

           lowerCamelCase								=		self.encoder(
               __a				,			output_hidden_states=__a				,			return_dict=__a    )

           lowerCamelCase								=		encoder_outputs[0]

           lowerCamelCase								=		self.pooler(__a    )

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

           return BaseModelOutputWithPoolingAndNoAttention(
               last_hidden_state=__a				,			pooler_output=__a				,			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    '       ,    UpperCAmelCase_       ,    )
class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



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






           '''simple docstring'''



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

           lowerCamelCase								=		self.resnet(__a				,			output_hidden_states=__a				,			return_dict=__a    )

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

           lowerCamelCase								=		self.classifier(__a    )

           lowerCamelCase								=		None

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

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

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





@add_start_docstrings(
    '\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    '       ,    UpperCAmelCase_       ,    )
class    lowerCamelCase__  (						UpperCAmelCase_       ,    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



           super().__init__(__a    )
           super()._init_backbone(__a    )

           lowerCamelCase								=		[config.embedding_size] + config.hidden_sizes
           lowerCamelCase								=		ResNetEmbeddings(__a    )
           lowerCamelCase								=		ResNetEncoder(__a    )

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







     @add_start_docstrings_to_model_forward(__a    )
     @replace_return_docstrings(output_type=__a				,			config_class=_CONFIG_FOR_DOC    )
     def 						_a      (self				,			__a				,			__a = None				,			__a = None    ):






           '''simple docstring'''



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

           lowerCamelCase								=		self.embedder(__a    )

           lowerCamelCase								=		self.encoder(__a				,			output_hidden_states=__a				,			return_dict=__a    )

           lowerCamelCase								=		outputs.hidden_states

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

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

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




a_						:     List[str]	     =		'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

a_						:     Any	     =		[{'type': 'code', 'content': INSTALL_CONTENT}]
a_						:     str	     =		{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}