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()
_lowercase:	Dict       			=		logging.get_logger(__name__)






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							MobileViTConfig()

							# size of the architecture
							if "mobilevit_s" in mobilevit_name:
														_lowerCAmelCase							=							[144, 192, 240]
														_lowerCAmelCase							=							[16, 32, 64, 96, 128, 160, 640]
							elif "mobilevit_xs" in mobilevit_name:
														_lowerCAmelCase							=							[96, 120, 144]
														_lowerCAmelCase							=							[16, 32, 48, 64, 80, 96, 384]
							elif "mobilevit_xxs" in mobilevit_name:
														_lowerCAmelCase							=							[64, 80, 96]
														_lowerCAmelCase							=							[16, 16, 24, 48, 64, 80, 320]
														_lowerCAmelCase							=							0.05
														_lowerCAmelCase							=							2.0

							if mobilevit_name.startswith('deeplabv3_'  ):
														_lowerCAmelCase							=							512
														_lowerCAmelCase							=							16
														_lowerCAmelCase							=							21
														_lowerCAmelCase							=							'pascal-voc-id2label.json'
							else:
														_lowerCAmelCase							=							1_000
														_lowerCAmelCase							=							'imagenet-1k-id2label.json'

							_lowerCAmelCase							=							'huggingface/label-files'
							_lowerCAmelCase							=							json.load(open(hf_hub_download(snake_case     , snake_case     , repo_type='dataset'  )     , 'r'  )  )
							_lowerCAmelCase							=							{int(snake_case  ): v for k, v in idalabel.items()}
							_lowerCAmelCase							=							idalabel
							_lowerCAmelCase							=							{v: k for k, v in idalabel.items()}

							return config






def _lowerCamelCase  (		snake_case     , snake_case=False  ):
							for i in range(1     , 6  ):
														if F'layer_{i}.' in name:
																					_lowerCAmelCase							=							name.replace(F'layer_{i}.'     , F'encoder.layer.{i - 1}.'  )

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

							if "exp_1x1" in name:
														_lowerCAmelCase							=							name.replace('exp_1x1'     , 'expand_1x1'  )
							if "red_1x1" in name:
														_lowerCAmelCase							=							name.replace('red_1x1'     , 'reduce_1x1'  )
							if ".local_rep.conv_3x3." in name:
														_lowerCAmelCase							=							name.replace('.local_rep.conv_3x3.'     , '.conv_kxk.'  )
							if ".local_rep.conv_1x1." in name:
														_lowerCAmelCase							=							name.replace('.local_rep.conv_1x1.'     , '.conv_1x1.'  )
							if ".norm." in name:
														_lowerCAmelCase							=							name.replace('.norm.'     , '.normalization.'  )
							if ".conv." in name:
														_lowerCAmelCase							=							name.replace('.conv.'     , '.convolution.'  )
							if ".conv_proj." in name:
														_lowerCAmelCase							=							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							=							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							=							name.replace(F'.{i}.{j}.'     , F'.{i}.'  )
																												if "expand_1x1" in name:
																																			_lowerCAmelCase							=							name.replace('expand_1x1'     , 'downsampling_layer.expand_1x1'  )
																												if "conv_3x3" in name:
																																			_lowerCAmelCase							=							name.replace('conv_3x3'     , 'downsampling_layer.conv_3x3'  )
																												if "reduce_1x1" in name:
																																			_lowerCAmelCase							=							name.replace('reduce_1x1'     , 'downsampling_layer.reduce_1x1'  )

							for i in range(2     , 5  ):
														if F'.global_rep.{i}.weight' in name:
																					_lowerCAmelCase							=							name.replace(F'.global_rep.{i}.weight'     , '.layernorm.weight'  )
														if F'.global_rep.{i}.bias' in name:
																					_lowerCAmelCase							=							name.replace(F'.global_rep.{i}.bias'     , '.layernorm.bias'  )

