


'''simple docstring'''






import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
		import bitsandbytes as bnb

from copy import deepcopy


__UpperCAmelCase					       =							logging.getLogger(__name__)


def       _snake_case     (  A				,					A				,					A = None				,					A = None				,					A = None				,					A = None				,					A = None				,					A = False				,					)		->					int:
							lowerCAmelCase__      =			bnb_quantization_config.load_in_abit
							lowerCAmelCase__      =			bnb_quantization_config.load_in_abit

							if load_in_abit and not is_abit_bnb_available():
														raise ImportError(
														    '''You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'''
														    ''' make sure you have the latest version of `bitsandbytes` installed.''' )
							if load_in_abit and not is_abit_bnb_available():
														raise ValueError(
														    '''You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'''
														    '''make sure you have the latest version of `bitsandbytes` installed.''' )

							lowerCAmelCase__      =			[]
							# custom device map
							if isinstance(A				,					A ) and len(device_map.keys() ) > 1:
														lowerCAmelCase__      =			[key for key, value in device_map.items() if value in ['''disk''', '''cpu''']]

							# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
							if bnb_quantization_config.skip_modules is None:
														lowerCAmelCase__      =			get_keys_to_not_convert(A )

							# add cpu modules to skip modules only for 4-bit modules
							if load_in_abit:
														bnb_quantization_config.skip_modules.extend(A )
							lowerCAmelCase__      =			bnb_quantization_config.skip_modules

							# We add the modules we want to keep in full precision
							if bnb_quantization_config.keep_in_fpaa_modules is None:
														lowerCAmelCase__      =			[]
							lowerCAmelCase__      =			bnb_quantization_config.keep_in_fpaa_modules
							modules_to_not_convert.extend(A )

							# compatibility with peft
							lowerCAmelCase__      =			load_in_abit
							lowerCAmelCase__      =			load_in_abit

							lowerCAmelCase__      =			get_parameter_device(A )
							if model_device.type != "meta":
														# quantization of an already loaded model
														logger.warning(
														    '''It is not recommended to quantize a loaded model. '''
														    '''The model should be instantiated under the `init_empty_weights` context manager.''' )
														lowerCAmelCase__      =			replace_with_bnb_layers(A				,					A				,					modules_to_not_convert=A )
														# convert param to the right dtype
														lowerCAmelCase__      =			bnb_quantization_config.torch_dtype
														for name, param in model.state_dict().items():
																					if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules ):
																												param.to(torch.floataa )
																												if param.dtype != torch.floataa:
																																			lowerCAmelCase__      =			name.replace('''.weight'''				,					'''''' ).replace('''.bias'''				,					'''''' )
																																			lowerCAmelCase__      =			getattr(A				,					A				,					A )
																																			if param is not None:
																																										param.to(torch.floataa )
																					elif torch.is_floating_point(A ):
																												param.to(A )
														if model_device.type == "cuda":
																					# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
																					model.cuda(torch.cuda.current_device() )
																					torch.cuda.empty_cache()
														elif torch.cuda.is_available():
																					model.to(torch.cuda.current_device() )
														else:
																					raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
														logger.info(
														    F"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
														    '''We move the model to cuda.''' )
														return model

							elif weights_location is None:
														raise RuntimeError(
														    F"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """ )

							else:
														with init_empty_weights():
																					lowerCAmelCase__      =			replace_with_bnb_layers(
																					    A				,					A				,					modules_to_not_convert=A )

														lowerCAmelCase__      =			get_quantized_model_device_map(
														    A				,					A				,					A				,					max_memory=A				,					no_split_module_classes=A				,					)
														if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
																					lowerCAmelCase__      =			True

														lowerCAmelCase__      =			any(x in list(device_map.values() ) for x in ['''cpu''', '''disk'''] )

														load_checkpoint_in_model(
														    A				,					A				,					A				,					dtype=bnb_quantization_config.torch_dtype				,					offload_folder=A				,					offload_state_dict=A				,					keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules				,					offload_abit_bnb=load_in_abit and offload				,					)
														return dispatch_model(A				,					device_map=A				,					offload_dir=A )


def       _snake_case     (  A				,					A				,					A=None				,					A=None				,					A=None )		->					Dict:
							if device_map is None:
														if torch.cuda.is_available():
																					lowerCAmelCase__      =			{'''''': torch.cuda.current_device()}
														else:
																					raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
														logger.info('''The device_map was not initialized.''' '''Setting device_map to `{\'\':torch.cuda.current_device()}`.''' )

