

from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


a		=[
    """openmmlab/upernet-convnext-tiny""",
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
a		="""UperNetConfig"""






class 	A_		(	nn.Module    ):
							def __init__(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Union[int, Tuple[int, int]]       ,SCREAMING_SNAKE_CASE__		:      Union[int, Tuple[int, int], str] = 0       ,SCREAMING_SNAKE_CASE__		:      bool = False       ,SCREAMING_SNAKE_CASE__		:      Union[int, Tuple[int, int]] = 1       ,):
								super().__init__()
								__lowerCamelCase    :    List[Any] 	=			nn.Convad(
								    in_channels=SCREAMING_SNAKE_CASE__       ,out_channels=SCREAMING_SNAKE_CASE__       ,kernel_size=SCREAMING_SNAKE_CASE__       ,padding=SCREAMING_SNAKE_CASE__       ,bias=SCREAMING_SNAKE_CASE__       ,dilation=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    int 	=			nn.BatchNormad(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			nn.ReLU()


							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								__lowerCamelCase    :    Optional[int] 	=			self.conv(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Any 	=			self.batch_norm(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			self.activation(SCREAMING_SNAKE_CASE__)

								return output






class 	A_		(	nn.Module    ):
							def __init__(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      int):
								super().__init__()
								__lowerCamelCase    :    List[str] 	=			[
								    nn.AdaptiveAvgPoolad(SCREAMING_SNAKE_CASE__),
								    UperNetConvModule(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,kernel_size=1),
								]
								for i, layer in enumerate(self.layers):
									self.add_module(str(SCREAMING_SNAKE_CASE__)       ,SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								__lowerCamelCase    :    int 	=			input
								for layer in self.layers:
									__lowerCamelCase    :    Union[str, Any] 	=			layer(SCREAMING_SNAKE_CASE__)
								return hidden_state






class 	A_		(	nn.Module    ):
							def __init__(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple[int, ...]       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      bool):
								super().__init__()
								__lowerCamelCase    :    Dict 	=			pool_scales
								__lowerCamelCase    :    int 	=			align_corners
								__lowerCamelCase    :    Dict 	=			in_channels
								__lowerCamelCase    :    Any 	=			channels
								__lowerCamelCase    :    Optional[int] 	=			[]
								for i, pool_scale in enumerate(SCREAMING_SNAKE_CASE__):
									__lowerCamelCase    :    List[str] 	=			UperNetPyramidPoolingBlock(pool_scale=SCREAMING_SNAKE_CASE__       ,in_channels=SCREAMING_SNAKE_CASE__       ,channels=SCREAMING_SNAKE_CASE__)
									self.blocks.append(SCREAMING_SNAKE_CASE__)
									self.add_module(str(SCREAMING_SNAKE_CASE__)       ,SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								__lowerCamelCase    :    Optional[int] 	=			[]
								for ppm in self.blocks:
									__lowerCamelCase    :    Dict 	=			ppm(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Union[str, Any] 	=			nn.functional.interpolate(
									    SCREAMING_SNAKE_CASE__       ,size=x.size()[2:]       ,mode='bilinear'       ,align_corners=self.align_corners)
									ppm_outs.append(SCREAMING_SNAKE_CASE__)
								return ppm_outs






class 	A_		(	nn.Module    ):
							def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
								super().__init__()

								__lowerCamelCase    :    Dict 	=			config
								__lowerCamelCase    :    List[str] 	=			config.pool_scales  # e.g. (1, 2, 3, 6)
								__lowerCamelCase    :    str 	=			in_channels
								__lowerCamelCase    :    Dict 	=			config.hidden_size
								__lowerCamelCase    :    List[str] 	=			False
								__lowerCamelCase    :    List[Any] 	=			nn.Convad(self.channels       ,config.num_labels       ,kernel_size=1)

