





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 (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase				:       List[str]	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Optional[int]:
    lowerCamelCase__    :      Dict					 =		MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        lowerCamelCase__    :      List[Any]					 =		[144, 192, 240]
        lowerCamelCase__    :      Optional[Any]					 =		[16, 32, 64, 96, 128, 160, 640]
    elif "mobilevit_xs" in mobilevit_name:
        lowerCamelCase__    :      Dict					 =		[96, 120, 144]
        lowerCamelCase__    :      int					 =		[16, 32, 48, 64, 80, 96, 384]
    elif "mobilevit_xxs" in mobilevit_name:
        lowerCamelCase__    :      Tuple					 =		[64, 80, 96]
        lowerCamelCase__    :      List[str]					 =		[16, 16, 24, 48, 64, 80, 320]
        lowerCamelCase__    :      Union[str, Any]					 =		0.05
        lowerCamelCase__    :      Tuple					 =		2.0

    if mobilevit_name.startswith('deeplabv3_' ):
        lowerCamelCase__    :      Union[str, Any]					 =		512
        lowerCamelCase__    :      Optional[Any]					 =		16
        lowerCamelCase__    :      Dict					 =		21
        lowerCamelCase__    :      int					 =		'pascal-voc-id2label.json'
    else:
        lowerCamelCase__    :      List[Any]					 =		1000
        lowerCamelCase__    :      int					 =		'imagenet-1k-id2label.json'

    lowerCamelCase__    :      List[Any]					 =		'huggingface/label-files'
    lowerCamelCase__    :      List[str]					 =		json.load(open(hf_hub_download(_UpperCAmelCase						,	_UpperCAmelCase						,	repo_type='dataset' )						,	'r' ) )
    lowerCamelCase__    :      Union[str, Any]					 =		{int(_UpperCAmelCase ): v for k, v in idalabel.items()}
    lowerCamelCase__    :      List[str]					 =		idalabel
    lowerCamelCase__    :      Union[str, Any]					 =		{v: k for k, v in idalabel.items()}

    return config


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase=False )			->     List[str]:
    for i in range(1						,	6 ):
        if F"""layer_{i}.""" in name:
            lowerCamelCase__    :      str					 =		name.replace(F"""layer_{i}."""						,	F"""encoder.layer.{i - 1}.""" )

    if "conv_1." in name:
        lowerCamelCase__    :      Any					 =		name.replace('conv_1.'						,	'conv_stem.' )
    if ".block." in name:
        lowerCamelCase__    :      Any					 =		name.replace('.block.'						,	'.' )

    if "exp_1x1" in name:
        lowerCamelCase__    :      str					 =		name.replace('exp_1x1'						,	'expand_1x1' )
    if "red_1x1" in name:
        lowerCamelCase__    :      List[Any]					 =		name.replace('red_1x1'						,	'reduce_1x1' )
    if ".local_rep.conv_3x3." in name:
        lowerCamelCase__    :      Tuple					 =		name.replace('.local_rep.conv_3x3.'						,	'.conv_kxk.' )
    if ".local_rep.conv_1x1." in name:
        lowerCamelCase__    :      str					 =		name.replace('.local_rep.conv_1x1.'						,	'.conv_1x1.' )
    if ".norm." in name:
        lowerCamelCase__    :      str					 =		name.replace('.norm.'						,	'.normalization.' )
    if ".conv." in name:
        lowerCamelCase__    :      List[Any]					 =		name.replace('.conv.'						,	'.convolution.' )
    if ".conv_proj." in name:
        lowerCamelCase__    :      Dict					 =		name.replace('.conv_proj.'						,	'.conv_projection.' )

    for i in range(0						,	2 ):
        for j in range(0						,	4 ):
            if F""".{i}.{j}.""" in name:
                lowerCamelCase__    :      Any					 =		name.replace(F""".{i}.{j}."""						,	F""".{i}.layer.{j}.""" )

