


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import SwiftFormerForImageClassification, SwiftFormerModel
   from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor



class        a					:





     def __init__(    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple=13    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=3    ,	SCREAMING_SNAKE_CASE_					:				Any=True    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=True    ,	SCREAMING_SNAKE_CASE_					:				Dict=0.1    ,	SCREAMING_SNAKE_CASE_					:				Tuple=0.1    ,	SCREAMING_SNAKE_CASE_					:				int=224    ,	SCREAMING_SNAKE_CASE_					:				Tuple=1000    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=[3, 3, 6, 4]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=[48, 56, 112, 220]    ,	):
        __lowerCamelCase:				Optional[Any]     =	parent
        __lowerCamelCase:				Any     =	batch_size
        __lowerCamelCase:				str     =	num_channels
        __lowerCamelCase:				str     =	is_training
        __lowerCamelCase:				List[str]     =	use_labels
        __lowerCamelCase:				List[Any]     =	hidden_dropout_prob
        __lowerCamelCase:				int     =	attention_probs_dropout_prob
        __lowerCamelCase:				Union[str, Any]     =	num_labels
        __lowerCamelCase:				int     =	image_size
        __lowerCamelCase:				Optional[Any]     =	layer_depths
        __lowerCamelCase:				Dict     =	embed_dims





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        __lowerCamelCase:				Any     =	None
        if self.use_labels:
           __lowerCamelCase:				Tuple     =	ids_tensor([self.batch_size]    ,	self.num_labels							)

        __lowerCamelCase:				Dict     =	self.get_config()

        return config, pixel_values, labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        return SwiftFormerConfig(
            depths=self.layer_depths    ,	embed_dims=self.embed_dims    ,	mlp_ratio=4    ,	downsamples=[True, True, True, True]    ,	hidden_act="""gelu"""    ,	num_labels=self.num_labels    ,	down_patch_size=3    ,	down_stride=2    ,	down_pad=1    ,	drop_rate=0.0    ,	drop_path_rate=0.0    ,	use_layer_scale=SCREAMING_SNAKE_CASE_    ,	layer_scale_init_value=1E-5    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Dict     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.embed_dims[-1], 7, 7)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	self.num_labels
        __lowerCamelCase:				Union[str, Any]     =	SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Any     =	model(SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)

        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

        __lowerCamelCase:				Tuple     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
        __lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        ((__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)):				Union[str, Any]     =	self.prepare_config_and_inputs()
        __lowerCamelCase:				Optional[int]     =	{"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  List[str]						      =	(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
     UpperCAmelCase__ :  List[str]						      =	(
         {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  List[str]						      =	False
     UpperCAmelCase__ :  int						      =	False
     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  Dict						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModelTester(self							)
        __lowerCamelCase:				Optional[Any]     =	ConfigTester(
            self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	has_text_modality=SCREAMING_SNAKE_CASE_    ,	hidden_size=37    ,	num_attention_heads=12    ,	num_hidden_layers=12    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        self.config_tester.run_common_tests()





     @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Optional[Any]     =	model_class(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				Optional[Any]     =	model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_    ,	nn.Linear							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				List[str]     =	model_class(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				str     =	inspect.signature(model.forward							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           __lowerCamelCase:				Optional[Any]     =	[*signature.parameters.keys()]

           __lowerCamelCase:				List[str]     =	["""pixel_values"""]
           self.assertListEqual(arg_names[:1]    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_							)





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)





     @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
           __lowerCamelCase:				List[str]     =	model_class(SCREAMING_SNAKE_CASE_							)
           model.to(SCREAMING_SNAKE_CASE_							)
           model.eval()

           with torch.no_grad():
              __lowerCamelCase:				Any     =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)

           __lowerCamelCase:				Dict     =	outputs.hidden_states

           __lowerCamelCase:				Tuple     =	8
           self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							)  # TODO

           # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
           # with the width and height being successively divided by 2, after every 2 blocks
           for i in range(len(SCREAMING_SNAKE_CASE_							)							):
              self.assertEqual(
                  hidden_states[i].shape    ,	torch.Size(
                      [
                          self.model_tester.batch_size,
                          self.model_tester.embed_dims[i // 2],
                          (self.model_tester.image_size // 4) // 2 ** (i // 2),
                          (self.model_tester.image_size // 4) // 2 ** (i // 2),
                      ]							)    ,	)

