



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


__snake_case						:Optional[int]      		=     [
    '''openmmlab/upernet-convnext-tiny''',
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
__snake_case						:List[str]      		=     '''UperNetConfig'''

class     _A       (	nn.Module    ):



       def __init__(		self							:     Optional[int]     ,		__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     ,		):


           '''simple docstring'''

           super().__init__()
           __a						       =				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     ,		)
           __a						       =				nn.BatchNormad(__SCREAMING_SNAKE_CASE)
           __a						       =				nn.ReLU()





       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     torch.Tensor):


           '''simple docstring'''

           __a						       =				self.conv(__SCREAMING_SNAKE_CASE)
           __a						       =				self.batch_norm(__SCREAMING_SNAKE_CASE)
           __a						       =				self.activation(__SCREAMING_SNAKE_CASE)

           return output


class     _A       (	nn.Module    ):



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           super().__init__()
           __a						       =				[
               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							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     torch.Tensor):


           '''simple docstring'''

           __a						       =				input
           for layer in self.layers:
               __a						       =				layer(__SCREAMING_SNAKE_CASE)
           return hidden_state


class     _A       (	nn.Module    ):



       def __init__(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     Tuple[int, ...]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     bool):


           '''simple docstring'''

           super().__init__()
           __a						       =				pool_scales
           __a						       =				align_corners
           __a						       =				in_channels
           __a						       =				channels
           __a						       =				[]
           for i, pool_scale in enumerate(__SCREAMING_SNAKE_CASE):
               __a						       =				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):


           '''simple docstring'''

           __a						       =				[]
           for ppm in self.blocks:
               __a						       =				ppm(__SCREAMING_SNAKE_CASE)
               __a						       =				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							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

           super().__init__()

           __a						       =				config
           __a						       =				config.pool_scales  # e.g. (1, 2, 3, 6)
           __a						       =				in_channels
           __a						       =				config.hidden_size
           __a						       =				False
           __a						       =				nn.Convad(self.channels     ,		config.num_labels     ,		kernel_size=1)

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

           __a						       =				UperNetConvModule(
               len(self.in_channels) * self.channels     ,		self.channels     ,		kernel_size=3     ,		padding=1     ,		)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           self.apply(self._init_weights)



       def 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]):


           '''simple docstring'''

           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							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				inputs[-1]
           __a						       =				[x]
           psp_outs.extend(self.psp_modules(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.cat(__SCREAMING_SNAKE_CASE     ,		dim=1)
           __a						       =				self.bottleneck(__SCREAMING_SNAKE_CASE)

           return output





       def 		_lowerCamelCase			(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     torch.Tensor):


           '''simple docstring'''

           __a						       =				[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
           __a						       =				len(__SCREAMING_SNAKE_CASE)
           for i in range(used_backbone_levels - 1     ,		0     ,		-1):
               __a						       =				laterals[i - 1].shape[2:]
               __a						       =				laterals[i - 1] + nn.functional.interpolate(
                   laterals[i]     ,		size=__SCREAMING_SNAKE_CASE     ,		mode='''bilinear'''     ,		align_corners=self.align_corners)

           # build outputs
           __a						       =				[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):
               __a						       =				nn.functional.interpolate(
                   fpn_outs[i]     ,		size=fpn_outs[0].shape[2:]     ,		mode='''bilinear'''     ,		align_corners=self.align_corners)
           __a						       =				torch.cat(__SCREAMING_SNAKE_CASE     ,		dim=1)
           __a						       =				self.fpn_bottleneck(__SCREAMING_SNAKE_CASE)
           __a						       =				self.classifier(__SCREAMING_SNAKE_CASE)

           return output


class     _A       (	nn.Module    ):



       def __init__(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     int = 2     ,		__SCREAMING_SNAKE_CASE							:     int = 3     ,		__SCREAMING_SNAKE_CASE							:     Union[int, Tuple[int, int]] = 1):


           '''simple docstring'''

           super().__init__()

           __a						       =				config
           __a						       =				config.auxiliary_in_channels
           __a						       =				config.auxiliary_channels
           __a						       =				config.auxiliary_num_convs
           __a						       =				config.auxiliary_concat_input
           __a						       =				in_index

           __a						       =				(kernel_size // 2) * dilation
           __a						       =				[]
           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:
               __a						       =				nn.Identity()
           else:
               __a						       =				nn.Sequential(*__SCREAMING_SNAKE_CASE)
           if self.concat_input:
               __a						       =				UperNetConvModule(
                   self.in_channels + self.channels     ,		self.channels     ,		kernel_size=__SCREAMING_SNAKE_CASE     ,		padding=kernel_size // 2)

           __a						       =				nn.Convad(self.channels     ,		config.num_labels     ,		kernel_size=1)



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           self.apply(self._init_weights)



       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

           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							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     torch.Tensor):


           '''simple docstring'''

           __a						       =				encoder_hidden_states[self.in_index]
           __a						       =				self.convs(__SCREAMING_SNAKE_CASE)
           if self.concat_input:
               __a						       =				self.conv_cat(torch.cat([hidden_states, output]     ,		dim=1))
           __a						       =				self.classifier(__SCREAMING_SNAKE_CASE)
           return output


class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						UperNetConfig
       UpperCamelCase__  :    List[str]											=						'''pixel_values'''
       UpperCamelCase__  :    int											=						True



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           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]):


           '''simple docstring'''

           self.backbone.init_weights()
           self.decode_head.init_weights()
           self.auxiliary_head.init_weights()





