


"""simple docstring"""
from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


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

# General docstring
__SCREAMING_SNAKE_CASE							:    str		=    'ResNetConfig'

# Base docstring
__SCREAMING_SNAKE_CASE							:    Any		=    'microsoft/resnet-50'
__SCREAMING_SNAKE_CASE							:    Tuple		=    [1, 2_048, 7, 7]

# Image classification docstring
__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    'microsoft/resnet-50'
__SCREAMING_SNAKE_CASE							:    Optional[int]		=    'tiger cat'

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





class      __A     (nn.Module):





							'''simple docstring'''

							def __init__(				self							:      Union[str, Any]		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int = 3		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      str = "relu"       )					->Tuple:



														"""simple docstring"""



														super().__init__()
														snake_case_           =		nn.Convad(
														    UpperCAmelCase_		,       UpperCAmelCase_		,       kernel_size=UpperCAmelCase_		,       stride=UpperCAmelCase_		,       padding=kernel_size // 2		,       bias=UpperCAmelCase_       )
														snake_case_           =		nn.BatchNormad(UpperCAmelCase_       )
														snake_case_           =		ACTaFN[activation] if activation is not None else nn.Identity()



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      Tensor       )					->Tensor:



														"""simple docstring"""



														snake_case_           =		self.convolution(UpperCAmelCase_       )
														snake_case_           =		self.normalization(UpperCAmelCase_       )
														snake_case_           =		self.activation(UpperCAmelCase_       )
														return hidden_state





class      __A     (nn.Module):





							'''simple docstring'''

							def __init__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      ResNetConfig       )					->Tuple:



														"""simple docstring"""



														super().__init__()
														snake_case_           =		ResNetConvLayer(
														    config.num_channels		,       config.embedding_size		,       kernel_size=7		,       stride=2		,       activation=config.hidden_act       )
														snake_case_           =		nn.MaxPoolad(kernel_size=3		,       stride=2		,       padding=1       )
														snake_case_           =		config.num_channels



							def 		lowerCAmelCase       (				self							:      List[Any]		,       UpperCAmelCase_							:      Tensor       )					->Tensor:



														"""simple docstring"""



														snake_case_           =		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."""       )
														snake_case_           =		self.embedder(UpperCAmelCase_       )
														snake_case_           =		self.pooler(UpperCAmelCase_       )
														return embedding





class      __A     (nn.Module):





							'''simple docstring'''

							def __init__(				self							:      Union[str, Any]		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int = 2       )					->List[Any]:



														"""simple docstring"""



														super().__init__()
														snake_case_           =		nn.Convad(UpperCAmelCase_		,       UpperCAmelCase_		,       kernel_size=1		,       stride=UpperCAmelCase_		,       bias=UpperCAmelCase_       )
														snake_case_           =		nn.BatchNormad(UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      Tensor       )					->Tensor:



														"""simple docstring"""



														snake_case_           =		self.convolution(UpperCAmelCase_       )
														snake_case_           =		self.normalization(UpperCAmelCase_       )
														return hidden_state





class      __A     (nn.Module):





							'''simple docstring'''

							def __init__(				self							:      Tuple		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      str = "relu"       )					->Tuple:



														"""simple docstring"""



														super().__init__()
														snake_case_           =		in_channels != out_channels or stride != 1
														snake_case_           =		(
														    ResNetShortCut(UpperCAmelCase_		,       UpperCAmelCase_		,       stride=UpperCAmelCase_       ) if should_apply_shortcut else nn.Identity()
														)
														snake_case_           =		nn.Sequential(
														    ResNetConvLayer(UpperCAmelCase_		,       UpperCAmelCase_		,       stride=UpperCAmelCase_       )		,       ResNetConvLayer(UpperCAmelCase_		,       UpperCAmelCase_		,       activation=UpperCAmelCase_       )		,       )
														snake_case_           =		ACTaFN[activation]



							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[Any]       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		hidden_state
														snake_case_           =		self.layer(UpperCAmelCase_       )
														snake_case_           =		self.shortcut(UpperCAmelCase_       )
														hidden_state += residual
														snake_case_           =		self.activation(UpperCAmelCase_       )
														return hidden_state





class      __A     (nn.Module):





							'''simple docstring'''

							def __init__(				self							:      Tuple		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      str = "relu"		,       UpperCAmelCase_							:      int = 4       )					->Tuple:



														"""simple docstring"""



														super().__init__()
														snake_case_           =		in_channels != out_channels or stride != 1
														snake_case_           =		out_channels // reduction
														snake_case_           =		(
														    ResNetShortCut(UpperCAmelCase_		,       UpperCAmelCase_		,       stride=UpperCAmelCase_       ) if should_apply_shortcut else nn.Identity()
														)
														snake_case_           =		nn.Sequential(
														    ResNetConvLayer(UpperCAmelCase_		,       UpperCAmelCase_		,       kernel_size=1       )		,       ResNetConvLayer(UpperCAmelCase_		,       UpperCAmelCase_		,       stride=UpperCAmelCase_       )		,       ResNetConvLayer(UpperCAmelCase_		,       UpperCAmelCase_		,       kernel_size=1		,       activation=UpperCAmelCase_       )		,       )
														snake_case_           =		ACTaFN[activation]