    for i in range(2						,	6 ):
        for j in range(0						,	4 ):
            if F""".{i}.{j}.""" in name:
                lowerCamelCase__    :      Optional[int]					 =		name.replace(F""".{i}.{j}."""						,	F""".{i}.""" )
                if "expand_1x1" in name:
                    lowerCamelCase__    :      int					 =		name.replace('expand_1x1'						,	'downsampling_layer.expand_1x1' )
                if "conv_3x3" in name:
                    lowerCamelCase__    :      Union[str, Any]					 =		name.replace('conv_3x3'						,	'downsampling_layer.conv_3x3' )
                if "reduce_1x1" in name:
                    lowerCamelCase__    :      Union[str, Any]					 =		name.replace('reduce_1x1'						,	'downsampling_layer.reduce_1x1' )

    for i in range(2						,	5 ):
        if F""".global_rep.{i}.weight""" in name:
            lowerCamelCase__    :      Optional[int]					 =		name.replace(F""".global_rep.{i}.weight"""						,	'.layernorm.weight' )
        if F""".global_rep.{i}.bias""" in name:
            lowerCamelCase__    :      List[Any]					 =		name.replace(F""".global_rep.{i}.bias"""						,	'.layernorm.bias' )

    if ".global_rep." in name:
        lowerCamelCase__    :      List[Any]					 =		name.replace('.global_rep.'						,	'.transformer.' )
    if ".pre_norm_mha.0." in name:
        lowerCamelCase__    :      str					 =		name.replace('.pre_norm_mha.0.'						,	'.layernorm_before.' )
    if ".pre_norm_mha.1.out_proj." in name:
        lowerCamelCase__    :      Union[str, Any]					 =		name.replace('.pre_norm_mha.1.out_proj.'						,	'.attention.output.dense.' )
    if ".pre_norm_ffn.0." in name:
        lowerCamelCase__    :      str					 =		name.replace('.pre_norm_ffn.0.'						,	'.layernorm_after.' )
    if ".pre_norm_ffn.1." in name:
        lowerCamelCase__    :      Optional[int]					 =		name.replace('.pre_norm_ffn.1.'						,	'.intermediate.dense.' )
    if ".pre_norm_ffn.4." in name:
        lowerCamelCase__    :      Union[str, Any]					 =		name.replace('.pre_norm_ffn.4.'						,	'.output.dense.' )
    if ".transformer." in name:
        lowerCamelCase__    :      str					 =		name.replace('.transformer.'						,	'.transformer.layer.' )

    if ".aspp_layer." in name:
        lowerCamelCase__    :      Union[str, Any]					 =		name.replace('.aspp_layer.'						,	'.' )
    if ".aspp_pool." in name:
        lowerCamelCase__    :      Tuple					 =		name.replace('.aspp_pool.'						,	'.' )
    if "seg_head." in name:
        lowerCamelCase__    :      Union[str, Any]					 =		name.replace('seg_head.'						,	'segmentation_head.' )
    if "segmentation_head.classifier.classifier." in name:
        lowerCamelCase__    :      Dict					 =		name.replace('segmentation_head.classifier.classifier.'						,	'segmentation_head.classifier.' )

    if "classifier.fc." in name:
        lowerCamelCase__    :      Optional[int]					 =		name.replace('classifier.fc.'						,	'classifier.' )
    elif (not base_model) and ("segmentation_head." not in name):
        lowerCamelCase__    :      Tuple					 =		'mobilevit.' + name

    return name


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=False )			->     Tuple:
    if base_model:
        lowerCamelCase__    :      Any					 =		''
    else:
        lowerCamelCase__    :      Any					 =		'mobilevit.'