       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=False):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				value


__snake_case						:Any      		=     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.
'''

__snake_case						:Tuple      		=     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.'''      ,__UpperCAmelCase      ,)
class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           super().__init__(__SCREAMING_SNAKE_CASE)

           __a						       =				AutoBackbone.from_config(config.backbone_config)

           # Semantic segmentation head(s)
           __a						       =				UperNetHead(__SCREAMING_SNAKE_CASE     ,		in_channels=self.backbone.channels)
           __a						       =				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							:     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     ,		):


           '''simple docstring'''

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

           __a						       =				self.backbone.forward_with_filtered_kwargs(
               __SCREAMING_SNAKE_CASE     ,		output_hidden_states=__SCREAMING_SNAKE_CASE     ,		output_attentions=__SCREAMING_SNAKE_CASE)
           __a						       =				outputs.feature_maps

           __a						       =				self.decode_head(__SCREAMING_SNAKE_CASE)
           __a						       =				nn.functional.interpolate(__SCREAMING_SNAKE_CASE     ,		size=pixel_values.shape[2:]     ,		mode='''bilinear'''     ,		align_corners=__SCREAMING_SNAKE_CASE)

           __a						       =				None
           if self.auxiliary_head is not None:
               __a						       =				self.auxiliary_head(__SCREAMING_SNAKE_CASE)
               __a						       =				nn.functional.interpolate(
                   __SCREAMING_SNAKE_CASE     ,		size=pixel_values.shape[2:]     ,		mode='''bilinear'''     ,		align_corners=__SCREAMING_SNAKE_CASE)

           __a						       =				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
                   __a						       =				CrossEntropyLoss(ignore_index=self.config.loss_ignore_index)
                   __a						       =				loss_fct(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
                   __a						       =				loss_fct(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
                   __a						       =				main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

           if not return_dict:
               if output_hidden_states:
                   __a						       =				(logits,) + outputs[1:]
               else:
                   __a						       =				(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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						:Union[str, Any]      		=     logging.get_logger(__name__)

__snake_case						:Any      		=     {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[int]											=						'''switch_transformers'''
       UpperCamelCase__  :    Optional[Any]											=						['''past_key_values''']
       UpperCamelCase__  :    Optional[Any]											=						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     str=32_128     ,		__SCREAMING_SNAKE_CASE							:     int=768     ,		__SCREAMING_SNAKE_CASE							:     Any=64     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2_048     ,		__SCREAMING_SNAKE_CASE							:     List[str]=64     ,		__SCREAMING_SNAKE_CASE							:     int=12     ,		__SCREAMING_SNAKE_CASE							:     Any=3     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=12     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=3     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Tuple=8     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.01     ,		__SCREAMING_SNAKE_CASE							:     Dict="float32"     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=32     ,		__SCREAMING_SNAKE_CASE							:     int=128     ,		__SCREAMING_SNAKE_CASE							:     Any=0.1     ,		__SCREAMING_SNAKE_CASE							:     int=1E-6     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.0_01     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.0_01     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1.0     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="relu"     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=False     ,		__SCREAMING_SNAKE_CASE							:     str=True     ,		__SCREAMING_SNAKE_CASE							:     int=0     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1     ,		**__SCREAMING_SNAKE_CASE							:     Dict     ,		):


           '''simple docstring'''

           __a						       =				vocab_size
           __a						       =				d_model
           __a						       =				d_kv
           __a						       =				d_ff

           __a						       =				num_sparse_encoder_layers

           __a						       =				num_layers
           __a						       =				(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           __a						       =				num_sparse_decoder_layers

           # This tells us, each how many encoder layer we'll have to set a sparse layer.
           if self.num_sparse_encoder_layers > 0:
               __a						       =				self.num_layers // self.num_sparse_encoder_layers
           else:
               __a						       =				self.num_layers  # HACK: this will create 0 sparse layers

           # This tells us, each how many encoder layer we'll have to set a sparse layer.
           if self.num_sparse_decoder_layers > 0:
               __a						       =				self.num_decoder_layers // self.num_sparse_decoder_layers
           else:
               __a						       =				self.num_decoder_layers  # HACK: this will create 0 sparse layers

           __a						       =				num_heads
           __a						       =				num_experts
           __a						       =				expert_capacity
           __a						       =				router_bias
           __a						       =				router_jitter_noise
           if router_dtype not in ["float32", "float16", "bfloat16"]:
               raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}')
           __a						       =				router_dtype

           __a						       =				router_ignore_padding_tokens
           __a						       =				relative_attention_num_buckets
           __a						       =				relative_attention_max_distance

           __a						       =				dropout_rate
           __a						       =				layer_norm_epsilon
           __a						       =				initializer_factor
           __a						       =				feed_forward_proj
           __a						       =				use_cache
           __a						       =				add_router_probs

           __a						       =				router_z_loss_coef
           __a						       =				router_aux_loss_coef

           __a						       =				self.feed_forward_proj.split('''-''')
           __a						       =				act_info[-1]
           __a						       =				act_info[0] == '''gated'''

           if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE) > 2:
               raise ValueError(
                   F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                   '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                   '''\'gated-gelu\' or \'relu\'''')

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
               __a						       =				'''gelu_new'''

           super().__init__(
               pad_token_id=__SCREAMING_SNAKE_CASE     ,		eos_token_id=__SCREAMING_SNAKE_CASE     ,		is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)