        __lowerCamelCase   ,     __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	True
           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __lowerCamelCase:				Optional[int]     =	True

           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        def _config_zero_init(SCREAMING_SNAKE_CASE_					:				Any							):
           __lowerCamelCase:				Dict     =	copy.deepcopy(SCREAMING_SNAKE_CASE_							)
           for key in configs_no_init.__dict__.keys():
              if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                 setattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	1E-1_0							)
              if isinstance(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							):
                 __lowerCamelCase:				Tuple     =	_config_zero_init(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)
                 setattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
           return configs_no_init

        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

        __lowerCamelCase:				str     =	_config_zero_init(SCREAMING_SNAKE_CASE_							)
        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	model_class(config=SCREAMING_SNAKE_CASE_							)
           for name, param in model.named_parameters():
              if param.requires_grad:
                 self.assertIn(
                     ((param.data.mean() * 1E9) / 1E9).round().item()    ,	[0.0, 1.0]    ,	msg=F'Parameter {name} of model {model_class} seems not properly initialized'    ,	)







     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        pass



def     __lowerCAmelCase						(							)       ->							List[Any]:
   __lowerCamelCase:				Optional[Any]     =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







@require_torch
@require_vision
class        a					(			unittest.TestCase   ):





     @cached_property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	self.default_image_processor
        __lowerCamelCase:				Tuple     =	prepare_img()
        __lowerCamelCase:				int     =	image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE_							)

        # forward pass
        with torch.no_grad():
           __lowerCamelCase:				List[Any]     =	model(**SCREAMING_SNAKE_CASE_							)

        # verify the logits
        __lowerCamelCase:				List[Any]     =	torch.Size((1, 1000)							)
        self.assertEqual(outputs.logits.shape    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]							).to(SCREAMING_SNAKE_CASE_							)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)							)






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		:				Optional[Any]									=       logging.get_logger(__name__)

# General docstring
_A		:				int									=       '''ResNetConfig'''

# Base docstring
_A		:				Union[str, Any]									=       '''microsoft/resnet-50'''
_A		:				str									=       [1, 2_048, 7, 7]

# Image classification docstring
_A		:				List[str]									=       '''microsoft/resnet-50'''
_A		:				Optional[int]									=       '''tiger cat'''

_A		:				Optional[Any]									=       [
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]



class        a					(			nn.Module   ):





     def __init__(    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int = 3    ,	SCREAMING_SNAKE_CASE_					:				int = 1    ,	SCREAMING_SNAKE_CASE_					:				str = "relu"							):
        super().__init__()
        __lowerCamelCase:				Optional[int]     =	nn.Convad(
            SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	kernel_size=SCREAMING_SNAKE_CASE_    ,	stride=SCREAMING_SNAKE_CASE_    ,	padding=kernel_size // 2    ,	bias=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	nn.BatchNormad(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	ACTaFN[activation] if activation is not None else nn.Identity()







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Tensor							):
        __lowerCamelCase:				Optional[int]     =	self.convolution(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	self.normalization(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	self.activation(SCREAMING_SNAKE_CASE_							)
        return hidden_state



class        a					(			nn.Module   ):





     def __init__(    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				ResNetConfig							):
        super().__init__()
        __lowerCamelCase:				Tuple     =	ResNetConvLayer(
            config.num_channels    ,	config.embedding_size    ,	kernel_size=7    ,	stride=2    ,	activation=config.hidden_act							)
        __lowerCamelCase:				Tuple     =	nn.MaxPoolad(kernel_size=3    ,	stride=2    ,	padding=1							)
        __lowerCamelCase:				List[Any]     =	config.num_channels







