




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


__lowerCamelCase   :						int				=			logging.get_logger(__name__)

# General docstring
__lowerCamelCase   :						Optional[int]				=			'''RegNetConfig'''

# Base docstring
__lowerCamelCase   :						List[Any]				=			'''facebook/regnet-y-040'''
__lowerCamelCase   :						Dict				=			[1, 10_88, 7, 7]

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

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


class    lowerCamelCase    (		nn.Module						):






			'''simple docstring'''





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


			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[Any]  )						->   int:
								__magic_name__							:	Union[str, Any]    							=							self.convolution(lowerCamelCase_  )
								__magic_name__							:	Optional[int]    							=							self.normalization(lowerCamelCase_  )
								__magic_name__							:	Union[str, Any]    							=							self.activation(lowerCamelCase_  )
								return hidden_state






class    lowerCamelCase    (		nn.Module						):






			'''simple docstring'''





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


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								__magic_name__							:	Optional[Any]    							=							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.'''  )
								__magic_name__							:	Tuple    							=							self.embedder(lowerCamelCase_  )
								return hidden_state






class    lowerCamelCase    (		nn.Module						):






			'''simple docstring'''





			def __init__( self			:   Optional[int]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int = 2  )						->   Dict:
								super().__init__()
								__magic_name__							:	Dict    							=							nn.Convad(lowerCamelCase_       ,						lowerCamelCase_       ,						kernel_size=1       ,						stride=lowerCamelCase_       ,						bias=lowerCamelCase_  )
								__magic_name__							:	Dict    							=							nn.BatchNormad(lowerCamelCase_  )


			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Tensor  )						->   Tensor:
								__magic_name__							:	Union[str, Any]    							=							self.convolution(lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							self.normalization(lowerCamelCase_  )
								return hidden_state






class    lowerCamelCase    (		nn.Module						):






			'''simple docstring'''





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

								__magic_name__							:	Tuple    							=							nn.AdaptiveAvgPoolad((1, 1)  )
								__magic_name__							:	List[Any]    							=							nn.Sequential(
								    nn.Convad(lowerCamelCase_       ,						lowerCamelCase_       ,						kernel_size=1  )       ,						nn.ReLU()       ,						nn.Convad(lowerCamelCase_       ,						lowerCamelCase_       ,						kernel_size=1  )       ,						nn.Sigmoid()       ,						)


			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   Dict  )						->   Tuple:
								# b c h w -> b c 1 1
								__magic_name__							:	List[str]    							=							self.pooler(lowerCamelCase_  )
								__magic_name__							:	List[Any]    							=							self.attention(lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							hidden_state * attention
								return hidden_state






class    lowerCamelCase    (		nn.Module						):






			'''simple docstring'''





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


			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   int  )						->   Optional[int]:
								__magic_name__							:	List[str]    							=							hidden_state
								__magic_name__							:	str    							=							self.layer(lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							self.shortcut(lowerCamelCase_  )
								hidden_state += residual
								__magic_name__							:	List[str]    							=							self.activation(lowerCamelCase_  )
								return hidden_state






class    lowerCamelCase    (		nn.Module						):






			'''simple docstring'''





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


			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   List[Any]  )						->   Dict:
								__magic_name__							:	List[str]    							=							hidden_state
								__magic_name__							:	str    							=							self.layer(lowerCamelCase_  )
								__magic_name__							:	List[Any]    							=							self.shortcut(lowerCamelCase_  )
								hidden_state += residual
								__magic_name__							:	List[str]    							=							self.activation(lowerCamelCase_  )
								return hidden_state






class    lowerCamelCase    (		nn.Module						):






			'''simple docstring'''





			def __init__( self			:   Optional[Any]       ,						lowerCamelCase_			:   RegNetConfig       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int = 2       ,						lowerCamelCase_			:   int = 2       ,						)						->   int:
								super().__init__()

								__magic_name__							:	Optional[int]    							=							RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer

								__magic_name__							:	Any    							=							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 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Any  )						->   List[Any]:
								__magic_name__							:	Optional[int]    							=							self.layers(lowerCamelCase_  )
								return hidden_state






class    lowerCamelCase    (		nn.Module						):






			'''simple docstring'''





			def __init__( self			:   int       ,						lowerCamelCase_			:   RegNetConfig  )						->   List[Any]:
								super().__init__()
								__magic_name__							:	Optional[int]    							=							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]       ,						)  )
								__magic_name__							:	Union[str, Any]    							=							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 							UpperCAmelCase__       ( self			:   Any       ,						lowerCamelCase_			:   Tensor       ,						lowerCamelCase_			:   bool = False       ,						lowerCamelCase_			:   bool = True  )						->   BaseModelOutputWithNoAttention:
								__magic_name__							:	str    							=							() if output_hidden_states else None

								for stage_module in self.stages:
													if output_hidden_states:
																		__magic_name__							:	Optional[int]    							=							hidden_states + (hidden_state,)

													__magic_name__							:	Union[str, Any]    							=							stage_module(lowerCamelCase_  )

								if output_hidden_states:
													__magic_name__							:	Union[str, 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=lowerCamelCase_       ,						hidden_states=lowerCamelCase_  )






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=RegNetConfig
			UpperCamelCase__								='''regnet'''
			UpperCamelCase__								='''pixel_values'''
			UpperCamelCase__								=True