							if isinstance(A				,					A ):
														if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
																					raise ValueError(
																					    '''If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '''
																					    '''\'sequential\'.''' )

														lowerCAmelCase__      =			{}
														special_dtypes.update(
														    {
														        name: bnb_quantization_config.torch_dtype
														        for name, _ in model.named_parameters()
														        if any(m in name for m in bnb_quantization_config.skip_modules )
														    } )
														special_dtypes.update(
														    {
														        name: torch.floataa
														        for name, _ in model.named_parameters()
														        if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules )
														    } )

														lowerCAmelCase__      =			{}
														lowerCAmelCase__      =			special_dtypes
														lowerCAmelCase__      =			no_split_module_classes
														lowerCAmelCase__      =			bnb_quantization_config.target_dtype

														# get max_memory for each device.
														if device_map != "sequential":
																					lowerCAmelCase__      =			get_balanced_memory(
																					    A				,					low_zero=(device_map == '''balanced_low_0''')				,					max_memory=A				,					**A				,					)

														lowerCAmelCase__      =			max_memory
														lowerCAmelCase__      =			infer_auto_device_map(A				,					**A )

							if isinstance(A				,					A ):
														# check if don't have any quantized module on the cpu
														lowerCAmelCase__      =			bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

														lowerCAmelCase__      =			{
														    key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
														}
														for device in ["cpu", "disk"]:
																					if device in device_map_without_some_modules.values():
																												if bnb_quantization_config.load_in_abit:
																																			raise ValueError(
																																			    '''
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        ''' )
																												else:
																																			logger.info(
																																			    '''Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit''' )
														del device_map_without_some_modules
							return device_map


def       _snake_case     (  A				,					A				,					A=None				,					A=None )		->					str:

							if modules_to_not_convert is None:
														lowerCAmelCase__      =			[]

							lowerCAmelCase__     ,      lowerCAmelCase__      =			_replace_with_bnb_layers(
							    A				,					A				,					A				,					A )
							if not has_been_replaced:
														logger.warning(
														    '''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
														    ''' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'''
														    ''' Please double check your model architecture, or submit an issue on github if you think this is'''
														    ''' a bug.''' )
							return model


def       _snake_case     (  A				,					A				,					A=None				,					A=None				,					)		->					Any:
							lowerCAmelCase__      =			False
							for name, module in model.named_children():
														if current_key_name is None:
																					lowerCAmelCase__      =			[]
														current_key_name.append(A )
														if isinstance(A				,					nn.Linear ) and name not in modules_to_not_convert:
																					# Check if the current key is not in the `modules_to_not_convert`
																					lowerCAmelCase__      =			'''.'''.join(A )
																					lowerCAmelCase__      =			True
																					for key in modules_to_not_convert:
																												if (
																												    (key in current_key_name_str) and (key + "." in current_key_name_str)
																												) or key == current_key_name_str:
																																			lowerCAmelCase__      =			False
																																			break
																					if proceed:
																												# Load bnb module with empty weight and replace ``nn.Linear` module
																												if bnb_quantization_config.load_in_abit:
																																			lowerCAmelCase__      =			bnb.nn.LinearabitLt(
																																			    module.in_features				,					module.out_features				,					module.bias is not None				,					has_fpaa_weights=A				,					threshold=bnb_quantization_config.llm_inta_threshold				,					)
																												elif bnb_quantization_config.load_in_abit:
																																			lowerCAmelCase__      =			bnb.nn.Linearabit(
																																			    module.in_features				,					module.out_features				,					module.bias is not None				,					bnb_quantization_config.bnb_abit_compute_dtype				,					compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant				,					quant_type=bnb_quantization_config.bnb_abit_quant_type				,					)
																												else:
																																			raise ValueError('''load_in_8bit and load_in_4bit can\'t be both False''' )
																												lowerCAmelCase__      =			module.weight.data
																												if module.bias is not None:
																																			lowerCAmelCase__      =			module.bias.data
																												bnb_module.requires_grad_(A )
																												setattr(A				,					A				,					A )
																												lowerCAmelCase__      =			True
														if len(list(module.children() ) ) > 0:
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			_replace_with_bnb_layers(
																					    A				,					A				,					A				,					A )
																					lowerCAmelCase__      =			has_been_replaced | _has_been_replaced
														# Remove the last key for recursion
														current_key_name.pop(-1 )
							return model, has_been_replaced


def       _snake_case     (  A )		->					Dict:
							# Create a copy of the model
							with init_empty_weights():
														lowerCAmelCase__      =			deepcopy(A )  # this has 0 cost since it is done inside `init_empty_weights` context manager`

							lowerCAmelCase__      =			find_tied_parameters(A )
							# For compatibility with Accelerate < 0.18
							if isinstance(A				,					A ):
														lowerCAmelCase__      =			sum(list(tied_params.values() )				,					[] ) + list(tied_params.keys() )
							else:
														lowerCAmelCase__      =			sum(A				,					[] )
							lowerCAmelCase__      =			len(A ) > 0