     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Tensor							):
        __lowerCamelCase:				Optional[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."""							)
        __lowerCamelCase:				Optional[Any]     =	self.embedder(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	self.pooler(SCREAMING_SNAKE_CASE_							)
        return embedding



class        a					(			nn.Module   ):





     def __init__(    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int = 2							):
        super().__init__()
        __lowerCamelCase:				Optional[Any]     =	nn.Convad(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	kernel_size=1    ,	stride=SCREAMING_SNAKE_CASE_    ,	bias=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	nn.BatchNormad(SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Tensor							):
        __lowerCamelCase:				Optional[int]     =	self.convolution(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	self.normalization(SCREAMING_SNAKE_CASE_							)
        return hidden_state



class        a					(			nn.Module   ):





     def __init__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int = 1    ,	SCREAMING_SNAKE_CASE_					:				str = "relu"							):
        super().__init__()
        __lowerCamelCase:				Union[str, Any]     =	in_channels != out_channels or stride != 1
        __lowerCamelCase:				int     =	(
            ResNetShortCut(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	stride=SCREAMING_SNAKE_CASE_							) if should_apply_shortcut else nn.Identity()
        )
        __lowerCamelCase:				List[str]     =	nn.Sequential(
            ResNetConvLayer(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	stride=SCREAMING_SNAKE_CASE_							)    ,	ResNetConvLayer(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	activation=SCREAMING_SNAKE_CASE_							)    ,	)
        __lowerCamelCase:				Dict     =	ACTaFN[activation]







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        __lowerCamelCase:				List[Any]     =	hidden_state
        __lowerCamelCase:				Optional[Any]     =	self.layer(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	self.shortcut(SCREAMING_SNAKE_CASE_							)
        hidden_state += residual
        __lowerCamelCase:				str     =	self.activation(SCREAMING_SNAKE_CASE_							)
        return hidden_state



class        a					(			nn.Module   ):





     def __init__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int = 1    ,	SCREAMING_SNAKE_CASE_					:				str = "relu"    ,	SCREAMING_SNAKE_CASE_					:				int = 4							):
        super().__init__()
        __lowerCamelCase:				str     =	in_channels != out_channels or stride != 1
        __lowerCamelCase:				Any     =	out_channels // reduction
        __lowerCamelCase:				str     =	(
            ResNetShortCut(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	stride=SCREAMING_SNAKE_CASE_							) if should_apply_shortcut else nn.Identity()
        )
        __lowerCamelCase:				Any     =	nn.Sequential(
            ResNetConvLayer(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	kernel_size=1							)    ,	ResNetConvLayer(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	stride=SCREAMING_SNAKE_CASE_							)    ,	ResNetConvLayer(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	kernel_size=1    ,	activation=SCREAMING_SNAKE_CASE_							)    ,	)
        __lowerCamelCase:				str     =	ACTaFN[activation]







     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        __lowerCamelCase:				Optional[Any]     =	hidden_state
        __lowerCamelCase:				int     =	self.layer(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	self.shortcut(SCREAMING_SNAKE_CASE_							)
        hidden_state += residual
        __lowerCamelCase:				List[str]     =	self.activation(SCREAMING_SNAKE_CASE_							)
        return hidden_state



class        a					(			nn.Module   ):





     def __init__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				ResNetConfig    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int = 2    ,	SCREAMING_SNAKE_CASE_					:				int = 2    ,	):
        super().__init__()

        __lowerCamelCase:				str     =	ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

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







     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Tensor							):
        __lowerCamelCase:				Tuple     =	input
        for layer in self.layers:
           __lowerCamelCase:				int     =	layer(SCREAMING_SNAKE_CASE_							)
        return hidden_state



class        a					(			nn.Module   ):





     def __init__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				ResNetConfig							):
        super().__init__()
        __lowerCamelCase:				Any     =	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(
                SCREAMING_SNAKE_CASE_    ,	config.embedding_size    ,	config.hidden_sizes[0]    ,	stride=2 if config.downsample_in_first_stage else 1    ,	depth=config.depths[0]    ,	)							)
        __lowerCamelCase:				List[str]     =	zip(config.hidden_sizes    ,	config.hidden_sizes[1:]							)
        for (in_channels, out_channels), depth in zip(SCREAMING_SNAKE_CASE_    ,	config.depths[1:]							):
           self.stages.append(ResNetStage(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	depth=SCREAMING_SNAKE_CASE_							)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Tensor    ,	SCREAMING_SNAKE_CASE_					:				bool = False    ,	SCREAMING_SNAKE_CASE_					:				bool = True							):
        __lowerCamelCase:				List[Any]     =	() if output_hidden_states else None

        for stage_module in self.stages:
           if output_hidden_states:
              __lowerCamelCase:				Optional[Any]     =	hidden_states + (hidden_state,)