							if ".global_rep." in name:
														_lowerCAmelCase							=							name.replace('.global_rep.'     , '.transformer.'  )
							if ".pre_norm_mha.0." in name:
														_lowerCAmelCase							=							name.replace('.pre_norm_mha.0.'     , '.layernorm_before.'  )
							if ".pre_norm_mha.1.out_proj." in name:
														_lowerCAmelCase							=							name.replace('.pre_norm_mha.1.out_proj.'     , '.attention.output.dense.'  )
							if ".pre_norm_ffn.0." in name:
														_lowerCAmelCase							=							name.replace('.pre_norm_ffn.0.'     , '.layernorm_after.'  )
							if ".pre_norm_ffn.1." in name:
														_lowerCAmelCase							=							name.replace('.pre_norm_ffn.1.'     , '.intermediate.dense.'  )
							if ".pre_norm_ffn.4." in name:
														_lowerCAmelCase							=							name.replace('.pre_norm_ffn.4.'     , '.output.dense.'  )
							if ".transformer." in name:
														_lowerCAmelCase							=							name.replace('.transformer.'     , '.transformer.layer.'  )

							if ".aspp_layer." in name:
														_lowerCAmelCase							=							name.replace('.aspp_layer.'     , '.'  )
							if ".aspp_pool." in name:
														_lowerCAmelCase							=							name.replace('.aspp_pool.'     , '.'  )
							if "seg_head." in name:
														_lowerCAmelCase							=							name.replace('seg_head.'     , 'segmentation_head.'  )
							if "segmentation_head.classifier.classifier." in name:
														_lowerCAmelCase							=							name.replace('segmentation_head.classifier.classifier.'     , 'segmentation_head.classifier.'  )

							if "classifier.fc." in name:
														_lowerCAmelCase							=							name.replace('classifier.fc.'     , 'classifier.'  )
							elif (not base_model) and ("segmentation_head." not in name):
														_lowerCAmelCase							=							'mobilevit.' + name

							return name






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case=False  ):
							if base_model:
														_lowerCAmelCase							=							''
							else:
														_lowerCAmelCase							=							'mobilevit.'

							for key in orig_state_dict.copy().keys():
														_lowerCAmelCase							=							orig_state_dict.pop(snake_case  )

														if key[:8] == "encoder.":
																					_lowerCAmelCase							=							key[8:]

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

							return orig_state_dict






def _lowerCamelCase  (		):
							_lowerCAmelCase							=							'http://images.cocodataset.org/val2017/000000039769.jpg'
							_lowerCAmelCase							=							Image.open(requests.get(snake_case     , stream=snake_case  ).raw  )
							return im






@torch.no_grad()
def _lowerCamelCase  (		snake_case     , snake_case     , snake_case     , snake_case=False  ):
							_lowerCAmelCase							=							get_mobilevit_config(snake_case  )

							# load original state_dict
							_lowerCAmelCase							=							torch.load(snake_case     , map_location='cpu'  )

							# load 🤗 model
							if mobilevit_name.startswith('deeplabv3_'  ):
														_lowerCAmelCase							=							MobileViTForSemanticSegmentation(snake_case  ).eval()
							else:
														_lowerCAmelCase							=							MobileViTForImageClassification(snake_case  ).eval()

							_lowerCAmelCase							=							convert_state_dict(snake_case     , snake_case  )
							model.load_state_dict(snake_case  )

							# Check outputs on an image, prepared by MobileViTImageProcessor
							_lowerCAmelCase							=							MobileViTImageProcessor(crop_size=config.image_size     , size=config.image_size + 32  )
							_lowerCAmelCase							=							image_processor(images=prepare_img()     , return_tensors='pt'  )
							_lowerCAmelCase							=							model(**snake_case  )
							_lowerCAmelCase							=							outputs.logits

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

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
																					        [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
																					        [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
																					    ]  )
														elif mobilevit_name == "deeplabv3_mobilevit_xs":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
																					        [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
																					        [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
																					    ]  )
														elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																					_lowerCAmelCase							=							torch.tensor(
																					    [
																					        [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
																					        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
																					        [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
																					    ]  )
														else:
																					raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'  )

														assert torch.allclose(logits[0, :3, :3, :3]     , snake_case     , atol=1E-4  )
							else:
														assert logits.shape == (1, 1_000)

														if mobilevit_name == "mobilevit_s":
																					_lowerCAmelCase							=							torch.tensor([-0.98_66, 0.23_92, -1.12_41]  )
														elif mobilevit_name == "mobilevit_xs":
																					_lowerCAmelCase							=							torch.tensor([-2.47_61, -0.93_99, -1.95_87]  )
														elif mobilevit_name == "mobilevit_xxs":
																					_lowerCAmelCase							=							torch.tensor([-1.93_64, -1.23_27, -0.46_53]  )
														else:
																					raise ValueError(F'Unknown mobilevit_name: {mobilevit_name}'  )

