



"""simple docstring"""







# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class        __A       (	A_     ,A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Union[str, Any]   	=				StableDiffusionControlNetImgaImgPipeline
		lowerCAmelCase			:					Any   	=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
		lowerCAmelCase			:					List[str]   	=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		lowerCAmelCase			:					Tuple   	=				IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}    )
		lowerCAmelCase			:					int   	=				IMAGE_TO_IMAGE_IMAGE_PARAMS







		def 					UpperCAmelCase    (	self						:  int						)					->		Union[str, Any]:






				"""simple docstring"""



				torch.manual_seed(0						)
				lowercase__   :    Optional[int]				=			UNetaDConditionModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,sample_size=32			,in_channels=4			,out_channels=4			,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,cross_attention_dim=32			,)
				torch.manual_seed(0						)
				lowercase__   :    Any				=			ControlNetModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,in_channels=4			,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,cross_attention_dim=32			,conditioning_embedding_out_channels=(16, 32)			,)
				torch.manual_seed(0						)
				lowercase__   :    List[str]				=			DDIMScheduler(
				    beta_start=0.0_0085			,beta_end=0.012			,beta_schedule='''scaled_linear'''			,clip_sample=_snake_case			,set_alpha_to_one=_snake_case			,)
				torch.manual_seed(0						)
				lowercase__   :    List[str]				=			AutoencoderKL(
				    block_out_channels=[32, 64]			,in_channels=3			,out_channels=3			,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,latent_channels=4			,)
				torch.manual_seed(0						)
				lowercase__   :    Optional[Any]				=			CLIPTextConfig(
				    bos_token_id=0			,eos_token_id=2			,hidden_size=32			,intermediate_size=37			,layer_norm_eps=1e-05			,num_attention_heads=4			,num_hidden_layers=5			,pad_token_id=1			,vocab_size=1_000			,)
				lowercase__   :    List[Any]				=			CLIPTextModel(_snake_case						)
				lowercase__   :    Union[str, Any]				=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

				lowercase__   :    Dict				=			{
				    '''unet''': unet,
				    '''controlnet''': controlnet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[Any]			,_snake_case						:  Any=0						)					->		Any:






				"""simple docstring"""



				if str(_snake_case						).startswith('''mps'''						):
						lowercase__   :    Optional[Any]				=			torch.manual_seed(_snake_case						)
				else:
						lowercase__   :    str				=			torch.Generator(device=_snake_case						).manual_seed(_snake_case						)

				lowercase__   :    List[Any]				=			2
				lowercase__   :    Optional[int]				=			randn_tensor(
				    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,generator=_snake_case			,device=torch.device(_snake_case						)			,)
				lowercase__   :    str				=			floats_tensor(control_image.shape			,rng=random.Random(_snake_case						)						).to(_snake_case						)
				lowercase__   :    Optional[Any]				=			image.cpu().permute(0			,2			,3			,1						)[0]
				lowercase__   :    Optional[Any]				=			Image.fromarray(np.uinta(_snake_case						)						).convert('''RGB'''						).resize((64, 64)						)
				lowercase__   :    Union[str, Any]				=			{
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''generator''': generator,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				    '''image''': image,
				    '''control_image''': control_image,
				}

				return inputs







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Dict:






				"""simple docstring"""



				return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3						)







		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()			,reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,)
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		str:






				"""simple docstring"""



				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)







		def 					UpperCAmelCase    (	self						:  Any						)					->		str:






				"""simple docstring"""



				self._test_inference_batch_single_identical(expected_max_diff=2e-3						)





class        __A       (	A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Dict   	=				StableDiffusionControlNetImgaImgPipeline
		lowerCAmelCase			:					List[Any]   	=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
		lowerCAmelCase			:					Union[str, Any]   	=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		lowerCAmelCase			:					Dict   	=				frozenset([]    )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Any:






