





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
@skip_mps
class      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase							,					unittest.TestCase   ):
					lowerCAmelCase_							=		StableDiffusionPanoramaPipeline
					lowerCAmelCase_							=		TEXT_TO_IMAGE_PARAMS
					lowerCAmelCase_							=		TEXT_TO_IMAGE_BATCH_PARAMS
					lowerCAmelCase_							=		TEXT_TO_IMAGE_IMAGE_PARAMS
					lowerCAmelCase_							=		TEXT_TO_IMAGE_IMAGE_PARAMS




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										torch.manual_seed(0					)
										_snake_case													=			UNetaDConditionModel(
										    block_out_channels=(32, 64)			,  layers_per_block=1			,  sample_size=32			,  in_channels=4			,  out_channels=4			,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,  cross_attention_dim=32			,  )
										_snake_case													=			DDIMScheduler()
										torch.manual_seed(0					)
										_snake_case													=			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					)
										_snake_case													=			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=1000			,  )
										_snake_case													=			CLIPTextModel(lowerCAmelCase_					)
										_snake_case													=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

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




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=0					)						->							Union[str, Any]:
										_snake_case													=			torch.manual_seed(lowerCAmelCase_					)
										_snake_case													=			{
										    'prompt': 'a photo of the dolomites',
										    'generator': generator,
										    # Setting height and width to None to prevent OOMs on CPU.
										    'height': None,
										    'width': None,
										    'num_inference_steps': 1,
										    'guidance_scale': 6.0,
										    'output_type': 'numpy',
										}
										return inputs




					def 		lowerCAmelCase     (						self					)						->							int:
										_snake_case													=			'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case													=			self.get_dummy_components()
										_snake_case													=			StableDiffusionPanoramaPipeline(**lowerCAmelCase_					)
										_snake_case													=			sd_pipe.to(lowerCAmelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

										_snake_case													=			self.get_dummy_inputs(lowerCAmelCase_					)
										_snake_case													=			sd_pipe(**lowerCAmelCase_					).images
										_snake_case													=			image[0, -3:, -3:, -1]
										assert image.shape == (1, 64, 64, 3)

										_snake_case													=			np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2




					def 		lowerCAmelCase     (						self					)						->							Optional[int]:
										super().test_inference_batch_consistent(batch_sizes=[1, 2]					)




					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										super().test_inference_batch_single_identical(batch_size=2			,  expected_max_diff=3.25E-3					)




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case													=			self.get_dummy_components()
										_snake_case													=			StableDiffusionPanoramaPipeline(**lowerCAmelCase_					)
										_snake_case													=			sd_pipe.to(lowerCAmelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

										_snake_case													=			self.get_dummy_inputs(lowerCAmelCase_					)
										_snake_case													=			'french fries'
										_snake_case													=			sd_pipe(**lowerCAmelCase_			,  negative_prompt=lowerCAmelCase_					)
										_snake_case													=			output.images
										_snake_case													=			image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										_snake_case													=			np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2




					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case													=			self.get_dummy_components()
										_snake_case													=			StableDiffusionPanoramaPipeline(**lowerCAmelCase_					)
										_snake_case													=			sd_pipe.to(lowerCAmelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

										_snake_case													=			self.get_dummy_inputs(lowerCAmelCase_					)
										_snake_case													=			sd_pipe(**lowerCAmelCase_			,  view_batch_size=2					)
										_snake_case													=			output.images
										_snake_case													=			image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										_snake_case													=			np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case													=			self.get_dummy_components()
										_snake_case													=			EulerAncestralDiscreteScheduler(
										    beta_start=0.0_00_85			,  beta_end=0.0_12			,  beta_schedule='scaled_linear'					)
										_snake_case													=			StableDiffusionPanoramaPipeline(**lowerCAmelCase_					)
										_snake_case													=			sd_pipe.to(lowerCAmelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

										_snake_case													=			self.get_dummy_inputs(lowerCAmelCase_					)
										_snake_case													=			sd_pipe(**lowerCAmelCase_					).images
										_snake_case													=			image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										_snake_case													=			np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2