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

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

							if push_to_hub:
														_lowerCAmelCase							=							{
														    '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							=							model_mapping[mobilevit_name]
														image_processor.push_to_hub(snake_case     , organization='apple'  )
														model.push_to_hub(snake_case     , organization='apple'  )


if __name__ == "__main__":
				_lowercase:	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.'''
				)

				_lowercase:	List[str]       			=		parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)







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 ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


_lowercase:	Any       			=		logging.get_logger(__name__)

# General docstring
_lowercase:	List[Any]       			=		'''RegNetConfig'''

# Base docstring
_lowercase:	List[Any]       			=		'''facebook/regnet-y-040'''
_lowercase:	int       			=		[1, 1_0_8_8, 7, 7]

# Image classification docstring
_lowercase:	Union[str, Any]       			=		'''facebook/regnet-y-040'''
_lowercase:	Tuple       			=		'''tabby, tabby cat'''

_lowercase:	str       			=		[
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]



class        lowerCamelCase__				(   nn.Module				):





			def __init__(       self	:	Dict  ,  lowercase__	:	int  ,  lowercase__	:	int  ,  lowercase__	:	int = 3  ,  lowercase__	:	int = 1  ,  lowercase__	:	int = 1  ,  lowercase__	:	Optional[str] = "relu"  ,  ):
										super().__init__()
										_lowerCAmelCase							=							nn.Convad(
										    lowercase__  ,  lowercase__  ,  kernel_size=lowercase__  ,  stride=lowercase__  ,  padding=kernel_size // 2  ,  groups=lowercase__  ,  bias=lowercase__  ,  )
										_lowerCAmelCase							=							nn.BatchNormad(lowercase__						)
										_lowerCAmelCase							=							ACTaFN[activation] if activation is not None else nn.Identity()





			def   SCREAMING_SNAKE_CASE__     (       self	:	str  ,  lowercase__	:	Union[str, Any]						):
										_lowerCAmelCase							=							self.convolution(lowercase__						)
										_lowerCAmelCase							=							self.normalization(lowercase__						)
										_lowerCAmelCase							=							self.activation(lowercase__						)
										return hidden_state







class        lowerCamelCase__				(   nn.Module				):





			def __init__(       self	:	int  ,  lowercase__	:	RegNetConfig						):
										super().__init__()
										_lowerCAmelCase							=							RegNetConvLayer(
										    config.num_channels  ,  config.embedding_size  ,  kernel_size=3  ,  stride=2  ,  activation=config.hidden_act						)
										_lowerCAmelCase							=							config.num_channels





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	Optional[int]						):
										_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(lowercase__						)
										return hidden_state







class        lowerCamelCase__				(   nn.Module				):





			def __init__(       self	:	Optional[Any]  ,  lowercase__	:	int  ,  lowercase__	:	int  ,  lowercase__	:	int = 2						):
										super().__init__()
										_lowerCAmelCase							=							nn.Convad(lowercase__  ,  lowercase__  ,  kernel_size=1  ,  stride=lowercase__  ,  bias=lowercase__						)
										_lowerCAmelCase							=							nn.BatchNormad(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple  ,  lowercase__	:	Tensor						):
										_lowerCAmelCase							=							self.convolution(lowercase__						)
										_lowerCAmelCase							=							self.normalization(lowercase__						)
										return hidden_state







class        lowerCamelCase__				(   nn.Module				):





			def __init__(       self	:	Optional[Any]  ,  lowercase__	:	int  ,  lowercase__	:	int						):
										super().__init__()