								# PSP Module
								__lowerCamelCase    :    List[str] 	=			UperNetPyramidPoolingModule(
								    self.pool_scales       ,self.in_channels[-1]       ,self.channels       ,align_corners=self.align_corners       ,)
								__lowerCamelCase    :    str 	=			UperNetConvModule(
								    self.in_channels[-1] + len(self.pool_scales) * self.channels       ,self.channels       ,kernel_size=3       ,padding=1       ,)
								# FPN Module
								__lowerCamelCase    :    Dict 	=			nn.ModuleList()
								__lowerCamelCase    :    Any 	=			nn.ModuleList()
								for in_channels in self.in_channels[:-1]:  # skip the top layer
									__lowerCamelCase    :    str 	=			UperNetConvModule(SCREAMING_SNAKE_CASE__       ,self.channels       ,kernel_size=1)
									__lowerCamelCase    :    str 	=			UperNetConvModule(self.channels       ,self.channels       ,kernel_size=3       ,padding=1)
									self.lateral_convs.append(SCREAMING_SNAKE_CASE__)
									self.fpn_convs.append(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			UperNetConvModule(
								    len(self.in_channels) * self.channels       ,self.channels       ,kernel_size=3       ,padding=1       ,)
							def    lowerCAmelCase					(		self		:      List[str]):
								self.apply(self._init_weights)
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Dict):
								if isinstance(SCREAMING_SNAKE_CASE__       ,nn.Convad):
									module.weight.data.normal_(mean=0.0       ,std=self.config.initializer_range)
									if module.bias is not None:
										module.bias.data.zero_()
							def    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
								__lowerCamelCase    :    Tuple 	=			inputs[-1]
								__lowerCamelCase    :    List[Any] 	=			[x]
								psp_outs.extend(self.psp_modules(SCREAMING_SNAKE_CASE__))
								__lowerCamelCase    :    Optional[int] 	=			torch.cat(SCREAMING_SNAKE_CASE__       ,dim=1)
								__lowerCamelCase    :    Tuple 	=			self.bottleneck(SCREAMING_SNAKE_CASE__)

								return output


							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								# build laterals
								__lowerCamelCase    :    Dict 	=			[lateral_conv(encoder_hidden_states[i]) for i, lateral_conv in enumerate(self.lateral_convs)]

								laterals.append(self.psp_forward(SCREAMING_SNAKE_CASE__))

								# build top-down path
								__lowerCamelCase    :    Dict 	=			len(SCREAMING_SNAKE_CASE__)
								for i in range(used_backbone_levels - 1       ,0       ,-1):
									__lowerCamelCase    :    int 	=			laterals[i - 1].shape[2:]
									__lowerCamelCase    :    Optional[Any] 	=			laterals[i - 1] + nn.functional.interpolate(
									    laterals[i]       ,size=SCREAMING_SNAKE_CASE__       ,mode='bilinear'       ,align_corners=self.align_corners)

								# build outputs
								__lowerCamelCase    :    str 	=			[self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels - 1)]
								# append psp feature
								fpn_outs.append(laterals[-1])

								for i in range(used_backbone_levels - 1       ,0       ,-1):
									__lowerCamelCase    :    str 	=			nn.functional.interpolate(
									    fpn_outs[i]       ,size=fpn_outs[0].shape[2:]       ,mode='bilinear'       ,align_corners=self.align_corners)
								__lowerCamelCase    :    Dict 	=			torch.cat(SCREAMING_SNAKE_CASE__       ,dim=1)
								__lowerCamelCase    :    Optional[int] 	=			self.fpn_bottleneck(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			self.classifier(SCREAMING_SNAKE_CASE__)

								return output






class 	A_		(	nn.Module    ):
							def __init__(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      int = 2       ,SCREAMING_SNAKE_CASE__		:      int = 3       ,SCREAMING_SNAKE_CASE__		:      Union[int, Tuple[int, int]] = 1):
								super().__init__()

								__lowerCamelCase    :    Optional[Any] 	=			config
								__lowerCamelCase    :    Optional[Any] 	=			config.auxiliary_in_channels
								__lowerCamelCase    :    Optional[int] 	=			config.auxiliary_channels
								__lowerCamelCase    :    Union[str, Any] 	=			config.auxiliary_num_convs
								__lowerCamelCase    :    Dict 	=			config.auxiliary_concat_input
								__lowerCamelCase    :    Union[str, Any] 	=			in_index

