





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

# General docstring
A   : Tuple			=				'''UperNetConfig'''


class   A      (nn.Module   ):
			'''simple docstring'''




			def __init__(      self       :   Dict							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   Union[int, Tuple[int, int]]							,     __lowerCAmelCase       :   Union[int, Tuple[int, int], str] = 0							,     __lowerCAmelCase       :   bool = False							,     __lowerCAmelCase       :   Union[int, Tuple[int, int]] = 1							,     )     ->     None:







					"""simple docstring"""






					super().__init__()
					A__  				=       nn.Convad(
					    in_channels=__lowerCAmelCase							,     out_channels=__lowerCAmelCase							,     kernel_size=__lowerCAmelCase							,     padding=__lowerCAmelCase							,     bias=__lowerCAmelCase							,     dilation=__lowerCAmelCase							,     )
					A__  				=       nn.BatchNormad(__lowerCAmelCase       )
					A__  				=       nn.ReLU()







			def        a_	(      self       :   Dict							,     __lowerCAmelCase       :   torch.Tensor       )     ->     torch.Tensor:







					"""simple docstring"""






					A__  				=       self.conv(__lowerCAmelCase       )
					A__  				=       self.batch_norm(__lowerCAmelCase       )
					A__  				=       self.activation(__lowerCAmelCase       )

					return output


class   A      (nn.Module   ):
			'''simple docstring'''




			def __init__(      self       :   Any							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   int       )     ->     None:







					"""simple docstring"""






					super().__init__()
					A__  				=       [
					    nn.AdaptiveAvgPoolad(__lowerCAmelCase       ),
					    UperNetConvModule(__lowerCAmelCase							,     __lowerCAmelCase							,     kernel_size=1       ),
					]
					for i, layer in enumerate(self.layers       ):
							self.add_module(str(__lowerCAmelCase       )							,     __lowerCAmelCase       )







			def        a_	(      self       :   Any							,     __lowerCAmelCase       :   torch.Tensor       )     ->     torch.Tensor:







					"""simple docstring"""






					A__  				=       input
					for layer in self.layers:
							A__  				=       layer(__lowerCAmelCase       )
					return hidden_state


class   A      (nn.Module   ):
			'''simple docstring'''




			def __init__(      self       :   int							,     __lowerCAmelCase       :   Tuple[int, ...]							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   bool       )     ->     None:







					"""simple docstring"""






					super().__init__()
					A__  				=       pool_scales
					A__  				=       align_corners
					A__  				=       in_channels
					A__  				=       channels
					A__  				=       []
					for i, pool_scale in enumerate(__lowerCAmelCase       ):
							A__  				=       UperNetPyramidPoolingBlock(pool_scale=__lowerCAmelCase							,     in_channels=__lowerCAmelCase							,     channels=__lowerCAmelCase       )
							self.blocks.append(__lowerCAmelCase       )
							self.add_module(str(__lowerCAmelCase       )							,     __lowerCAmelCase       )







			def        a_	(      self       :   str							,     __lowerCAmelCase       :   torch.Tensor       )     ->     List[torch.Tensor]:







					"""simple docstring"""






					A__  				=       []
					for ppm in self.blocks:
							A__  				=       ppm(__lowerCAmelCase       )
							A__  				=       nn.functional.interpolate(
							    __lowerCAmelCase							,     size=x.size()[2:]							,     mode="""bilinear"""							,     align_corners=self.align_corners       )
							ppm_outs.append(__lowerCAmelCase       )
					return ppm_outs


class   A      (nn.Module   ):
			'''simple docstring'''




			def __init__(      self       :   Tuple							,     __lowerCAmelCase       :   Union[str, Any]							,     __lowerCAmelCase       :   List[Any]       )     ->     int:







					"""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(__lowerCAmelCase							,     self.channels							,     kernel_size=1       )
							A__  				=       UperNetConvModule(self.channels							,     self.channels							,     kernel_size=3							,     padding=1       )
							self.lateral_convs.append(__lowerCAmelCase       )
							self.fpn_convs.append(__lowerCAmelCase       )

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






			def        a_	(      self       :   str       )     ->     str:







					"""simple docstring"""






					self.apply(self._init_weights       )






			def        a_	(      self       :   Optional[Any]							,     __lowerCAmelCase       :   Tuple       )     ->     List[Any]:







					"""simple docstring"""






					if isinstance(__lowerCAmelCase							,     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        a_	(      self       :   Any							,     __lowerCAmelCase       :   Tuple       )     ->     Optional[Any]:







					"""simple docstring"""






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

					return output







			def        a_	(      self       :   Dict							,     __lowerCAmelCase       :   torch.Tensor       )     ->     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(__lowerCAmelCase       )       )

					# build top-down path
					A__  				=       len(__lowerCAmelCase       )
					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=__lowerCAmelCase							,     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(__lowerCAmelCase							,     dim=1       )
					A__  				=       self.fpn_bottleneck(__lowerCAmelCase       )
					A__  				=       self.classifier(__lowerCAmelCase       )

					return output


class   A      (nn.Module   ):
			'''simple docstring'''




			def __init__(      self       :   Optional[Any]							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 3							,     __lowerCAmelCase       :   Union[int, Tuple[int, int]] = 1       )     ->     None:







					"""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=__lowerCAmelCase							,     padding=__lowerCAmelCase							,     dilation=__lowerCAmelCase       )       )
					for i in range(self.num_convs - 1       ):
							convs.append(
							    UperNetConvModule(
							        self.channels							,     self.channels							,     kernel_size=__lowerCAmelCase							,     padding=__lowerCAmelCase							,     dilation=__lowerCAmelCase       )       )
					if self.num_convs == 0:
							A__  				=       nn.Identity()
					else:
							A__  				=       nn.Sequential(*__lowerCAmelCase       )
					if self.concat_input:
							A__  				=       UperNetConvModule(
							    self.in_channels + self.channels							,     self.channels							,     kernel_size=__lowerCAmelCase							,     padding=kernel_size // 2       )

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






			def        a_	(      self       :   Union[str, Any]       )     ->     Union[str, Any]:







					"""simple docstring"""






					self.apply(self._init_weights       )






			def        a_	(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   int       )     ->     List[Any]:







					"""simple docstring"""






					if isinstance(__lowerCAmelCase							,     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        a_	(      self       :   List[str]							,     __lowerCAmelCase       :   torch.Tensor       )     ->     torch.Tensor:







					"""simple docstring"""






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


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Tuple          =   UperNetConfig
			__lowerCamelCase     :	Union[str, Any]          =   '''pixel_values'''
			__lowerCamelCase     :	List[Any]          =   True






			def        a_	(      self       :   str							,     __lowerCAmelCase       :   List[str]       )     ->     Any:







					"""simple docstring"""






					if isinstance(__lowerCAmelCase							,     __lowerCAmelCase       ):
							module.backbone.init_weights()
							module.decode_head.init_weights()
							module.auxiliary_head.init_weights()






			def        a_	(      self       :   str       )     ->     List[Any]:







					"""simple docstring"""






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







			def        a_	(      self       :   List[str]							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   Dict=False       )     ->     Dict:







					"""simple docstring"""






					if isinstance(__lowerCAmelCase							,     __lowerCAmelCase       ):
							A__  				=       value

A   : str			=				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   : List[str]			=				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   ):
			'''simple docstring'''




			def __init__(      self       :   List[str]							,     __lowerCAmelCase       :   Optional[Any]       )     ->     List[str]:







					"""simple docstring"""






					super().__init__(__lowerCAmelCase       )

					A__  				=       AutoBackbone.from_config(config.backbone_config       )

					# Semantic segmentation head(s)
					A__  				=       UperNetHead(__lowerCAmelCase							,     in_channels=self.backbone.channels       )
					A__  				=       UperNetFCNHead(__lowerCAmelCase       ) 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=__lowerCAmelCase							,     config_class=_CONFIG_FOR_DOC       )
			def        a_	(      self       :   Any							,     __lowerCAmelCase       :   Optional[torch.Tensor] = None							,     __lowerCAmelCase       :   Optional[bool] = None							,     __lowerCAmelCase       :   Optional[bool] = None							,     __lowerCAmelCase       :   Optional[torch.Tensor] = None							,     __lowerCAmelCase       :   Optional[bool] = None							,     )     ->     Union[tuple, SemanticSegmenterOutput]:







					"""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(
					    __lowerCAmelCase							,     output_hidden_states=__lowerCAmelCase							,     output_attentions=__lowerCAmelCase       )
					A__  				=       outputs.feature_maps

					A__  				=       self.decode_head(__lowerCAmelCase       )
					A__  				=       nn.functional.interpolate(__lowerCAmelCase							,     size=pixel_values.shape[2:]							,     mode="""bilinear"""							,     align_corners=__lowerCAmelCase       )

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

					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(__lowerCAmelCase							,     __lowerCAmelCase       )
									A__  				=       loss_fct(__lowerCAmelCase							,     __lowerCAmelCase       )
									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=__lowerCAmelCase							,     logits=__lowerCAmelCase							,     hidden_states=outputs.hidden_states							,     attentions=outputs.attentions							,     )










import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					A   : List[str]			=				argparse.ArgumentParser(
					    description=(
					        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
					        ''' Distillation'''
					    )
					)
					parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
					parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
					parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
					parser.add_argument('''--vocab_transform''', action='''store_true''')
					A   : Tuple			=				parser.parse_args()

					if args.model_type == "bert":
										A   : Dict			=				BertForMaskedLM.from_pretrained(args.model_name)
										A   : List[str]			=				'''bert'''
					else:
										raise ValueError('''args.model_type should be "bert".''')

					A   : Optional[Any]			=				model.state_dict()
					A   : int			=				{}

					for w in ["word_embeddings", "position_embeddings"]:
										A   : str			=				state_dict[F'''{prefix}.embeddings.{w}.weight''']
					for w in ["weight", "bias"]:
										A   : Any			=				state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

					A   : Tuple			=				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															A   : int			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															A   : List[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
															]
															A   : Union[str, Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
															]
															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
															]
										std_idx += 1

					A   : int			=				state_dict['''cls.predictions.decoder.weight''']
					A   : str			=				state_dict['''cls.predictions.bias''']
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															A   : List[Any]			=				state_dict[F'''cls.predictions.transform.dense.{w}''']
															A   : List[str]			=				state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

					print(F'''N layers selected for distillation: {std_idx}''')
					print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

					print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
					torch.save(compressed_sd, args.dump_checkpoint)