										_lowerCAmelCase							=							nn.AdaptiveAvgPoolad((1, 1)						)
										_lowerCAmelCase							=							nn.Sequential(
										    nn.Convad(lowercase__  ,  lowercase__  ,  kernel_size=1						)  ,  nn.ReLU()  ,  nn.Convad(lowercase__  ,  lowercase__  ,  kernel_size=1						)  ,  nn.Sigmoid()  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	List[Any]						):
										# b c h w -> b c 1 1
										_lowerCAmelCase							=							self.pooler(lowercase__						)
										_lowerCAmelCase							=							self.attention(lowercase__						)
										_lowerCAmelCase							=							hidden_state * attention
										return hidden_state







class        lowerCamelCase__				(   nn.Module				):





			def __init__(       self	:	Dict  ,  lowercase__	:	RegNetConfig  ,  lowercase__	:	int  ,  lowercase__	:	int  ,  lowercase__	:	int = 1						):
										super().__init__()
										_lowerCAmelCase							=							in_channels != out_channels or stride != 1
										_lowerCAmelCase							=							max(1  ,  out_channels // config.groups_width						)
										_lowerCAmelCase							=							(
										    RegNetShortCut(lowercase__  ,  lowercase__  ,  stride=lowercase__						) if should_apply_shortcut else nn.Identity()
										)
										_lowerCAmelCase							=							nn.Sequential(
										    RegNetConvLayer(lowercase__  ,  lowercase__  ,  kernel_size=1  ,  activation=config.hidden_act						)  ,  RegNetConvLayer(lowercase__  ,  lowercase__  ,  stride=lowercase__  ,  groups=lowercase__  ,  activation=config.hidden_act						)  ,  RegNetConvLayer(lowercase__  ,  lowercase__  ,  kernel_size=1  ,  activation=lowercase__						)  ,  )
										_lowerCAmelCase							=							ACTaFN[config.hidden_act]





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict  ,  lowercase__	:	Any						):
										_lowerCAmelCase							=							hidden_state
										_lowerCAmelCase							=							self.layer(lowercase__						)
										_lowerCAmelCase							=							self.shortcut(lowercase__						)
										hidden_state += residual
										_lowerCAmelCase							=							self.activation(lowercase__						)
										return hidden_state







class        lowerCamelCase__				(   nn.Module				):





			def __init__(       self	:	Any  ,  lowercase__	:	RegNetConfig  ,  lowercase__	:	int  ,  lowercase__	:	int  ,  lowercase__	:	int = 1						):
										super().__init__()
										_lowerCAmelCase							=							in_channels != out_channels or stride != 1
										_lowerCAmelCase							=							max(1  ,  out_channels // config.groups_width						)
										_lowerCAmelCase							=							(
										    RegNetShortCut(lowercase__  ,  lowercase__  ,  stride=lowercase__						) if should_apply_shortcut else nn.Identity()
										)
										_lowerCAmelCase							=							nn.Sequential(
										    RegNetConvLayer(lowercase__  ,  lowercase__  ,  kernel_size=1  ,  activation=config.hidden_act						)  ,  RegNetConvLayer(lowercase__  ,  lowercase__  ,  stride=lowercase__  ,  groups=lowercase__  ,  activation=config.hidden_act						)  ,  RegNetSELayer(lowercase__  ,  reduced_channels=int(round(in_channels / 4						)						)						)  ,  RegNetConvLayer(lowercase__  ,  lowercase__  ,  kernel_size=1  ,  activation=lowercase__						)  ,  )
										_lowerCAmelCase							=							ACTaFN[config.hidden_act]





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	Tuple						):
										_lowerCAmelCase							=							hidden_state
										_lowerCAmelCase							=							self.layer(lowercase__						)
										_lowerCAmelCase							=							self.shortcut(lowercase__						)
										hidden_state += residual
										_lowerCAmelCase							=							self.activation(lowercase__						)
										return hidden_state







class        lowerCamelCase__				(   nn.Module				):





			def __init__(       self	:	Dict  ,  lowercase__	:	RegNetConfig  ,  lowercase__	:	int  ,  lowercase__	:	int  ,  lowercase__	:	int = 2  ,  lowercase__	:	int = 2  ,  ):
										super().__init__()