								__lowerCamelCase    :    Any 	=			(kernel_size // 2) * dilation
								__lowerCamelCase    :    Optional[int] 	=			[]
								convs.append(
								    UperNetConvModule(
								        self.in_channels       ,self.channels       ,kernel_size=SCREAMING_SNAKE_CASE__       ,padding=SCREAMING_SNAKE_CASE__       ,dilation=SCREAMING_SNAKE_CASE__))
								for i in range(self.num_convs - 1):
									convs.append(
									    UperNetConvModule(
									        self.channels       ,self.channels       ,kernel_size=SCREAMING_SNAKE_CASE__       ,padding=SCREAMING_SNAKE_CASE__       ,dilation=SCREAMING_SNAKE_CASE__))
								if self.num_convs == 0:
									__lowerCamelCase    :    Any 	=			nn.Identity()
								else:
									__lowerCamelCase    :    int 	=			nn.Sequential(*SCREAMING_SNAKE_CASE__)
								if self.concat_input:
									__lowerCamelCase    :    Optional[int] 	=			UperNetConvModule(
									    self.in_channels + self.channels       ,self.channels       ,kernel_size=SCREAMING_SNAKE_CASE__       ,padding=kernel_size // 2)

								__lowerCamelCase    :    List[str] 	=			nn.Convad(self.channels       ,config.num_labels       ,kernel_size=1)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								self.apply(self._init_weights)
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Dict):
								if isinstance(SCREAMING_SNAKE_CASE__       ,nn.Convad):
									module.weight.data.normal_(mean=0.0       ,std=self.config.initializer_range)
									if module.bias is not None:
										module.bias.data.zero_()


							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								# just take the relevant feature maps
								__lowerCamelCase    :    List[str] 	=			encoder_hidden_states[self.in_index]
								__lowerCamelCase    :    Optional[Any] 	=			self.convs(SCREAMING_SNAKE_CASE__)
								if self.concat_input:
									__lowerCamelCase    :    List[str] 	=			self.conv_cat(torch.cat([hidden_states, output]       ,dim=1))
								__lowerCamelCase    :    str 	=			self.classifier(SCREAMING_SNAKE_CASE__)
								return output






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  List[str]										=				UperNetConfig
							_UpperCAmelCase   :  Optional[Any]										=				'''pixel_values'''
							_UpperCAmelCase   :  List[Any]										=				True
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Dict):
								if isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__):
									module.backbone.init_weights()
									module.decode_head.init_weights()
									module.auxiliary_head.init_weights()
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								self.backbone.init_weights()
								self.decode_head.init_weights()
								self.auxiliary_head.init_weights()


							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Any=False):
								if isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__):
									__lowerCamelCase    :    Dict 	=			value
a		=r"""
    Parameters:
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
        config ([`UperNetConfig`]): 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.
"""

a		=r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
            `attentions` under returned tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
            returned tensors for more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""






@add_start_docstrings(
    '''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.'''      ,							SCREAMING_SNAKE_CASE      ,							)
class 	A_		(	SCREAMING_SNAKE_CASE    ):
							def __init__(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      int):
								super().__init__(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			AutoBackbone.from_config(config.backbone_config)

								# Semantic segmentation head(s)
								__lowerCamelCase    :    List[str] 	=			UperNetHead(SCREAMING_SNAKE_CASE__       ,in_channels=self.backbone.channels)
								__lowerCamelCase    :    int 	=			UperNetFCNHead(SCREAMING_SNAKE_CASE__) if config.use_auxiliary_head else None

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


							@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('batch_size, sequence_length'))
							@replace_return_docstrings(output_type=SCREAMING_SNAKE_CASE__       ,config_class=_CONFIG_FOR_DOC)
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[torch.Tensor] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[bool] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[bool] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[torch.Tensor] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[bool] = None       ,):
								__lowerCamelCase    :    Dict 	=			return_dict if return_dict is not None else self.config.use_return_dict
								__lowerCamelCase    :    int 	=			(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								__lowerCamelCase    :    Tuple 	=			output_attentions if output_attentions is not None else self.config.output_attentions