							# Check if it is a base model
							lowerCAmelCase__      =			False
							if hasattr(A				,					'''base_model_prefix''' ):
														lowerCAmelCase__      =			not hasattr(A				,					model.base_model_prefix )

							# Ignore this for base models (BertModel, GPT2Model, etc.)
							if (not has_tied_params) and is_base_model:
														return []

							# otherwise they have an attached head
							lowerCAmelCase__      =			list(model.named_children() )
							lowerCAmelCase__      =			[list_modules[-1][0]]

							# add last module together with tied weights
							lowerCAmelCase__      =			set(A ) - set(A )
							lowerCAmelCase__      =			list(set(A ) ) + list(A )

							# remove ".weight" from the keys
							lowerCAmelCase__      =			['''.weight''', '''.bias''']
							lowerCAmelCase__      =			[]
							for name in list_untouched:
														for name_to_remove in names_to_remove:
																					if name_to_remove in name:
																												lowerCAmelCase__      =			name.replace(A				,					'''''' )
														filtered_module_names.append(A )

							return filtered_module_names


def       _snake_case     (  A )		->					str:
							for m in model.modules():
														if isinstance(A				,					bnb.nn.Linearabit ):
																					return True
							return False


def       _snake_case     (  A )		->					int:
							return next(parameter.parameters() ).device






def       _snake_case     (  A				,					A				,					A				,					A				,					A				,					A				,					A )		->					int:
							# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
							if fpaa_statistics is None:
														set_module_tensor_to_device(A				,					A				,					0				,					dtype=A				,					value=A )
														lowerCAmelCase__      =			param_name
														lowerCAmelCase__      =			model
														if "." in tensor_name:
																					lowerCAmelCase__      =			tensor_name.split('''.''' )
																					for split in splits[:-1]:
																												lowerCAmelCase__      =			getattr(A				,					A )
																												if new_module is None:
																																			raise ValueError(F"""{module} has no attribute {split}.""" )
																												lowerCAmelCase__      =			new_module
																					lowerCAmelCase__      =			splits[-1]
														# offload weights
														lowerCAmelCase__      =			False
														offload_weight(module._parameters[tensor_name]				,					A				,					A				,					index=A )
														if hasattr(module._parameters[tensor_name]				,					'''SCB''' ):
																					offload_weight(
																					    module._parameters[tensor_name].SCB				,					param_name.replace('''weight'''				,					'''SCB''' )				,					A				,					index=A				,					)
							else:
														offload_weight(A				,					A				,					A				,					index=A )
														offload_weight(A				,					param_name.replace('''weight'''				,					'''SCB''' )				,					A				,					index=A )

							set_module_tensor_to_device(A				,					A				,					'''meta'''				,					dtype=A				,					value=torch.empty(*param.size() ) )



'''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 ...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


__UpperCAmelCase					       =							logging.get_logger(__name__)

# General docstring
__UpperCAmelCase					       =							'''RegNetConfig'''

# Base docstring
__UpperCAmelCase					       =							'''facebook/regnet-y-040'''
__UpperCAmelCase					       =							[1, 1_088, 7, 7]

# Image classification docstring
__UpperCAmelCase					       =							'''facebook/regnet-y-040'''
__UpperCAmelCase					       =							'''tabby, tabby cat'''

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




class 	a__       (							nn.Module						):





							'''simple docstring'''






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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Dict:
														lowerCAmelCase__      =			self.convolution(lowerCamelCase_     )
														lowerCAmelCase__      =			self.normalization(lowerCamelCase_     )
														lowerCAmelCase__      =			self.activation(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							Optional[Any]:
														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      ,					lowerCamelCase_     ) ->							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(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = 2     ) ->							Any:
														super().__init__()
														lowerCAmelCase__      =			nn.Convad(lowerCamelCase_      ,					lowerCamelCase_      ,					kernel_size=1      ,					stride=lowerCamelCase_      ,					bias=lowerCamelCase_     )
														lowerCAmelCase__      =			nn.BatchNormad(lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Tensor:
														lowerCAmelCase__      =			self.convolution(lowerCamelCase_     )
														lowerCAmelCase__      =			self.normalization(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






