





'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =None
				__a            =None
				__a            =None
				__a            =None


class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					Tuple					,				__a     :					Tuple=1					,				__a     :					List[Any]=0					,				__a     :					Any=2					,				__a     :					Any=5_12					,				__a     :					Optional[Any]="cls"					,				__a     :					List[Any]=False					,				__a     :					Tuple=True					,				**__a     :					Tuple					,				):
								super().__init__(pad_token_id=__a					,				bos_token_id=__a					,				eos_token_id=__a					,				**__a					)
								_a								= project_dim
								_a								= pooler_fn
								_a								= learn_encoder
								_a								= use_attention_mask


class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =[r'pooler', r'logit_scale']
				__a            =[r'position_ids', r'predictions.decoder.bias']
				__a            ='roberta'
				__a            =RobertaSeriesConfig




				def __init__(  self     :					Optional[int]					,				__a     :					List[Any]					):
								super().__init__(__a					)
								_a								= XLMRobertaModel(__a					)
								_a								= nn.Linear(config.hidden_size					,				config.project_dim					)
								_a								= getattr(__a					,				"has_pre_transformation"					,				__a					)
								if self.has_pre_transformation:
												_a								= nn.Linear(config.hidden_size					,				config.project_dim					)
												_a								= nn.LayerNorm(config.hidden_size					,				eps=config.layer_norm_eps					)
								self.post_init()




				def   UpperCamelCase__   (  self     :					Any					,				__a     :					Optional[torch.Tensor] = None					,				__a     :					Optional[torch.Tensor] = None					,				__a     :					Optional[torch.Tensor] = None					,				__a     :					Optional[torch.Tensor] = None					,				__a     :					Optional[torch.Tensor] = None					,				__a     :					Optional[torch.Tensor] = None					,				__a     :					Optional[torch.Tensor] = None					,				__a     :					Optional[torch.Tensor] = None					,				__a     :					Optional[bool] = None					,				__a     :					Optional[bool] = None					,				__a     :					Optional[bool] = None					,				):
								_a								= return_dict if return_dict is not None else self.config.use_return_dict

								_a								= self.base_model(
								    input_ids=__a					,				attention_mask=__a					,				token_type_ids=__a					,				position_ids=__a					,				head_mask=__a					,				inputs_embeds=__a					,				encoder_hidden_states=__a					,				encoder_attention_mask=__a					,				output_attentions=__a					,				output_hidden_states=True if self.has_pre_transformation else output_hidden_states					,				return_dict=__a					,				)

								if self.has_pre_transformation:
												_a								= outputs["hidden_states"][-2]
												_a								= self.pre_LN(__a					)
												_a								= self.transformation_pre(__a					)

												return TransformationModelOutput(
												    projection_state=__a					,				last_hidden_state=outputs.last_hidden_state					,				hidden_states=outputs.hidden_states					,				attentions=outputs.attentions					,				)
								else:
												_a								= self.transformation(outputs.last_hidden_state					)
												return TransformationModelOutput(
												    projection_state=__a					,				last_hidden_state=outputs.last_hidden_state					,				hidden_states=outputs.hidden_states					,				attentions=outputs.attentions					,				)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ResNetForImageClassification",
					    "ResNetModel",
					    "ResNetPreTrainedModel",
					    "ResNetBackbone",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFResNetForImageClassification",
					    "TFResNetModel",
					    "TFResNetPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    [
					    "FlaxResNetForImageClassification",
					    "FlaxResNetModel",
					    "FlaxResNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_resnet import (
										    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ResNetBackbone,
										    ResNetForImageClassification,
										    ResNetModel,
										    ResNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_resnet import (
										    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFResNetForImageClassification,
										    TFResNetModel,
										    TFResNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Any												=    _LazyModule(__name__, globals()["__file__"], _import_structure)