					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case													=			self.get_dummy_components()
										_snake_case													=			PNDMScheduler(
										    beta_start=0.0_00_85			,  beta_end=0.0_12			,  beta_schedule='scaled_linear'			,  skip_prk_steps=lowerCAmelCase_					)
										_snake_case													=			StableDiffusionPanoramaPipeline(**lowerCAmelCase_					)
										_snake_case													=			sd_pipe.to(lowerCAmelCase_					)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

										_snake_case													=			self.get_dummy_inputs(lowerCAmelCase_					)
										_snake_case													=			sd_pipe(**lowerCAmelCase_					).images
										_snake_case													=			image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										_snake_case													=			np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2





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




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_=0					)						->							List[Any]:
										_snake_case													=			torch.manual_seed(lowerCAmelCase_					)
										_snake_case													=			{
										    'prompt': 'a photo of the dolomites',
										    'generator': generator,
										    'num_inference_steps': 3,
										    'guidance_scale': 7.5,
										    'output_type': 'numpy',
										}
										return inputs




					def 		lowerCAmelCase     (						self					)						->							Any:
										_snake_case													=			'stabilityai/stable-diffusion-2-base'
										_snake_case													=			DDIMScheduler.from_pretrained(lowerCAmelCase_			,  subfolder='scheduler'					)
										_snake_case													=			StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase_			,  scheduler=lowerCAmelCase_			,  safety_checker=lowerCAmelCase_					)
										pipe.to(lowerCAmelCase_					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
										pipe.enable_attention_slicing()

										_snake_case													=			self.get_inputs()
										_snake_case													=			pipe(**lowerCAmelCase_					).images
										_snake_case													=			image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 2048, 3)

										_snake_case													=			np.array(
										    [
										        0.36_96_83_92,
										        0.27_02_53_72,
										        0.32_44_67_66,
										        0.28_37_93_87,
										        0.36_36_32_74,
										        0.30_73_33_47,
										        0.27_10_00_27,
										        0.27_05_41_25,
										        0.25_53_60_96,
										    ]					)

										assert np.abs(expected_slice - image_slice					).max() < 1E-2




					def 		lowerCAmelCase     (						self					)						->							Any:
										_snake_case													=			StableDiffusionPanoramaPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2-base'			,  safety_checker=lowerCAmelCase_					)
										_snake_case													=			LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
										pipe.to(lowerCAmelCase_					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
										pipe.enable_attention_slicing()

										_snake_case													=			self.get_inputs()
										_snake_case													=			pipe(**lowerCAmelCase_					).images
										_snake_case													=			image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 2048, 3)

										_snake_case													=			np.array(
										    [
										        [
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										        ]
										    ]					)

										assert np.abs(expected_slice - image_slice					).max() < 1E-3




					def 		lowerCAmelCase     (						self					)						->							int:
										_snake_case													=			0

										def callback_fn(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					) -> None:
															_snake_case													=			True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																				_snake_case													=			latents.detach().cpu().numpy()
																				assert latents.shape == (1, 4, 64, 256)
																				_snake_case													=			latents[0, -3:, -3:, -1]

																				_snake_case													=			np.array(
																				    [
																				        0.18_68_18_69,
																				        0.33_90_78_16,
																				        0.5_36_12_76,
																				        0.14_43_28_65,
																				        -0.02_85_66_11,
																				        -0.73_94_11_23,
																				        0.23_39_79_87,
																				        0.47_32_26_82,
																				        -0.37_82_31_64,
																				    ]					)
																				assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2
															elif step == 2:
																				_snake_case													=			latents.detach().cpu().numpy()
																				assert latents.shape == (1, 4, 64, 256)
																				_snake_case													=			latents[0, -3:, -3:, -1]

																				_snake_case													=			np.array(
																				    [
																				        0.18_53_96_45,
																				        0.33_98_72_48,
																				        0.5_37_85_59,
																				        0.14_43_71_42,
																				        -0.02_45_52_61,
																				        -0.7_33_83_17,
																				        0.23_99_07_55,
																				        0.47_35_62_72,
																				        -0.3_78_65_05,
																				    ]					)

																				assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2

										_snake_case													=			False

										_snake_case													=			'stabilityai/stable-diffusion-2-base'
										_snake_case													=			DDIMScheduler.from_pretrained(lowerCAmelCase_			,  subfolder='scheduler'					)
										_snake_case													=			StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase_			,  scheduler=lowerCAmelCase_			,  safety_checker=lowerCAmelCase_					)
										_snake_case													=			pipe.to(lowerCAmelCase_					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
										pipe.enable_attention_slicing()