			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   List[str]  )						->   Optional[Any]:
								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 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   int=False  )						->   Tuple:
								if isinstance(lowerCamelCase_       ,						lowerCamelCase_  ):
													__magic_name__							:	List[Any]    							=							value






__lowerCamelCase   :						Union[str, 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.
'''

__lowerCamelCase   :						Tuple				=			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.'''				,_lowerCamelCase				,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Any       ,						lowerCamelCase_			:   Union[str, Any]  )						->   str:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							config
								__magic_name__							:	Optional[Any]    							=							RegNetEmbeddings(lowerCamelCase_  )
								__magic_name__							:	Dict    							=							RegNetEncoder(lowerCamelCase_  )
								__magic_name__							:	List[Any]    							=							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 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   Tensor       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None  )						->   BaseModelOutputWithPoolingAndNoAttention:
								__magic_name__							:	Dict    							=							(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								__magic_name__							:	Tuple    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	List[Any]    							=							self.embedder(lowerCamelCase_  )

								__magic_name__							:	Optional[int]    							=							self.encoder(
								    lowerCamelCase_       ,						output_hidden_states=lowerCamelCase_       ,						return_dict=lowerCamelCase_  )

								__magic_name__							:	Optional[int]    							=							encoder_outputs[0]

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






			'''simple docstring'''





			def __init__( self			:   List[str]       ,						lowerCamelCase_			:   Dict  )						->   int:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							config.num_labels
								__magic_name__							:	Optional[Any]    							=							RegNetModel(lowerCamelCase_  )
								# classification head
								__magic_name__							:	Union[str, 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(lowerCamelCase_  )
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,						output_type=lowerCamelCase_       ,						config_class=_CONFIG_FOR_DOC       ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,						)
			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Optional[torch.FloatTensor] = None       ,						lowerCamelCase_			:   Optional[torch.LongTensor] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						)						->   ImageClassifierOutputWithNoAttention:
								__magic_name__							:	List[Any]    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	Optional[Any]    							=							self.regnet(lowerCamelCase_       ,						output_hidden_states=lowerCamelCase_       ,						return_dict=lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							outputs.pooler_output if return_dict else outputs[1]

								__magic_name__							:	Dict    							=							self.classifier(lowerCamelCase_  )

								__magic_name__							:	Tuple    							=							None

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

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

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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)

__lowerCamelCase   :						Dict				=			{}


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								='''llama'''
			UpperCamelCase__								=['''past_key_values''']

			def __init__( self			:   Dict       ,						lowerCamelCase_			:   List[str]=32000       ,						lowerCamelCase_			:   List[Any]=4096       ,						lowerCamelCase_			:   Any=11008       ,						lowerCamelCase_			:   Optional[Any]=32       ,						lowerCamelCase_			:   Dict=32       ,						lowerCamelCase_			:   Optional[Any]=None       ,						lowerCamelCase_			:   Dict="silu"       ,						lowerCamelCase_			:   Optional[int]=2048       ,						lowerCamelCase_			:   int=0.0_2       ,						lowerCamelCase_			:   Tuple=1E-6       ,						lowerCamelCase_			:   Tuple=True       ,						lowerCamelCase_			:   List[str]=0       ,						lowerCamelCase_			:   List[str]=1       ,						lowerCamelCase_			:   Optional[int]=2       ,						lowerCamelCase_			:   List[str]=1       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   int=None       ,						**lowerCamelCase_			:   Tuple       ,						)						->   Tuple:
								__magic_name__							:	Any    							=							vocab_size
								__magic_name__							:	Optional[int]    							=							max_position_embeddings
								__magic_name__							:	Union[str, Any]    							=							hidden_size
								__magic_name__							:	Dict    							=							intermediate_size
								__magic_name__							:	Union[str, Any]    							=							num_hidden_layers
								__magic_name__							:	Tuple    							=							num_attention_heads

								# for backward compatibility
								if num_key_value_heads is None:
													__magic_name__							:	Optional[int]    							=							num_attention_heads

								__magic_name__							:	Optional[int]    							=							num_key_value_heads
								__magic_name__							:	List[str]    							=							hidden_act
								__magic_name__							:	List[str]    							=							initializer_range
								__magic_name__							:	Tuple    							=							rms_norm_eps
								__magic_name__							:	Optional[int]    							=							pretraining_tp
								__magic_name__							:	Any    							=							use_cache
								__magic_name__							:	int    							=							rope_scaling
								self._rope_scaling_validation()

								super().__init__(
								    pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						tie_word_embeddings=lowerCamelCase_       ,						**lowerCamelCase_       ,						)


			def 							UpperCAmelCase__       ( self			:   str  )						->   Tuple:
								if self.rope_scaling is None:
													return

								if not isinstance(self.rope_scaling       ,						lowerCamelCase_  ) or len(self.rope_scaling  ) != 2:
													raise ValueError(
													    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
													    F'''got {self.rope_scaling}'''  )
								__magic_name__							:	List[str]    							=							self.rope_scaling.get('''type'''       ,						lowerCamelCase_  )
								__magic_name__							:	str    							=							self.rope_scaling.get('''factor'''       ,						lowerCamelCase_  )
								if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
													raise ValueError(
													    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''  )
								if rope_scaling_factor is None or not isinstance(lowerCamelCase_       ,						lowerCamelCase_  ) or rope_scaling_factor <= 1.0:
													raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''  )