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

														lowerCAmelCase__      =			nn.AdaptiveAvgPoolad((1, 1)     )
														lowerCAmelCase__      =			nn.Sequential(
														    nn.Convad(lowerCamelCase_      ,					lowerCamelCase_      ,					kernel_size=1     )      ,					nn.ReLU()      ,					nn.Convad(lowerCamelCase_      ,					lowerCamelCase_      ,					kernel_size=1     )      ,					nn.Sigmoid()      ,					)







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							int:
														# b c h w -> b c 1 1
														lowerCAmelCase__      =			self.pooler(lowerCamelCase_     )
														lowerCAmelCase__      =			self.attention(lowerCamelCase_     )
														lowerCAmelCase__      =			hidden_state * attention
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							List[str]:
														lowerCAmelCase__      =			hidden_state
														lowerCAmelCase__      =			self.layer(lowerCamelCase_     )
														lowerCAmelCase__      =			self.shortcut(lowerCamelCase_     )
														hidden_state += residual
														lowerCAmelCase__      =			self.activation(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			hidden_state
														lowerCAmelCase__      =			self.layer(lowerCamelCase_     )
														lowerCAmelCase__      =			self.shortcut(lowerCamelCase_     )
														hidden_state += residual
														lowerCAmelCase__      =			self.activation(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_ = 2      ,					lowerCamelCase_ = 2      ,					) ->							Dict:
														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(
														        lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					stride=lowerCamelCase_      ,					)      ,					*[layer(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) for _ in range(depth - 1     )]      ,					)







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							List[str]:
														lowerCAmelCase__      =			self.layers(lowerCamelCase_     )
														return hidden_state




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							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(
														    RegNetStage(
														        lowerCamelCase_      ,					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(lowerCamelCase_      ,					config.depths[1:]     ):
																					self.stages.append(RegNetStage(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					depth=lowerCamelCase_     )     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = False      ,					lowerCamelCase_ = 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(lowerCamelCase_     )

														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=lowerCamelCase_      ,					hidden_states=lowerCamelCase_     )




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     List[Any]					=   RegNetConfig
							lowercase__    :     Tuple					=   "regnet"
							lowercase__    :     List[str]					=   "pixel_values"
							lowercase__    :     Tuple					=   True







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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=False     ) ->							int:
														if isinstance(lowerCamelCase_      ,					lowerCamelCase_     ):
																					lowerCAmelCase__      =			value

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

    Parameters:
        config ([`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.
'''

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

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




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





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							Optional[int]:
														super().__init__(lowerCamelCase_     )
														lowerCAmelCase__      =			config
														lowerCAmelCase__      =			RegNetEmbeddings(lowerCamelCase_     )
														lowerCAmelCase__      =			RegNetEncoder(lowerCamelCase_     )
														lowerCAmelCase__      =			nn.AdaptiveAvgPoolad((1, 1)     )
														# Initialize weights and apply final processing
														self.post_init()







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

														lowerCAmelCase__      =			self.encoder(
														    lowerCamelCase_      ,					output_hidden_states=lowerCamelCase_      ,					return_dict=lowerCamelCase_     )

														lowerCAmelCase__      =			encoder_outputs[0]

														lowerCAmelCase__      =			self.pooler(lowerCamelCase_     )

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

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




@add_start_docstrings(
    "\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    "       ,       a__       ,       )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							Optional[Any]:
														super().__init__(lowerCamelCase_     )
														lowerCAmelCase__      =			config.num_labels
														lowerCAmelCase__      =			RegNetModel(lowerCamelCase_     )
														# 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(lowerCamelCase_     )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT      ,					output_type=lowerCamelCase_      ,					config_class=_CONFIG_FOR_DOC      ,					expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,					)
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					) ->							ImageClassifierOutputWithNoAttention:
														lowerCAmelCase__      =			return_dict if return_dict is not None else self.config.use_return_dict

														lowerCAmelCase__      =			self.regnet(lowerCamelCase_      ,					output_hidden_states=lowerCamelCase_      ,					return_dict=lowerCamelCase_     )

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

														lowerCAmelCase__      =			self.classifier(lowerCamelCase_     )

														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(lowerCamelCase_      ,					lowerCamelCase_     )
																					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(lowerCamelCase_      ,					lowerCamelCase_     )

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

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