							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      List[Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		hidden_state
														snake_case_           =		self.layer(UpperCAmelCase_       )
														snake_case_           =		self.shortcut(UpperCAmelCase_       )
														hidden_state += residual
														snake_case_           =		self.activation(UpperCAmelCase_       )
														return hidden_state





class      __A     (nn.Module):





							'''simple docstring'''

							def __init__(				self							:      str		,       UpperCAmelCase_							:      ResNetConfig		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      int = 2		,       UpperCAmelCase_							:      int = 2		,       )					->List[Any]:



														"""simple docstring"""



														super().__init__()

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

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



							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      Tensor       )					->Tensor:



														"""simple docstring"""



														snake_case_           =		input
														for layer in self.layers:
																					snake_case_           =		layer(UpperCAmelCase_       )
														return hidden_state





class      __A     (nn.Module):





							'''simple docstring'''

							def __init__(				self							:      Tuple		,       UpperCAmelCase_							:      ResNetConfig       )					->List[Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      Tensor		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      bool = True       )					->BaseModelOutputWithNoAttention:



														"""simple docstring"""



														snake_case_           =		() if output_hidden_states else None

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

																					snake_case_           =		stage_module(UpperCAmelCase_       )

														if output_hidden_states:
																					snake_case_           =		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=UpperCAmelCase_		,       hidden_states=UpperCAmelCase_		,       )





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		List[Any]							      =    ResNetConfig
							__lowercase:		List[Any]							      =    """resnet"""
							__lowercase:		Optional[Any]							      =    """pixel_values"""
							__lowercase:		List[Any]							      =    True



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      str       )					->Tuple:



														"""simple docstring"""



														if isinstance(UpperCAmelCase_		,       nn.Convad       ):
																					nn.init.kaiming_normal_(module.weight		,       mode="""fan_out"""		,       nonlinearity="""relu"""       )
														elif isinstance(UpperCAmelCase_		,       (nn.BatchNormad, nn.GroupNorm)       ):
																					nn.init.constant_(module.weight		,       1       )
																					nn.init.constant_(module.bias		,       0       )



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      Optional[int]=False       )					->Tuple:



														"""simple docstring"""



														if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																					snake_case_           =		value


__SCREAMING_SNAKE_CASE							:    int		=    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 ([`ResNetConfig`]): 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'

__SCREAMING_SNAKE_CASE							:    List[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 [`~utils.ModelOutput`] instead of a plain tuple.\n'





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





							'''simple docstring'''

							def __init__(				self							:      str		,       UpperCAmelCase_							:      Tuple       )					->Union[str, Any]:



														"""simple docstring"""



														super().__init__(UpperCAmelCase_       )
														snake_case_           =		config
														snake_case_           =		ResNetEmbeddings(UpperCAmelCase_       )
														snake_case_           =		ResNetEncoder(UpperCAmelCase_       )
														snake_case_           =		nn.AdaptiveAvgPoolad((1, 1)       )
														# Initialize weights and apply final processing
														self.post_init()



							@add_start_docstrings_to_model_forward(UpperCAmelCase_       )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC		,       output_type=UpperCAmelCase_		,       config_class=_CONFIG_FOR_DOC		,       modality="""vision"""		,       expected_output=_EXPECTED_OUTPUT_SHAPE		,       )
							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Tensor		,       UpperCAmelCase_							:      Optional[bool] = None		,       UpperCAmelCase_							:      Optional[bool] = None       )					->BaseModelOutputWithPoolingAndNoAttention:



														"""simple docstring"""



														snake_case_           =		(
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														snake_case_           =		return_dict if return_dict is not None else self.config.use_return_dict

														snake_case_           =		self.embedder(UpperCAmelCase_       )

														snake_case_           =		self.encoder(
														    UpperCAmelCase_		,       output_hidden_states=UpperCAmelCase_		,       return_dict=UpperCAmelCase_       )

														snake_case_           =		encoder_outputs[0]

														snake_case_           =		self.pooler(UpperCAmelCase_       )

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

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





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





							'''simple docstring'''

							def __init__(				self							:      int		,       UpperCAmelCase_							:      int       )					->Any:



														"""simple docstring"""



														super().__init__(UpperCAmelCase_       )
														snake_case_           =		config.num_labels
														snake_case_           =		ResNetModel(UpperCAmelCase_       )
														# classification head
														snake_case_           =		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(UpperCAmelCase_       )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT		,       output_type=UpperCAmelCase_		,       config_class=_CONFIG_FOR_DOC		,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,       )
							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      Optional[torch.FloatTensor] = None		,       UpperCAmelCase_							:      Optional[torch.LongTensor] = None		,       UpperCAmelCase_							:      Optional[bool] = None		,       UpperCAmelCase_							:      Optional[bool] = None		,       )					->ImageClassifierOutputWithNoAttention:



														"""simple docstring"""



														snake_case_           =		return_dict if return_dict is not None else self.config.use_return_dict

														snake_case_           =		self.resnet(UpperCAmelCase_		,       output_hidden_states=UpperCAmelCase_		,       return_dict=UpperCAmelCase_       )

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

														snake_case_           =		self.classifier(UpperCAmelCase_       )

														snake_case_           =		None

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

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

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





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





							'''simple docstring'''

							def __init__(				self							:      List[Any]		,       UpperCAmelCase_							:      Any       )					->Union[str, Any]:



														"""simple docstring"""



														super().__init__(UpperCAmelCase_       )
														super()._init_backbone(UpperCAmelCase_       )

														snake_case_           =		[config.embedding_size] + config.hidden_sizes
														snake_case_           =		ResNetEmbeddings(UpperCAmelCase_       )
														snake_case_           =		ResNetEncoder(UpperCAmelCase_       )

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



							@add_start_docstrings_to_model_forward(UpperCAmelCase_       )
							@replace_return_docstrings(output_type=UpperCAmelCase_		,       config_class=_CONFIG_FOR_DOC       )
							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      Tensor		,       UpperCAmelCase_							:      Optional[bool] = None		,       UpperCAmelCase_							:      Optional[bool] = None       )					->BackboneOutput:



														"""simple docstring"""



														snake_case_           =		return_dict if return_dict is not None else self.config.use_return_dict
														snake_case_           =		(
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)

														snake_case_           =		self.embedder(UpperCAmelCase_       )

														snake_case_           =		self.encoder(UpperCAmelCase_		,       output_hidden_states=UpperCAmelCase_		,       return_dict=UpperCAmelCase_       )

														snake_case_           =		outputs.hidden_states

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

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

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







"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging


__SCREAMING_SNAKE_CASE							:    str		=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:    str		=    {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Any							      =    """mctct"""



							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      List[Any]=8_065		,       UpperCAmelCase_							:      Tuple=1_536		,       UpperCAmelCase_							:      Optional[Any]=36		,       UpperCAmelCase_							:      int=6_144		,       UpperCAmelCase_							:      Dict=4		,       UpperCAmelCase_							:      Any=384		,       UpperCAmelCase_							:      List[str]=920		,       UpperCAmelCase_							:      Any=1E-5		,       UpperCAmelCase_							:      Any=0.3		,       UpperCAmelCase_							:      Tuple="relu"		,       UpperCAmelCase_							:      Union[str, Any]=0.02		,       UpperCAmelCase_							:      Dict=0.3		,       UpperCAmelCase_							:      str=0.3		,       UpperCAmelCase_							:      Any=1		,       UpperCAmelCase_							:      Any=0		,       UpperCAmelCase_							:      int=2		,       UpperCAmelCase_							:      str=1		,       UpperCAmelCase_							:      Tuple=0.3		,       UpperCAmelCase_							:      Optional[Any]=1		,       UpperCAmelCase_							:      Optional[Any]=(7,)		,       UpperCAmelCase_							:      Optional[Any]=(3,)		,       UpperCAmelCase_							:      List[str]=80		,       UpperCAmelCase_							:      Tuple=1		,       UpperCAmelCase_							:      List[Any]=None		,       UpperCAmelCase_							:      List[str]="sum"		,       UpperCAmelCase_							:      Union[str, Any]=False		,       **UpperCAmelCase_							:      Any		,       )					->Dict:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_		,       pad_token_id=UpperCAmelCase_		,       bos_token_id=UpperCAmelCase_		,       eos_token_id=UpperCAmelCase_       )
														snake_case_           =		vocab_size
														snake_case_           =		hidden_size
														snake_case_           =		num_hidden_layers
														snake_case_           =		intermediate_size
														snake_case_           =		num_attention_heads
														snake_case_           =		attention_head_dim
														snake_case_           =		max_position_embeddings
														snake_case_           =		layer_norm_eps
														snake_case_           =		layerdrop
														snake_case_           =		hidden_act
														snake_case_           =		initializer_range
														snake_case_           =		hidden_dropout_prob
														snake_case_           =		attention_probs_dropout_prob
														snake_case_           =		pad_token_id
														snake_case_           =		bos_token_id
														snake_case_           =		eos_token_id
														snake_case_           =		conv_glu_dim
														snake_case_           =		conv_dropout
														snake_case_           =		num_conv_layers
														snake_case_           =		input_feat_per_channel
														snake_case_           =		input_channels
														snake_case_           =		conv_channels
														snake_case_           =		ctc_loss_reduction
														snake_case_           =		ctc_zero_infinity

														# prevents config testing fail with exporting to json
														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		list(UpperCAmelCase_       )

														if len(self.conv_kernel       ) != self.num_conv_layers:
																					raise ValueError(
																					    """Configuration for convolutional module is incorrect. """
																					    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
																					    F"""but is `len(config.conv_kernel) = {len(self.conv_kernel       )}`, """
																					    F"""`config.num_conv_layers = {self.num_conv_layers}`."""       )