										_snake_case													=			self.get_inputs()
										pipe(**lowerCAmelCase_			,  callback=lowerCAmelCase_			,  callback_steps=1					)
										assert callback_fn.has_been_called
										assert number_of_steps == 3






					def 		lowerCAmelCase     (						self					)						->							Dict:
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										_snake_case													=			'stabilityai/stable-diffusion-2-base'
										_snake_case													=			DDIMScheduler.from_pretrained(lowerCAmelCase_			,  subfolder='scheduler'					)
										_snake_case													=			StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase_			,  scheduler=lowerCAmelCase_			,  safety_checker=lowerCAmelCase_					)
										_snake_case													=			pipe.to(lowerCAmelCase_					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
										pipe.enable_attention_slicing(1					)
										pipe.enable_sequential_cpu_offload()

										_snake_case													=			self.get_inputs()
										_snake_case													=			pipe(**lowerCAmelCase_					)

										_snake_case													=			torch.cuda.max_memory_allocated()
										# make sure that less than 5.2 GB is allocated
										assert mem_bytes < 5.5 * 10**9










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_ 							=       logging.get_logger(__name__)

# General docstring
UpperCAmelCase_ 							=       """ResNetConfig"""

# Base docstring
UpperCAmelCase_ 							=       """microsoft/resnet-50"""
UpperCAmelCase_ 							=       [1, 2048, 7, 7]

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

UpperCAmelCase_ 							=       [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]
class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 3			,  lowerCAmelCase_ = 1			,  lowerCAmelCase_ = "relu"					)						->							Union[str, Any]:
										super().__init__()
										_snake_case													=			nn.Convad(
										    lowerCAmelCase_			,  lowerCAmelCase_			,  kernel_size=lowerCAmelCase_			,  stride=lowerCAmelCase_			,  padding=kernel_size // 2			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.BatchNormad(lowerCAmelCase_					)
										_snake_case													=			ACTaFN[activation] if activation is not None else nn.Identity()






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			self.convolution(lowerCAmelCase_					)
										_snake_case													=			self.normalization(lowerCAmelCase_					)
										_snake_case													=			self.activation(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							Dict:
										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 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			pixel_values.shape[1]
										if num_channels != self.num_channels:
															raise ValueError(
															    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'					)
										_snake_case													=			self.embedder(lowerCAmelCase_					)
										_snake_case													=			self.pooler(lowerCAmelCase_					)
										return embedding





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 2					)						->							List[Any]:
										super().__init__()
										_snake_case													=			nn.Convad(lowerCAmelCase_			,  lowerCAmelCase_			,  kernel_size=1			,  stride=lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.BatchNormad(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			self.convolution(lowerCAmelCase_					)
										_snake_case													=			self.normalization(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							str:
										_snake_case													=			hidden_state
										_snake_case													=			self.layer(lowerCAmelCase_					)
										_snake_case													=			self.shortcut(lowerCAmelCase_					)
										hidden_state += residual
										_snake_case													=			self.activation(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case													=			hidden_state
										_snake_case													=			self.layer(lowerCAmelCase_					)
										_snake_case													=			self.shortcut(lowerCAmelCase_					)
										hidden_state += residual
										_snake_case													=			self.activation(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 2			,  lowerCAmelCase_ = 2			,  )						->							Tuple:
										super().__init__()

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

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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			input
										for layer in self.layers:
															_snake_case													=			layer(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							int:
										super().__init__()
										_snake_case													=			nn.ModuleList([]					)
										# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
										self.stages.append(
										    ResNetStage(
										        lowerCAmelCase_			,  config.embedding_size			,  config.hidden_sizes[0]			,  stride=2 if config.downsample_in_first_stage else 1			,  depth=config.depths[0]			,  )					)
										_snake_case													=			zip(config.hidden_sizes			,  config.hidden_sizes[1:]					)
										for (in_channels, out_channels), depth in zip(lowerCAmelCase_			,  config.depths[1:]					):
															self.stages.append(ResNetStage(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  depth=lowerCAmelCase_					)					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = True					)						->							BaseModelOutputWithNoAttention:
										_snake_case													=			() if output_hidden_states else None