								__lowerCamelCase    :    Optional[int] 	=			self.backbone.forward_with_filtered_kwargs(
								    SCREAMING_SNAKE_CASE__       ,output_hidden_states=SCREAMING_SNAKE_CASE__       ,output_attentions=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[Any] 	=			outputs.feature_maps

								__lowerCamelCase    :    str 	=			self.decode_head(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Dict 	=			nn.functional.interpolate(SCREAMING_SNAKE_CASE__       ,size=pixel_values.shape[2:]       ,mode='bilinear'       ,align_corners=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[Any] 	=			None
								if self.auxiliary_head is not None:
									__lowerCamelCase    :    Tuple 	=			self.auxiliary_head(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    List[Any] 	=			nn.functional.interpolate(
									    SCREAMING_SNAKE_CASE__       ,size=pixel_values.shape[2:]       ,mode='bilinear'       ,align_corners=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			None
								if labels is not None:
									if self.config.num_labels == 1:
										raise ValueError('The number of labels should be greater than one')
									else:
										# compute weighted loss
										__lowerCamelCase    :    List[str] 	=			CrossEntropyLoss(ignore_index=self.config.loss_ignore_index)
										__lowerCamelCase    :    Dict 	=			loss_fct(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Optional[Any] 	=			loss_fct(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    str 	=			main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

								if not return_dict:
									if output_hidden_states:
										__lowerCamelCase    :    int 	=			(logits,) + outputs[1:]
									else:
										__lowerCamelCase    :    Any 	=			(logits,) + outputs[2:]
									return ((loss,) + output) if loss is not None else output

								return SemanticSegmenterOutput(
								    loss=SCREAMING_SNAKE_CASE__       ,logits=SCREAMING_SNAKE_CASE__       ,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_tokenizers_available,
    is_torch_available,
)


a		={
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""RoFormerTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RoFormerForCausalLM""",
					    """RoFormerForMaskedLM""",
					    """RoFormerForMultipleChoice""",
					    """RoFormerForQuestionAnswering""",
					    """RoFormerForSequenceClassification""",
					    """RoFormerForTokenClassification""",
					    """RoFormerLayer""",
					    """RoFormerModel""",
					    """RoFormerPreTrainedModel""",
					    """load_tf_weights_in_roformer""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRoFormerForCausalLM""",
					    """TFRoFormerForMaskedLM""",
					    """TFRoFormerForMultipleChoice""",
					    """TFRoFormerForQuestionAnswering""",
					    """TFRoFormerForSequenceClassification""",
					    """TFRoFormerForTokenClassification""",
					    """TFRoFormerLayer""",
					    """TFRoFormerModel""",
					    """TFRoFormerPreTrainedModel""",
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaxRoFormerForMaskedLM""",
					    """FlaxRoFormerForMultipleChoice""",
					    """FlaxRoFormerForQuestionAnswering""",
					    """FlaxRoFormerForSequenceClassification""",
					    """FlaxRoFormerForTokenClassification""",
					    """FlaxRoFormerModel""",
					    """FlaxRoFormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
					from .tokenization_roformer import RoFormerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roformer_fast import RoFormerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roformer import (
										    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RoFormerForCausalLM,
										    RoFormerForMaskedLM,
										    RoFormerForMultipleChoice,
										    RoFormerForQuestionAnswering,
										    RoFormerForSequenceClassification,
										    RoFormerForTokenClassification,
										    RoFormerLayer,
										    RoFormerModel,
										    RoFormerPreTrainedModel,
										    load_tf_weights_in_roformer,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roformer import (
										    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRoFormerForCausalLM,
										    TFRoFormerForMaskedLM,
										    TFRoFormerForMultipleChoice,
										    TFRoFormerForQuestionAnswering,
										    TFRoFormerForSequenceClassification,
										    TFRoFormerForTokenClassification,
										    TFRoFormerLayer,
										    TFRoFormerModel,
										    TFRoFormerPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roformer import (
										    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxRoFormerForMaskedLM,
										    FlaxRoFormerForMultipleChoice,
										    FlaxRoFormerForQuestionAnswering,
										    FlaxRoFormerForSequenceClassification,
										    FlaxRoFormerForTokenClassification,
										    FlaxRoFormerModel,
										    FlaxRoFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