    for key in orig_state_dict.copy().keys():
        lowerCamelCase__    :      Any					 =		orig_state_dict.pop(_UpperCAmelCase )

        if key[:8] == "encoder.":
            lowerCamelCase__    :      Union[str, Any]					 =		key[8:]

        if "qkv" in key:
            lowerCamelCase__    :      Optional[int]					 =		key.split('.' )
            lowerCamelCase__    :      List[Any]					 =		int(key_split[0][6:] ) - 1
            lowerCamelCase__    :      Tuple					 =		int(key_split[3] )
            lowerCamelCase__    :      str					 =		model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
            lowerCamelCase__    :      str					 =		layer.transformer.layer[transformer_num].attention.attention.all_head_size
            lowerCamelCase__    :      Union[str, Any]					 =		(
                F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
            )
            if "weight" in key:
                lowerCamelCase__    :      Any					 =		val[:dim, :]
                lowerCamelCase__    :      Dict					 =		val[dim : dim * 2, :]
                lowerCamelCase__    :      Any					 =		val[-dim:, :]
            else:
                lowerCamelCase__    :      Tuple					 =		val[:dim]
                lowerCamelCase__    :      List[Any]					 =		val[dim : dim * 2]
                lowerCamelCase__    :      List[str]					 =		val[-dim:]
        else:
            lowerCamelCase__    :      Union[str, Any]					 =		val

    return orig_state_dict


def        SCREAMING_SNAKE_CASE      (       )			->     List[Any]:
    lowerCamelCase__    :      Union[str, Any]					 =		'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCamelCase__    :      List[str]					 =		Image.open(requests.get(_UpperCAmelCase						,	stream=_UpperCAmelCase ).raw )
    return im


@torch.no_grad()
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=False )			->     str:
    lowerCamelCase__    :      str					 =		get_mobilevit_config(_UpperCAmelCase )

    # load original state_dict
    lowerCamelCase__    :      List[str]					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )

    # load 🤗 model
    if mobilevit_name.startswith('deeplabv3_' ):
        lowerCamelCase__    :      Optional[Any]					 =		MobileViTForSemanticSegmentation(_UpperCAmelCase ).eval()
    else:
        lowerCamelCase__    :      Optional[int]					 =		MobileViTForImageClassification(_UpperCAmelCase ).eval()

    lowerCamelCase__    :      Optional[int]					 =		convert_state_dict(_UpperCAmelCase						,	_UpperCAmelCase )
    model.load_state_dict(_UpperCAmelCase )

    # Check outputs on an image, prepared by MobileViTImageProcessor
    lowerCamelCase__    :      Any					 =		MobileViTImageProcessor(crop_size=config.image_size						,	size=config.image_size + 32 )
    lowerCamelCase__    :      int					 =		image_processor(images=prepare_img()						,	return_tensors='pt' )
    lowerCamelCase__    :      Union[str, Any]					 =		model(**_UpperCAmelCase )
    lowerCamelCase__    :      int					 =		outputs.logits

    if mobilevit_name.startswith('deeplabv3_' ):
        assert logits.shape == (1, 21, 32, 32)

        if mobilevit_name == "deeplabv3_mobilevit_s":
            lowerCamelCase__    :      Any					 =		torch.tensor(
                [
                    [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
                    [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
                    [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
                ] )
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            lowerCamelCase__    :      Tuple					 =		torch.tensor(
                [
                    [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
                    [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
                    [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
                ] )
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            lowerCamelCase__    :      Optional[int]					 =		torch.tensor(
                [
                    [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
                    [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
                    [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
                ] )
        else:
            raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )

        assert torch.allclose(logits[0, :3, :3, :3]						,	_UpperCAmelCase						,	atol=1e-4 )
    else:
        assert logits.shape == (1, 1000)

        if mobilevit_name == "mobilevit_s":
            lowerCamelCase__    :      Optional[int]					 =		torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
        elif mobilevit_name == "mobilevit_xs":
            lowerCamelCase__    :      Tuple					 =		torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
        elif mobilevit_name == "mobilevit_xxs":
            lowerCamelCase__    :      str					 =		torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
        else:
            raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )

        assert torch.allclose(logits[0, :3]						,	_UpperCAmelCase						,	atol=1e-4 )

    Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
    print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
    model.save_pretrained(_UpperCAmelCase )
    print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
    image_processor.save_pretrained(_UpperCAmelCase )

    if push_to_hub:
        lowerCamelCase__    :      Union[str, Any]					 =		{
            'mobilevit_s': 'mobilevit-small',
            'mobilevit_xs': 'mobilevit-x-small',
            'mobilevit_xxs': 'mobilevit-xx-small',
            'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
            'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
            'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
        }

        print('Pushing to the hub...' )
        lowerCamelCase__    :      Tuple					 =		model_mapping[mobilevit_name]
        image_processor.push_to_hub(_UpperCAmelCase						,	organization='apple' )
        model.push_to_hub(_UpperCAmelCase						,	organization='apple' )


if __name__ == "__main__":
    _UpperCAmelCase				:       Union[str, Any]	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--mobilevit_name""",
        default="""mobilevit_s""",
        type=str,
        help=(
            """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
            """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
        ),
    )
    parser.add_argument(
        """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
    )
    parser.add_argument(
        """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
    )

    _UpperCAmelCase				:       Optional[int]	   =				parser.parse_args()
    convert_movilevit_checkpoint(
        args.mobilevit_name, args.checkpoint_path, 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


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

# General docstring
_A			:      Optional[Any]					      =				'''ResNetConfig'''

# Base docstring
_A			:      Tuple					      =				'''microsoft/resnet-50'''
_A			:      List[str]					      =				[1, 2048, 7, 7]

# Image classification docstring
_A			:      str					      =				'''microsoft/resnet-50'''
_A			:      Dict					      =				'''tiger cat'''

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







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






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





 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tensor							)  ->    Tensor:
  __lowerCAmelCase											=						self.convolution(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.normalization(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.activation(SCREAMING_SNAKE_CASE__							)
  return hidden_state







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	ResNetConfig							)  ->    List[str]:
  super().__init__()
  __lowerCAmelCase											=						ResNetConvLayer(
      config.num_channels      ,       config.embedding_size      ,       kernel_size=7      ,       stride=2      ,       activation=config.hidden_act							)
  __lowerCAmelCase											=						nn.MaxPoolad(kernel_size=3      ,       stride=2      ,       padding=1							)
  __lowerCAmelCase											=						config.num_channels





 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tensor							)  ->    Tensor:
  __lowerCAmelCase											=						pixel_values.shape[1]
  if num_channels != self.num_channels:
   raise ValueError(
       """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""							)
  __lowerCAmelCase											=						self.embedder(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.pooler(SCREAMING_SNAKE_CASE__							)
  return embedding







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






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





 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tensor							)  ->    Tensor:
  __lowerCAmelCase											=						self.convolution(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.normalization(SCREAMING_SNAKE_CASE__							)
  return hidden_state







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






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





 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    int:
  __lowerCAmelCase											=						hidden_state
  __lowerCAmelCase											=						self.layer(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.shortcut(SCREAMING_SNAKE_CASE__							)
  hidden_state += residual
  __lowerCAmelCase											=						self.activation(SCREAMING_SNAKE_CASE__							)
  return hidden_state







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






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





 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    Tuple:
  __lowerCAmelCase											=						hidden_state
  __lowerCAmelCase											=						self.layer(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						self.shortcut(SCREAMING_SNAKE_CASE__							)
  hidden_state += residual
  __lowerCAmelCase											=						self.activation(SCREAMING_SNAKE_CASE__							)
  return hidden_state







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	ResNetConfig      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	int = 2      ,       SCREAMING_SNAKE_CASE__   :	int = 2      ,       )  ->    int:
  super().__init__()