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

															_snake_case													=			stage_module(lowerCAmelCase_					)

										if output_hidden_states:
															_snake_case													=			hidden_states + (hidden_state,)

										if not return_dict:
															return tuple(v for v in [hidden_state, hidden_states] if v is not None					)

										return BaseModelOutputWithNoAttention(
										    last_hidden_state=lowerCAmelCase_			,  hidden_states=lowerCAmelCase_			,  )





class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		ResNetConfig
					lowerCAmelCase_							=		'''resnet'''
					lowerCAmelCase_							=		'''pixel_values'''
					lowerCAmelCase_							=		True




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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=False					)						->							List[Any]:
										if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
															_snake_case													=			value





UpperCAmelCase_ 							=       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_ 							=       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.'''							,					_lowerCamelCase							,					)
class      UpperCamelCase_						(					_lowerCamelCase   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							int:
										super().__init__(lowerCAmelCase_					)
										_snake_case													=			config
										_snake_case													=			ResNetEmbeddings(lowerCAmelCase_					)
										_snake_case													=			ResNetEncoder(lowerCAmelCase_					)
										_snake_case													=			nn.AdaptiveAvgPoolad((1, 1)					)
										# Initialize weights and apply final processing
										self.post_init()






					@add_start_docstrings_to_model_forward(lowerCAmelCase_					)
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC			,  output_type=lowerCAmelCase_			,  config_class=_CONFIG_FOR_DOC			,  modality='vision'			,  expected_output=_EXPECTED_OUTPUT_SHAPE			,  )
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None					)						->							BaseModelOutputWithPoolingAndNoAttention:
										_snake_case													=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										_snake_case													=			return_dict if return_dict is not None else self.config.use_return_dict

										_snake_case													=			self.embedder(lowerCAmelCase_					)

										_snake_case													=			self.encoder(
										    lowerCAmelCase_			,  output_hidden_states=lowerCAmelCase_			,  return_dict=lowerCAmelCase_					)

										_snake_case													=			encoder_outputs[0]

										_snake_case													=			self.pooler(lowerCAmelCase_					)

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

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





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




					def __init__(						self			,  lowerCAmelCase_					)						->							Union[str, Any]:
										super().__init__(lowerCAmelCase_					)
										_snake_case													=			config.num_labels
										_snake_case													=			ResNetModel(lowerCAmelCase_					)
										# classification head
										_snake_case													=			nn.Sequential(
										    nn.Flatten()			,  nn.Linear(config.hidden_sizes[-1]			,  config.num_labels					) if config.num_labels > 0 else nn.Identity()			,  )
										# initialize weights and apply final processing
										self.post_init()






					@add_start_docstrings_to_model_forward(lowerCAmelCase_					)
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT			,  output_type=lowerCAmelCase_			,  config_class=_CONFIG_FOR_DOC			,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT			,  )
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  )						->							ImageClassifierOutputWithNoAttention:
										_snake_case													=			return_dict if return_dict is not None else self.config.use_return_dict

										_snake_case													=			self.resnet(lowerCAmelCase_			,  output_hidden_states=lowerCAmelCase_			,  return_dict=lowerCAmelCase_					)

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

										_snake_case													=			self.classifier(lowerCAmelCase_					)

										_snake_case													=			None

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

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

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





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




					def __init__(						self			,  lowerCAmelCase_					)						->							List[Any]:
										super().__init__(lowerCAmelCase_					)
										super()._init_backbone(lowerCAmelCase_					)

										_snake_case													=			[config.embedding_size] + config.hidden_sizes
										_snake_case													=			ResNetEmbeddings(lowerCAmelCase_					)
										_snake_case													=			ResNetEncoder(lowerCAmelCase_					)

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






					@add_start_docstrings_to_model_forward(lowerCAmelCase_					)
					@replace_return_docstrings(output_type=lowerCAmelCase_			,  config_class=_CONFIG_FOR_DOC					)
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None					)						->							BackboneOutput:
										_snake_case													=			return_dict if return_dict is not None else self.config.use_return_dict
										_snake_case													=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)

										_snake_case													=			self.embedder(lowerCAmelCase_					)

										_snake_case													=			self.encoder(lowerCAmelCase_			,  output_hidden_states=lowerCAmelCase_			,  return_dict=lowerCAmelCase_					)

										_snake_case													=			outputs.hidden_states

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

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

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



