'''simple docstring'''




_lowercase		:				List[Any]	       =		8.3_1_4_4_6_2  # Unit - J mol-1 K-1




def 							lowerCamelCase			(   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float				):
							if moles < 0 or kelvin < 0 or volume < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume




def 							lowerCamelCase			(   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float				):
							if moles < 0 or kelvin < 0 or pressure < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
						from doctest import testmod

						testmod()






'''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


_lowercase		:				Tuple	       =		logging.get_logger(__name__)

# General docstring
_lowercase		:				List[str]	       =		"RegNetConfig"

# Base docstring
_lowercase		:				Dict	       =		"facebook/regnet-y-040"
_lowercase		:				Union[str, Any]	       =		[1, 1088, 7, 7]

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

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





class 					__magic_name__		(					nn.Module):







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Dict       		= self.convolution(lowercase_					)
										lowercase_      :    str       		= self.normalization(lowercase_					)
										lowercase_      :    Optional[Any]       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						List[Any]       ,    lowercase_       :						RegNetConfig					):
										super().__init__()
										lowercase_      :    str       		= RegNetConvLayer(
										    config.num_channels       ,    config.embedding_size       ,    kernel_size=3       ,    stride=2       ,    activation=config.hidden_act					)
										lowercase_      :    Any       		= config.num_channels







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						Optional[Any]					):
										lowercase_      :    List[str]       		= 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."""					)
										lowercase_      :    Any       		= self.embedder(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						Tensor					):
										lowercase_      :    Tuple       		= self.convolution(lowercase_					)
										lowercase_      :    str       		= self.normalization(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						str       ,    lowercase_       :						int       ,    lowercase_       :						int					):
										super().__init__()

										lowercase_      :    int       		= nn.AdaptiveAvgPoolad((1, 1)					)
										lowercase_      :    int       		= 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       :						Optional[int]       ,    lowercase_       :						Any					):
										# b c h w -> b c 1 1
										lowercase_      :    List[str]       		= self.pooler(lowercase_					)
										lowercase_      :    Optional[int]       		= self.attention(lowercase_					)
										lowercase_      :    Any       		= hidden_state * attention
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Optional[int]       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 1					):
										super().__init__()
										lowercase_      :    List[Any]       		= in_channels != out_channels or stride != 1
										lowercase_      :    Optional[int]       		= max(1       ,    out_channels // config.groups_width					)
										lowercase_      :    Dict       		= (
										    RegNetShortCut(lowercase_       ,    lowercase_       ,    stride=lowercase_					) if should_apply_shortcut else nn.Identity()
										)
										lowercase_      :    List[Any]       		= 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_					)       ,    )
										lowercase_      :    int       		= ACTaFN[config.hidden_act]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						Any					):
										lowercase_      :    Any       		= hidden_state
										lowercase_      :    Union[str, Any]       		= self.layer(lowercase_					)
										lowercase_      :    Union[str, Any]       		= self.shortcut(lowercase_					)
										hidden_state += residual
										lowercase_      :    str       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Optional[Any]       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 1					):
										super().__init__()
										lowercase_      :    str       		= in_channels != out_channels or stride != 1
										lowercase_      :    int       		= max(1       ,    out_channels // config.groups_width					)
										lowercase_      :    int       		= (
										    RegNetShortCut(lowercase_       ,    lowercase_       ,    stride=lowercase_					) if should_apply_shortcut else nn.Identity()
										)
										lowercase_      :    Union[str, Any]       		= 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_					)       ,    )
										lowercase_      :    Optional[int]       		= ACTaFN[config.hidden_act]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Union[str, Any]					):
										lowercase_      :    Optional[int]       		= hidden_state
										lowercase_      :    str       		= self.layer(lowercase_					)
										lowercase_      :    int       		= self.shortcut(lowercase_					)
										hidden_state += residual
										lowercase_      :    Optional[Any]       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						str       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 2       ,    lowercase_       :						int = 2       ,    ):
										super().__init__()

										lowercase_      :    str       		= RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

										lowercase_      :    str       		= 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       :						Union[str, Any]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Tuple       		= self.layers(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Dict       ,    lowercase_       :						RegNetConfig					):
										super().__init__()
										lowercase_      :    Optional[Any]       		= 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]       ,    )					)
										lowercase_      :    Optional[Any]       		= 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       :						str       ,    lowercase_       :						Tensor       ,    lowercase_       :						bool = False       ,    lowercase_       :						bool = True					):
										lowercase_      :    Tuple       		= () if output_hidden_states else None

										for stage_module in self.stages:
																	if output_hidden_states:
																								lowercase_      :    Union[str, Any]       		= hidden_states + (hidden_state,)

																	lowercase_      :    Dict       		= stage_module(lowercase_					)

										if output_hidden_states:
																	lowercase_      :    Optional[Any]       		= 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 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = RegNetConfig
			UpperCamelCase__       = '''regnet'''
			UpperCamelCase__       = '''pixel_values'''
			UpperCamelCase__       = True