				"""simple docstring"""



				torch.manual_seed(0						)
				lowercase__   :    List[Any]				=			UNetaDConditionModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,sample_size=32			,in_channels=4			,out_channels=4			,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,cross_attention_dim=32			,)
				torch.manual_seed(0						)

				def init_weights(_snake_case						:  Optional[int]						):
						if isinstance(_snake_case			,torch.nn.Convad						):
								torch.nn.init.normal(m.weight						)
								m.bias.data.fill_(1.0						)

				lowercase__   :    Any				=			ControlNetModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,in_channels=4			,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,cross_attention_dim=32			,conditioning_embedding_out_channels=(16, 32)			,)
				controlneta.controlnet_down_blocks.apply(_snake_case						)

				torch.manual_seed(0						)
				lowercase__   :    Any				=			ControlNetModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,in_channels=4			,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,cross_attention_dim=32			,conditioning_embedding_out_channels=(16, 32)			,)
				controlneta.controlnet_down_blocks.apply(_snake_case						)

				torch.manual_seed(0						)
				lowercase__   :    Dict				=			DDIMScheduler(
				    beta_start=0.0_0085			,beta_end=0.012			,beta_schedule='''scaled_linear'''			,clip_sample=_snake_case			,set_alpha_to_one=_snake_case			,)
				torch.manual_seed(0						)
				lowercase__   :    List[str]				=			AutoencoderKL(
				    block_out_channels=[32, 64]			,in_channels=3			,out_channels=3			,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,latent_channels=4			,)
				torch.manual_seed(0						)
				lowercase__   :    List[Any]				=			CLIPTextConfig(
				    bos_token_id=0			,eos_token_id=2			,hidden_size=32			,intermediate_size=37			,layer_norm_eps=1e-05			,num_attention_heads=4			,num_hidden_layers=5			,pad_token_id=1			,vocab_size=1_000			,)
				lowercase__   :    int				=			CLIPTextModel(_snake_case						)
				lowercase__   :    Union[str, Any]				=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

				lowercase__   :    int				=			MultiControlNetModel([controlneta, controlneta]						)

				lowercase__   :    Optional[Any]				=			{
				    '''unet''': unet,
				    '''controlnet''': controlnet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  Dict			,_snake_case						:  Union[str, Any]=0						)					->		List[Any]:






				"""simple docstring"""



				if str(_snake_case						).startswith('''mps'''						):
						lowercase__   :    int				=			torch.manual_seed(_snake_case						)
				else:
						lowercase__   :    Dict				=			torch.Generator(device=_snake_case						).manual_seed(_snake_case						)

				lowercase__   :    int				=			2

				lowercase__   :    Optional[Any]				=			[
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,generator=_snake_case			,device=torch.device(_snake_case						)			,),
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,generator=_snake_case			,device=torch.device(_snake_case						)			,),
				]

				lowercase__   :    Dict				=			floats_tensor(control_image[0].shape			,rng=random.Random(_snake_case						)						).to(_snake_case						)
				lowercase__   :    Dict				=			image.cpu().permute(0			,2			,3			,1						)[0]
				lowercase__   :    Optional[int]				=			Image.fromarray(np.uinta(_snake_case						)						).convert('''RGB'''						).resize((64, 64)						)
				lowercase__   :    Any				=			{
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''generator''': generator,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				    '''image''': image,
				    '''control_image''': control_image,
				}

				return inputs







		def 					UpperCAmelCase    (	self						:  Dict						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    Dict				=			self.get_dummy_components()
				lowercase__   :    Dict				=			self.pipeline_class(**_snake_case						)
				pipe.to(_snake_case						)

				lowercase__   :    Optional[Any]				=			10.0
				lowercase__   :    Tuple				=			4

				lowercase__   :    Dict				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    Optional[Any]				=			steps
				lowercase__   :    Any				=			scale
				lowercase__   :    Optional[Any]				=			pipe(**_snake_case						)[0]

