"""simple docstring"""




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
				from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor
class 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=13						,__UpperCAmelCase=30						,__UpperCAmelCase=2						,__UpperCAmelCase=3						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=32						,__UpperCAmelCase=5						,__UpperCAmelCase=4						,__UpperCAmelCase=37						,__UpperCAmelCase="gelu"						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.1						,__UpperCAmelCase=10						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=3						,__UpperCAmelCase=0.6						,__UpperCAmelCase=None						,)    -> Any:
													A__		   =							parent
													A__		   =							batch_size
													A__		   =							image_size
													A__		   =							patch_size
													A__		   =							num_channels
													A__		   =							is_training
													A__		   =							use_labels
													A__		   =							hidden_size
													A__		   =							num_hidden_layers
													A__		   =							num_attention_heads
													A__		   =							intermediate_size
													A__		   =							hidden_act
													A__		   =							hidden_dropout_prob
													A__		   =							attention_probs_dropout_prob
													A__		   =							type_sequence_label_size
													A__		   =							initializer_range
													A__		   =							mask_ratio
													A__		   =							scope

													# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
													# (we add 1 for the [CLS] token)
													A__		   =							(image_size // patch_size) ** 2
													A__		   =							int(math.ceil((1 - mask_ratio) * (num_patches + 1)   )   )





						def  snake_case__		(				self   )    -> List[str]:
													A__		   =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

													A__		   =							None
													if self.use_labels:
																				A__		   =							ids_tensor([self.batch_size]						,self.type_sequence_label_size   )

													A__		   =							self.get_config()

													return config, pixel_values, labels





						def  snake_case__		(				self   )    -> Dict:
													return ViTMAEConfig(
													    image_size=self.image_size						,patch_size=self.patch_size						,num_channels=self.num_channels						,hidden_size=self.hidden_size						,num_hidden_layers=self.num_hidden_layers						,num_attention_heads=self.num_attention_heads						,intermediate_size=self.intermediate_size						,hidden_act=self.hidden_act						,hidden_dropout_prob=self.hidden_dropout_prob						,attention_probs_dropout_prob=self.attention_probs_dropout_prob						,is_decoder=__UpperCAmelCase						,initializer_range=self.initializer_range						,mask_ratio=self.mask_ratio						,)





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[int]:
													A__		   =							ViTMAEModel(config=__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.last_hidden_state.shape						,(self.batch_size, self.seq_length, self.hidden_size)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													A__		   =							ViTMAEForPreTraining(__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							model(__UpperCAmelCase   )
													A__		   =							(self.image_size // self.patch_size) ** 2
													A__		   =							self.patch_size**2 * self.num_channels
													self.parent.assertEqual(result.logits.shape						,(self.batch_size, num_patches, expected_num_channels)   )

													# test greyscale images
													A__		   =							1
													A__		   =							ViTMAEForPreTraining(__UpperCAmelCase   )
													model.to(__UpperCAmelCase   )
													model.eval()
													A__		   =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
													A__		   =							model(__UpperCAmelCase   )
													A__		   =							self.patch_size**2
													self.parent.assertEqual(result.logits.shape						,(self.batch_size, num_patches, expected_num_channels)   )





						def  snake_case__		(				self   )    -> Tuple:
													A__		   =							self.prepare_config_and_inputs()
													A__   ,     A__   ,     A__		   =							config_and_inputs
													A__		   =							{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class 			UpperCamelCase__(	__A						,  __A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  Optional[int]  							=						(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
						lowerCAmelCase__     :  int  							=						{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

						lowerCAmelCase__     :  List[str]  							=						False
						lowerCAmelCase__     :  Dict  							=						False
						lowerCAmelCase__     :  int  							=						False
						lowerCAmelCase__     :  List[str]  							=						False





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							ViTMAEModelTester(self   )
													A__		   =							ConfigTester(self						,config_class=__UpperCAmelCase						,has_text_modality=__UpperCAmelCase						,hidden_size=37   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													self.config_tester.run_common_tests()





						@unittest.skip(reason='ViTMAE does not use inputs_embeds'   )
						def  snake_case__		(				self   )    -> Optional[int]:
													pass