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						Optional[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       :						List[Any]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Any=False					):
										if isinstance(lowercase_       ,    lowercase_					):
																	lowercase_      :    List[str]       		= value




_lowercase		:				Dict	       =		r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

_lowercase		:				Any	       =		r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n"





@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 					__magic_name__		(					_UpperCAmelCase):







			def __init__(			self       :						Any       ,    lowercase_       :						Any					):
										super().__init__(lowercase_					)
										lowercase_      :    List[str]       		= config
										lowercase_      :    Union[str, Any]       		= RegNetEmbeddings(lowercase_					)
										lowercase_      :    Union[str, Any]       		= RegNetEncoder(lowercase_					)
										lowercase_      :    str       		= 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       :						str       ,    lowercase_       :						Tensor       ,    lowercase_       :						Optional[bool] = None       ,    lowercase_       :						Optional[bool] = None					):
										lowercase_      :    List[Any]       		= (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										lowercase_      :    Tuple       		= return_dict if return_dict is not None else self.config.use_return_dict

										lowercase_      :    str       		= self.embedder(lowercase_					)

										lowercase_      :    Optional[Any]       		= self.encoder(
										    lowercase_       ,    output_hidden_states=lowercase_       ,    return_dict=lowercase_					)

										lowercase_      :    List[Any]       		= encoder_outputs[0]

										lowercase_      :    str       		= 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(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    ''',  _UpperCAmelCase,  )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 					__magic_name__		(					_UpperCAmelCase):







			def __init__(			self       :						Dict       ,    lowercase_       :						str					):
										super().__init__(lowercase_					)
										lowercase_      :    Any       		= config.num_labels
										lowercase_      :    List[str]       		= RegNetModel(lowercase_					)
										# classification head
										lowercase_      :    Any       		= 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       :						List[str]       ,    lowercase_       :						Optional[torch.FloatTensor] = None       ,    lowercase_       :						Optional[torch.LongTensor] = None       ,    lowercase_       :						Optional[bool] = None       ,    lowercase_       :						Optional[bool] = None       ,    ):
										lowercase_      :    Union[str, Any]       		= return_dict if return_dict is not None else self.config.use_return_dict

										lowercase_      :    Optional[int]       		= self.regnet(lowercase_       ,    output_hidden_states=lowercase_       ,    return_dict=lowercase_					)

										lowercase_      :    Optional[int]       		= outputs.pooler_output if return_dict else outputs[1]

										lowercase_      :    List[Any]       		= self.classifier(lowercase_					)

										lowercase_      :    Optional[int]       		= None

										if labels is not None:
																	if self.config.problem_type is None:
																								if self.num_labels == 1:
																															lowercase_      :    Optional[int]       		= """regression"""
																								elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																															lowercase_      :    str       		= """single_label_classification"""
																								else:
																															lowercase_      :    str       		= """multi_label_classification"""
																	if self.config.problem_type == "regression":
																								lowercase_      :    str       		= MSELoss()
																								if self.num_labels == 1:
																															lowercase_      :    List[Any]       		= loss_fct(logits.squeeze()       ,    labels.squeeze()					)
																								else:
																															lowercase_      :    List[str]       		= loss_fct(lowercase_       ,    lowercase_					)
																	elif self.config.problem_type == "single_label_classification":
																								lowercase_      :    Optional[int]       		= CrossEntropyLoss()
																								lowercase_      :    Union[str, Any]       		= loss_fct(logits.view(-1       ,    self.num_labels					)       ,    labels.view(-1					)					)
																	elif self.config.problem_type == "multi_label_classification":
																								lowercase_      :    Dict       		= BCEWithLogitsLoss()
																								lowercase_      :    Tuple       		= loss_fct(lowercase_       ,    lowercase_					)

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

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