										_lowerCAmelCase							=							RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple  ,  lowercase__	:	Any						):
										_lowerCAmelCase							=							self.layers(lowercase__						)
										return hidden_state







class        lowerCamelCase__				(   nn.Module				):





			def __init__(       self	:	Dict  ,  lowercase__	:	RegNetConfig						):
										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(
										    RegNetStage(
										        lowercase__  ,  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(lowercase__  ,  config.depths[1:]						):
																	self.stages.append(RegNetStage(lowercase__  ,  lowercase__  ,  lowercase__  ,  depth=lowercase__						)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Tensor  ,  lowercase__	:	bool = False  ,  lowercase__	:	bool = True						):
										_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(lowercase__						)

										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=lowercase__  ,  hidden_states=lowercase__						)







class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=RegNetConfig
			UpperCamelCase__    							="regnet"
			UpperCamelCase__    							="pixel_values"
			UpperCamelCase__    							=True





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	List[Any]						):
										if isinstance(lowercase__  ,  nn.Convad						):
																	nn.init.kaiming_normal_(module.weight  ,  mode='fan_out'  ,  nonlinearity='relu'						)
										elif isinstance(lowercase__  ,  (nn.BatchNormad, nn.GroupNorm)						):
																	nn.init.constant_(module.weight  ,  1						)
																	nn.init.constant_(module.bias  ,  0						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	List[str]  ,  lowercase__	:	List[Any]=False						):
										if isinstance(lowercase__  ,  lowercase__						):
																	_lowerCAmelCase							=							value







_lowercase:	Optional[Any]       			=		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 ([`RegNetConfig`]): 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.
'''

_lowercase:	str       			=		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 [`~file_utils.ModelOutput`] instead of a plain tuple.
'''



@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."       ,UpperCAmelCase       ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class        lowerCamelCase__				(   UpperCAmelCase				):





			def __init__(       self	:	List[str]  ,  lowercase__	:	int						):
										super().__init__(lowercase__						)
										_lowerCAmelCase							=							config
										_lowerCAmelCase							=							RegNetEmbeddings(lowercase__						)
										_lowerCAmelCase							=							RegNetEncoder(lowercase__						)
										_lowerCAmelCase							=							nn.AdaptiveAvgPoolad((1, 1)						)
										# Initialize weights and apply final processing
										self.post_init()





			@add_start_docstrings_to_model_forward(lowercase__						)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC  ,  output_type=lowercase__  ,  config_class=_CONFIG_FOR_DOC  ,  modality='vision'  ,  expected_output=_EXPECTED_OUTPUT_SHAPE  ,  )
			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	Tensor  ,  lowercase__	:	Optional[bool] = None  ,  lowercase__	:	Optional[bool] = None						):
										_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(lowercase__						)

										_lowerCAmelCase							=							self.encoder(
										    lowercase__  ,  output_hidden_states=lowercase__  ,  return_dict=lowercase__						)

										_lowerCAmelCase							=							encoder_outputs[0]

										_lowerCAmelCase							=							self.pooler(lowercase__						)

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

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







@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "       ,UpperCAmelCase       ,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class        lowerCamelCase__				(   UpperCAmelCase				):





			def __init__(       self	:	str  ,  lowercase__	:	Union[str, Any]						):
										super().__init__(lowercase__						)
										_lowerCAmelCase							=							config.num_labels
										_lowerCAmelCase							=							RegNetModel(lowercase__						)
										# 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(lowercase__						)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT  ,  output_type=lowercase__  ,  config_class=_CONFIG_FOR_DOC  ,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT  ,  )
			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Optional[torch.FloatTensor] = None  ,  lowercase__	:	Optional[torch.LongTensor] = None  ,  lowercase__	:	Optional[bool] = None  ,  lowercase__	:	Optional[bool] = None  ,  ):
										_lowerCAmelCase							=							return_dict if return_dict is not None else self.config.use_return_dict

										_lowerCAmelCase							=							self.regnet(lowercase__  ,  output_hidden_states=lowercase__  ,  return_dict=lowercase__						)

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

										_lowerCAmelCase							=							self.classifier(lowercase__						)

										_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(lowercase__  ,  lowercase__						)
																	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(lowercase__  ,  lowercase__						)

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

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






