


"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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







def   _a (					_SCREAMING_SNAKE_CASE )			-> Dict:





							if "resnet-50" in model_name:
														snake_case_							=						ResNetConfig.from_pretrained("""microsoft/resnet-50""" )
							elif "resnet-101" in model_name:
														snake_case_							=						ResNetConfig.from_pretrained("""microsoft/resnet-101""" )
							else:
														raise ValueError("""Model name should include either resnet50 or resnet101""" )

							snake_case_							=						DetrConfig(use_timm_backbone=UpperCamelCase__ ,       backbone_config=UpperCamelCase__ )

							# set label attributes
							snake_case_							=						"""panoptic""" in model_name
							if is_panoptic:
														snake_case_							=						250
							else:
														snake_case_							=						91
														snake_case_							=						"""huggingface/label-files"""
														snake_case_							=						"""coco-detection-id2label.json"""
														snake_case_							=						json.load(open(hf_hub_download(UpperCamelCase__ ,       UpperCamelCase__ ,       repo_type="""dataset""" ) ,       """r""" ) )
														snake_case_							=						{int(UpperCamelCase__ ): v for k, v in idalabel.items()}
														snake_case_							=						idalabel
														snake_case_							=						{v: k for k, v in idalabel.items()}

							return config, is_panoptic







def   _a (					_SCREAMING_SNAKE_CASE )			-> int:
							snake_case_							=						[]

							# stem
							# fmt: off
							rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""") )
							rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""") )
							rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""") )
							rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""") )
							rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""") )
							# stages
							for stage_idx in range(len(config.backbone_config.depths ) ):
														for layer_idx in range(config.backbone_config.depths[stage_idx] ):
																					# shortcut
																					if layer_idx == 0:
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
																												    ) )
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
																												    ) )
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
																												    ) )
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
																												    ) )
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
																												    ) )
																					# 3 convs
																					for i in range(3 ):
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
																												    ) )
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
																												    ) )
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
																												    ) )
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
																												    ) )
																												rename_keys.append(
																												    (
																												        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
																												        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																												    ) )
    # fmt: on

							for i in range(config.encoder_layers ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (
														        f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
														        f"""encoder.layers.{i}.self_attn.out_proj.weight""",
														    ) )
														rename_keys.append(
														    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""") )
														rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight""") )
														rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias""") )
														rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight""") )
														rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias""") )
														rename_keys.append(
														    (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""") )
														rename_keys.append(
														    (f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias""") )
														rename_keys.append(
														    (f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight""") )
														rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias""") )
														# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
														rename_keys.append(
														    (
														        f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
														        f"""decoder.layers.{i}.self_attn.out_proj.weight""",
														    ) )
														rename_keys.append(
														    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""") )
														rename_keys.append(
														    (
														        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
														        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
														    ) )
														rename_keys.append(
														    (
														        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
														        f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
														    ) )
														rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight""") )
														rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias""") )
														rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight""") )
														rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias""") )
														rename_keys.append(
														    (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""") )
														rename_keys.append(
														    (f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias""") )
														rename_keys.append(
														    (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""") )
														rename_keys.append(
														    (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""") )
														rename_keys.append(
														    (f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight""") )
														rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias""") )

							# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
							rename_keys.extend(
							    [
							        ("""input_proj.weight""", """input_projection.weight"""),
							        ("""input_proj.bias""", """input_projection.bias"""),
							        ("""query_embed.weight""", """query_position_embeddings.weight"""),
							        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
							        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
							        ("""class_embed.weight""", """class_labels_classifier.weight"""),
							        ("""class_embed.bias""", """class_labels_classifier.bias"""),
							        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
							        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
							        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
							        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
							        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
							        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
							    ] )

							return rename_keys







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> Optional[int]:
							snake_case_							=						state_dict.pop(UpperCamelCase__ )
							snake_case_							=						val







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE=False )			-> int:
							snake_case_							=						""""""
							if is_panoptic:
														snake_case_							=						"""detr."""