           __lowerCamelCase:				Union[str, Any]     =	stage_module(SCREAMING_SNAKE_CASE_							)

        if output_hidden_states:
           __lowerCamelCase:				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=SCREAMING_SNAKE_CASE_    ,	hidden_states=SCREAMING_SNAKE_CASE_    ,	)



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Dict						      =	ResNetConfig
     UpperCAmelCase__ :  str						      =	"resnet"
     UpperCAmelCase__ :  Optional[int]						      =	"pixel_values"
     UpperCAmelCase__ :  List[str]						      =	True





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







     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				str=False							):
        if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				Union[str, Any]     =	value



_A		:				Dict									=       r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

_A		:				Optional[int]									=       r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''



@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."     ,_UpperCAmelCase     ,)
class        a					(			_UpperCAmelCase   ):





     def __init__(    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]							):
        super().__init__(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	config
        __lowerCamelCase:				Tuple     =	ResNetEmbeddings(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	ResNetEncoder(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	nn.AdaptiveAvgPoolad((1, 1)							)
        # Initialize weights and apply final processing
        self.post_init()







     @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_							)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC    ,	output_type=SCREAMING_SNAKE_CASE_    ,	config_class=_CONFIG_FOR_DOC    ,	modality="""vision"""    ,	expected_output=_EXPECTED_OUTPUT_SHAPE    ,	)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Tensor    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None							):
        __lowerCamelCase:				Dict     =	(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        __lowerCamelCase:				List[Any]     =	return_dict if return_dict is not None else self.config.use_return_dict

        __lowerCamelCase:				Any     =	self.embedder(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	self.encoder(
            SCREAMING_SNAKE_CASE_    ,	output_hidden_states=SCREAMING_SNAKE_CASE_    ,	return_dict=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	encoder_outputs[0]

        __lowerCamelCase:				List[Any]     =	self.pooler(SCREAMING_SNAKE_CASE_							)

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

        return BaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=SCREAMING_SNAKE_CASE_    ,	pooler_output=SCREAMING_SNAKE_CASE_    ,	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        a					(			_UpperCAmelCase   ):





     def __init__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        super().__init__(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	config.num_labels
        __lowerCamelCase:				int     =	ResNetModel(SCREAMING_SNAKE_CASE_							)
        # classification head
        __lowerCamelCase:				str     =	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(SCREAMING_SNAKE_CASE_							)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT    ,	output_type=SCREAMING_SNAKE_CASE_    ,	config_class=_CONFIG_FOR_DOC    ,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,	)
     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[torch.FloatTensor] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[torch.LongTensor] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	):
        __lowerCamelCase:				Tuple     =	return_dict if return_dict is not None else self.config.use_return_dict

        __lowerCamelCase:				List[str]     =	self.resnet(SCREAMING_SNAKE_CASE_    ,	output_hidden_states=SCREAMING_SNAKE_CASE_    ,	return_dict=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	outputs.pooler_output if return_dict else outputs[1]

        __lowerCamelCase:				str     =	self.classifier(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Dict     =	None

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

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

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



@add_start_docstrings(
    "\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    "     ,_UpperCAmelCase     ,)
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase   ):





     def __init__(    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        super().__init__(SCREAMING_SNAKE_CASE_							)
        super()._init_backbone(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[int]     =	[config.embedding_size] + config.hidden_sizes
        __lowerCamelCase:				str     =	ResNetEmbeddings(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	ResNetEncoder(SCREAMING_SNAKE_CASE_							)

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







     @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_							)
     @replace_return_docstrings(output_type=SCREAMING_SNAKE_CASE_    ,	config_class=_CONFIG_FOR_DOC							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Tensor    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None							):
        __lowerCamelCase:				Dict     =	return_dict if return_dict is not None else self.config.use_return_dict
        __lowerCamelCase:				Dict     =	(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        __lowerCamelCase:				str     =	self.embedder(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Dict     =	self.encoder(SCREAMING_SNAKE_CASE_    ,	output_hidden_states=SCREAMING_SNAKE_CASE_    ,	return_dict=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	outputs.hidden_states

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

        if not return_dict:
           __lowerCamelCase:				Union[str, Any]     =	(feature_maps,)
           if output_hidden_states:
              output += (outputs.hidden_states,)
           return output

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