				lowercase__   :    List[str]				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    Optional[int]				=			steps
				lowercase__   :    int				=			scale
				lowercase__   :    List[str]				=			pipe(**_snake_case			,control_guidance_start=0.1			,control_guidance_end=0.2						)[0]

				lowercase__   :    int				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    Optional[int]				=			steps
				lowercase__   :    Dict				=			scale
				lowercase__   :    Dict				=			pipe(**_snake_case			,control_guidance_start=[0.1, 0.3]			,control_guidance_end=[0.2, 0.7]						)[0]

				lowercase__   :    Dict				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    List[Any]				=			steps
				lowercase__   :    Optional[int]				=			scale
				lowercase__   :    List[Any]				=			pipe(**_snake_case			,control_guidance_start=0.4			,control_guidance_end=[0.5, 0.8]						)[0]

				# make sure that all outputs are different
				assert np.sum(np.abs(output_a - output_a						)						) > 1e-3
				assert np.sum(np.abs(output_a - output_a						)						) > 1e-3
				assert np.sum(np.abs(output_a - output_a						)						) > 1e-3







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[int]:






				"""simple docstring"""



				return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3						)







		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()			,reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,)
		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Union[str, Any]:






				"""simple docstring"""



				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)







		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Optional[int]:






				"""simple docstring"""



				self._test_inference_batch_single_identical(expected_max_diff=2e-3						)







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			self.get_dummy_components()
				lowercase__   :    Optional[Any]				=			self.pipeline_class(**_snake_case						)
				pipe.to(_snake_case						)
				pipe.set_progress_bar_config(disable=_snake_case						)
				with tempfile.TemporaryDirectory() as tmpdir:
						try:
								# save_pretrained is not implemented for Multi-ControlNet
								pipe.save_pretrained(_snake_case						)
						except NotImplementedError:
								pass





@slow
@require_torch_gpu
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Any						)					->		int:






				"""simple docstring"""



				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    int				=			ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''						)

				lowercase__   :    Any				=			StableDiffusionControlNetImgaImgPipeline.from_pretrained(
				    '''runwayml/stable-diffusion-v1-5'''			,safety_checker=_snake_case			,controlnet=_snake_case						)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=_snake_case						)

				lowercase__   :    Optional[Any]				=			torch.Generator(device='''cpu'''						).manual_seed(0						)
				lowercase__   :    List[str]				=			'''evil space-punk bird'''
				lowercase__   :    Optional[Any]				=			load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''						).resize((512, 512)						)
				lowercase__   :    Tuple				=			load_image(
				    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''						).resize((512, 512)						)

				lowercase__   :    List[Any]				=			pipe(
				    _snake_case			,_snake_case			,control_image=_snake_case			,generator=_snake_case			,output_type='''np'''			,num_inference_steps=50			,strength=0.6			,)

				lowercase__   :    List[Any]				=			output.images[0]

				assert image.shape == (512, 512, 3)

				lowercase__   :    Dict				=			load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'''						)

				assert np.abs(expected_image - image						).max() < 9e-2



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


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

# General docstring
_UpperCAmelCase		:   Dict										=       """ResNetConfig"""

# Base docstring
_UpperCAmelCase		:   Optional[int]										=       """microsoft/resnet-50"""
_UpperCAmelCase		:   Optional[Any]										=       [1, 2048, 7, 7]

# Image classification docstring
_UpperCAmelCase		:   Tuple										=       """microsoft/resnet-50"""
_UpperCAmelCase		:   int										=       """tiger cat"""

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







class  lowercase				(       nn.Module			):




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




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    self.convolution(snake_case       )
											snake_case_							=    self.normalization(snake_case       )
											snake_case_							=    self.activation(snake_case       )
											return hidden_state







class  lowercase				(       nn.Module			):




				def __init__(					self      ,   snake_case       ):
											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     a							(					self      ,   snake_case       ):
											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(snake_case       )
											snake_case_							=    self.pooler(snake_case       )
											return embedding







class  lowercase				(       nn.Module			):




				def __init__(					self      ,   snake_case      ,   snake_case      ,   snake_case = 2       ):
											super().__init__()
											snake_case_							=    nn.Convad(snake_case      ,   snake_case      ,   kernel_size=1      ,   stride=snake_case      ,   bias=snake_case       )
											snake_case_							=    nn.BatchNormad(snake_case       )