							# first: transformer encoder
							for i in range(6 ):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														snake_case_							=						state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
														snake_case_							=						state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
														# next, add query, keys and values (in that order) to the state dict
														snake_case_							=						in_proj_weight[:256, :]
														snake_case_							=						in_proj_bias[:256]
														snake_case_							=						in_proj_weight[256:512, :]
														snake_case_							=						in_proj_bias[256:512]
														snake_case_							=						in_proj_weight[-256:, :]
														snake_case_							=						in_proj_bias[-256:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6 ):
														# read in weights + bias of input projection layer of self-attention
														snake_case_							=						state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
														snake_case_							=						state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
														# next, add query, keys and values (in that order) to the state dict
														snake_case_							=						in_proj_weight[:256, :]
														snake_case_							=						in_proj_bias[:256]
														snake_case_							=						in_proj_weight[256:512, :]
														snake_case_							=						in_proj_bias[256:512]
														snake_case_							=						in_proj_weight[-256:, :]
														snake_case_							=						in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														snake_case_							=						state_dict.pop(
														    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
														snake_case_							=						state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														snake_case_							=						in_proj_weight_cross_attn[:256, :]
														snake_case_							=						in_proj_bias_cross_attn[:256]
														snake_case_							=						in_proj_weight_cross_attn[256:512, :]
														snake_case_							=						in_proj_bias_cross_attn[256:512]
														snake_case_							=						in_proj_weight_cross_attn[-256:, :]
														snake_case_							=						in_proj_bias_cross_attn[-256:]







def   _a (					)			-> Union[str, Any]:
							snake_case_							=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							snake_case_							=						Image.open(requests.get(UpperCamelCase__ ,       stream=UpperCamelCase__ ).raw )

							return im







@torch.no_grad()
def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE=None ,       _SCREAMING_SNAKE_CASE=False )			-> List[str]:
							snake_case_				,  snake_case_							=						get_detr_config(UpperCamelCase__ )

							# load original model from torch hub
							snake_case_							=						{
							    """detr-resnet-50""": """detr_resnet50""",
							    """detr-resnet-101""": """detr_resnet101""",
							}
							logger.info(f"""Converting model {model_name}...""" )
							snake_case_							=						torch.hub.load("""facebookresearch/detr""" ,       model_name_to_original_name[model_name] ,       pretrained=UpperCamelCase__ ).eval()
							snake_case_							=						detr.state_dict()
							# rename keys
							for src, dest in create_rename_keys(UpperCamelCase__ ):
														if is_panoptic:
																					snake_case_							=						"""detr.""" + src
														rename_key(UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ )
							# query, key and value matrices need special treatment
							read_in_q_k_v(UpperCamelCase__ ,       is_panoptic=UpperCamelCase__ )
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							snake_case_							=						"""detr.model.""" if is_panoptic else """model."""
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith("""detr""" )
																					    and not key.startswith("""class_labels_classifier""" )
																					    and not key.startswith("""bbox_predictor""" )
																					):
																												snake_case_							=						state_dict.pop(UpperCamelCase__ )
																												snake_case_							=						val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												snake_case_							=						state_dict.pop(UpperCamelCase__ )
																												snake_case_							=						val
																					elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
																												continue
																					else:
																												snake_case_							=						state_dict.pop(UpperCamelCase__ )
																												snake_case_							=						val
														else:
																					if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
																												snake_case_							=						state_dict.pop(UpperCamelCase__ )
																												snake_case_							=						val

    # finally, create HuggingFace model and load state dict
							snake_case_							=						DetrForSegmentation(UpperCamelCase__ ) if is_panoptic else DetrForObjectDetection(UpperCamelCase__ )
							model.load_state_dict(UpperCamelCase__ )
							model.eval()

							# verify our conversion on an image
							snake_case_							=						"""coco_panoptic""" if is_panoptic else """coco_detection"""
							snake_case_							=						DetrImageProcessor(format=UpperCamelCase__ )

							snake_case_							=						processor(images=prepare_img() ,       return_tensors="""pt""" )
							snake_case_							=						encoding["""pixel_values"""]

							snake_case_							=						detr(UpperCamelCase__ )
							snake_case_							=						model(UpperCamelCase__ )

							assert torch.allclose(outputs.logits ,       original_outputs["""pred_logits"""] ,       atol=1E-3 )
							assert torch.allclose(outputs.pred_boxes ,       original_outputs["""pred_boxes"""] ,       atol=1E-3 )
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks ,       original_outputs["""pred_masks"""] ,       atol=1E-4 )
							print("""Looks ok!""" )

							if pytorch_dump_folder_path is not None:
														# Save model and image processor
														logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
														Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
														model.save_pretrained(UpperCamelCase__ )
														processor.save_pretrained(UpperCamelCase__ )

							if push_to_hub:
														# Upload model and image processor to the hub
														logger.info("""Uploading PyTorch model and image processor to the hub...""" )
														model.push_to_hub(f"""nielsr/{model_name}""" )
														processor.push_to_hub(f"""nielsr/{model_name}""" )


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:			str						=			argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name',
				    default='detr-resnet-50',
				    type=str,
				    choices=['detr-resnet-50', 'detr-resnet-101'],
				    help='Name of the DETR model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
				)
				parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
				__SCREAMING_SNAKE_CASE							:			int						=			parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""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						,)