  __lowerCAmelCase											=						ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

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





 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tensor							)  ->    Tensor:
  __lowerCAmelCase											=						input
  for layer in self.layers:
   __lowerCAmelCase											=						layer(SCREAMING_SNAKE_CASE__							)
  return hidden_state







class 				_lowercase     (				nn.Module			):


 '''simple docstring'''






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





 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Tensor      ,       SCREAMING_SNAKE_CASE__   :	bool = False      ,       SCREAMING_SNAKE_CASE__   :	bool = True							)  ->    BaseModelOutputWithNoAttention:
  __lowerCAmelCase											=						() if output_hidden_states else None

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

   __lowerCAmelCase											=						stage_module(SCREAMING_SNAKE_CASE__							)

  if output_hidden_states:
   __lowerCAmelCase											=						hidden_states + (hidden_state,)

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

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







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       int      	=					ResNetConfig
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					"""resnet"""
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					"""pixel_values"""
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					True
 def a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    str:
  if isinstance(SCREAMING_SNAKE_CASE__      ,       nn.Convad							):
   nn.init.kaiming_normal_(module.weight      ,       mode="""fan_out"""      ,       nonlinearity="""relu"""							)
  elif isinstance(SCREAMING_SNAKE_CASE__      ,       (nn.BatchNormad, nn.GroupNorm)							):
   nn.init.constant_(module.weight      ,       1							)
   nn.init.constant_(module.bias      ,       0							)





 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=False							)  ->    int:
  if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						value







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

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

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

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







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


 '''simple docstring'''






 def __init__(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    List[str]:
  super().__init__(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						config
  __lowerCAmelCase											=						ResNetEmbeddings(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						ResNetEncoder(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						nn.AdaptiveAvgPoolad((1, 1)							)
  # Initialize weights and apply final processing
  self.post_init()





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

  __lowerCAmelCase											=						self.embedder(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						self.encoder(
      SCREAMING_SNAKE_CASE__      ,       output_hidden_states=SCREAMING_SNAKE_CASE__      ,       return_dict=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						encoder_outputs[0]

  __lowerCAmelCase											=						self.pooler(SCREAMING_SNAKE_CASE__							)

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

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







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


 '''simple docstring'''






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





 @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__							)
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT      ,       output_type=SCREAMING_SNAKE_CASE__      ,       config_class=_CONFIG_FOR_DOC      ,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,       )
 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	Optional[torch.FloatTensor] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[torch.LongTensor] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None      ,       )  ->    ImageClassifierOutputWithNoAttention:
  __lowerCAmelCase											=						return_dict if return_dict is not None else self.config.use_return_dict

  __lowerCAmelCase											=						self.resnet(SCREAMING_SNAKE_CASE__      ,       output_hidden_states=SCREAMING_SNAKE_CASE__      ,       return_dict=SCREAMING_SNAKE_CASE__							)

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

  __lowerCAmelCase											=						self.classifier(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						None

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

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

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







@add_start_docstrings(
    """
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    """  ,							UpperCAmelCase__  ,							)
class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__			):


 '''simple docstring'''






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

  __lowerCAmelCase											=						[config.embedding_size] + config.hidden_sizes
  __lowerCAmelCase											=						ResNetEmbeddings(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						ResNetEncoder(SCREAMING_SNAKE_CASE__							)

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





 @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__							)
 @replace_return_docstrings(output_type=SCREAMING_SNAKE_CASE__      ,       config_class=_CONFIG_FOR_DOC							)
 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tensor      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[bool] = None							)  ->    BackboneOutput:
  __lowerCAmelCase											=						return_dict if return_dict is not None else self.config.use_return_dict
  __lowerCAmelCase											=						(
      output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
  )

  __lowerCAmelCase											=						self.embedder(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						self.encoder(SCREAMING_SNAKE_CASE__      ,       output_hidden_states=SCREAMING_SNAKE_CASE__      ,       return_dict=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						outputs.hidden_states

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

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

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