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    self.convolution(snake_case       )
											snake_case_							=    self.normalization(snake_case       )
											return hidden_state







class  lowercase				(       nn.Module			):




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




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    hidden_state
											snake_case_							=    self.layer(snake_case       )
											snake_case_							=    self.shortcut(snake_case       )
											hidden_state += residual
											snake_case_							=    self.activation(snake_case       )
											return hidden_state







class  lowercase				(       nn.Module			):




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




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    hidden_state
											snake_case_							=    self.layer(snake_case       )
											snake_case_							=    self.shortcut(snake_case       )
											hidden_state += residual
											snake_case_							=    self.activation(snake_case       )
											return hidden_state







class  lowercase				(       nn.Module			):




				def __init__(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case = 2      ,   snake_case = 2      ,   ):
											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(snake_case      ,   snake_case      ,   stride=snake_case      ,   activation=config.hidden_act       )      ,   *[layer(snake_case      ,   snake_case      ,   activation=config.hidden_act       ) for _ in range(depth - 1       )]      ,   )




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    input
											for layer in self.layers:
																		snake_case_							=    layer(snake_case       )
											return hidden_state







class  lowercase				(       nn.Module			):




				def __init__(					self      ,   snake_case       ):
											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(
											        snake_case      ,   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(snake_case      ,   config.depths[1:]       ):
																		self.stages.append(ResNetStage(snake_case      ,   snake_case      ,   snake_case      ,   depth=snake_case       )       )




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

											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=snake_case      ,   hidden_states=snake_case      ,   )







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: List[str]       			=			ResNetConfig
				__SCREAMING_SNAKE_CASE					: Any       			=			'''resnet'''
				__SCREAMING_SNAKE_CASE					: int       			=			'''pixel_values'''
				__SCREAMING_SNAKE_CASE					: Tuple       			=			True




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




				def     a							(					self      ,   snake_case      ,   snake_case=False       ):
											if isinstance(snake_case      ,   snake_case       ):
																		snake_case_							=    value



_UpperCAmelCase		:   Tuple										=       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.
"""

_UpperCAmelCase		:   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.'''	,  lowercase_	,  )
class  lowercase				(       lowercase_			):




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




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

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

											snake_case_							=    encoder_outputs[0]

											snake_case_							=    self.pooler(snake_case       )

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

											return BaseModelOutputWithPoolingAndNoAttention(
											    last_hidden_state=snake_case      ,   pooler_output=snake_case      ,   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.
    '''	,  lowercase_	,  )
class  lowercase				(       lowercase_			):




				def __init__(					self      ,   snake_case       ):
											super().__init__(snake_case       )
											snake_case_							=    config.num_labels
											snake_case_							=    ResNetModel(snake_case       )
											# 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(snake_case       )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT      ,   output_type=snake_case      ,   config_class=_CONFIG_FOR_DOC      ,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,   )
				def     a							(					self      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   ):
											snake_case_							=    return_dict if return_dict is not None else self.config.use_return_dict

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

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

											snake_case_							=    self.classifier(snake_case       )

											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(snake_case      ,   snake_case       )
																		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(snake_case      ,   snake_case       )

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

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







@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''	,  lowercase_	,  )
class  lowercase				(       lowercase_	,  lowercase_			):




				def __init__(					self      ,   snake_case       ):
											super().__init__(snake_case       )
											super()._init_backbone(snake_case       )

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

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




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

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

											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=snake_case      ,   hidden_states=outputs.hidden_states if output_hidden_states else None      ,   attentions=snake_case      ,   )