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__		   =							model_class(__UpperCAmelCase   )
																				self.assertIsInstance(model.get_input_embeddings()						,(nn.Module)   )
																				A__		   =							model.get_output_embeddings()
																				self.assertTrue(x is None or isinstance(__UpperCAmelCase						,nn.Linear   )   )





						def  snake_case__		(				self   )    -> Any:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__		   =							model_class(__UpperCAmelCase   )
																				A__		   =							inspect.signature(model.forward   )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				A__		   =							[*signature.parameters.keys()]

																				A__		   =							['pixel_values']
																				self.assertListEqual(arg_names[:1]						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Any:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Union[str, Any]:
													# make masks reproducible
													np.random.seed(2   )

													A__		   =							int((pt_model.config.image_size // pt_model.config.patch_size) ** 2   )
													A__		   =							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
													A__		   =							torch.from_numpy(__UpperCAmelCase   )

													# Add `noise` argument.
													# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
													A__		   =							pt_noise

													super().check_pt_tf_models(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> List[Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__		   =							model_class(__UpperCAmelCase   )
																				model.to(__UpperCAmelCase   )
																				model.eval()
																				# make random mask reproducible
																				torch.manual_seed(2   )
																				with torch.no_grad():
																											A__		   =							model(**self._prepare_for_class(__UpperCAmelCase						,__UpperCAmelCase   )   )

																				A__		   =							outputs[0].cpu().numpy()
																				A__		   =							0

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											model.save_pretrained(__UpperCAmelCase   )
																											A__		   =							model_class.from_pretrained(__UpperCAmelCase   )
																											model.to(__UpperCAmelCase   )
																											# make random mask reproducible
																											torch.manual_seed(2   )
																											with torch.no_grad():
																																		A__		   =							model(**self._prepare_for_class(__UpperCAmelCase						,__UpperCAmelCase   )   )

																											# Make sure we don't have nans
																											A__		   =							after_outputs[0].cpu().numpy()
																											A__		   =							0
																											A__		   =							np.amax(np.abs(out_a - out_a   )   )
																											self.assertLessEqual(__UpperCAmelCase						,1e-5   )





						@unittest.skip(
						    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'   )
						def  snake_case__		(				self   )    -> List[str]:
													pass





						@unittest.skip(
						    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'   )
						def  snake_case__		(				self   )    -> Union[str, Any]:
													pass





						@unittest.skip(
						    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'   )
						def  snake_case__		(				self   )    -> Any:
													pass





						@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'   )
						def  snake_case__		(				self   )    -> Any:
													pass





						@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
						def  snake_case__		(				self   )    -> int:
													pass





						@slow
						def  snake_case__		(				self   )    -> int:
													for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__		   =							ViTMAEModel.from_pretrained(__UpperCAmelCase   )
																				self.assertIsNotNone(__UpperCAmelCase   )






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
							return image


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





						@cached_property
						def  snake_case__		(				self   )    -> List[str]:
													return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'   ) if is_vision_available() else None





						@slow
						def  snake_case__		(				self   )    -> Tuple:
													# make random mask reproducible across the PT and TF model
													np.random.seed(2   )

													A__		   =							ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'   ).to(__UpperCAmelCase   )

													A__		   =							self.default_image_processor
													A__		   =							prepare_img()
													A__		   =							image_processor(images=__UpperCAmelCase						,return_tensors='pt'   ).to(__UpperCAmelCase   )

													# prepare a noise vector that will be also used for testing the TF model
													# (this way we can ensure that the PT and TF models operate on the same inputs)
													A__		   =							ViTMAEConfig()
													A__		   =							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2   )
													A__		   =							np.random.uniform(size=(1, num_patches)   )

													# forward pass
													with torch.no_grad():
																				A__		   =							model(**__UpperCAmelCase						,noise=torch.from_numpy(__UpperCAmelCase   ).to(device=__UpperCAmelCase   )   )

													# verify the logits
													A__		   =							torch.Size((1, 1_96, 7_68)   )
													self.assertEqual(outputs.logits.shape						,__UpperCAmelCase   )

													A__		   =							torch.tensor(
													    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]   )

													self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]						,expected_slice.to(__UpperCAmelCase   )						,atol=1e-4   )   )






"""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						,)





