




"""simple docstring"""






import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


UpperCamelCase__						=			logging.getLogger(__name__)




class   a__			(       UpperCamelCase_  ):





						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Optional[int]     ,a__      :	Optional[int]     ,a__      :	int=None     ,a__      :	Any=None)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.layer[current_layer](a__     ,a__     ,head_mask[current_layer])

											_lowerCAmelCase:Tuple									=  layer_outputs[0]

											return hidden_states




@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''					,	UpperCamelCase_					,	)
class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	int     ,a__      :	Any)						->       Tuple:




											"""simple docstring"""







											super().__init__(a__)

											_lowerCAmelCase:List[Any]									=  BertEncoderWithPabee(a__)

											self.init_weights()
											_lowerCAmelCase:Dict									=  0
											_lowerCAmelCase:Dict									=  0
											_lowerCAmelCase:Tuple									=  0

											_lowerCAmelCase:Tuple									=  0





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Any)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:int									=  threshold





						def       __UpperCamelCase     (							self      :	List[str]     ,a__      :	Dict)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  patience





						def       __UpperCamelCase     (							self      :	List[Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  0
											_lowerCAmelCase:Dict									=  0





						def       __UpperCamelCase     (							self      :	Optional[int])						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.inference_layers_num / self.inference_instances_num
											_lowerCAmelCase:Tuple									=  (
											    F'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
											    F' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
											)
											print(a__)







						@add_start_docstrings_to_model_forward(a__)
						def       __UpperCamelCase     (							self      :	List[str]     ,a__      :	List[str]=None     ,a__      :	Union[str, Any]=None     ,a__      :	Tuple=None     ,a__      :	Optional[Any]=None     ,a__      :	Optional[int]=None     ,a__      :	List[str]=None     ,a__      :	List[str]=None     ,a__      :	Tuple=None     ,a__      :	str=None     ,a__      :	Optional[Any]=None     ,a__      :	Dict=False     ,)						->       str:




											"""simple docstring"""







											if input_ids is not None and inputs_embeds is not None:
																raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''')
											elif input_ids is not None:
																_lowerCAmelCase:Optional[Any]									=  input_ids.size()
											elif inputs_embeds is not None:
																_lowerCAmelCase:Union[str, Any]									=  inputs_embeds.size()[:-1]
											else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds''')

											_lowerCAmelCase:int									=  input_ids.device if input_ids is not None else inputs_embeds.device

											if attention_mask is None:
																_lowerCAmelCase:Union[str, Any]									=  torch.ones(a__     ,device=a__)
											if token_type_ids is None:
																_lowerCAmelCase:Optional[Any]									=  torch.zeros(a__     ,dtype=torch.long     ,device=a__)

											# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
											# ourselves in which case we just need to make it broadcastable to all heads.
											_lowerCAmelCase:torch.Tensor									=  self.get_extended_attention_mask(a__     ,a__     ,a__)

											# If a 2D ou 3D attention mask is provided for the cross-attention
											# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
											if self.config.is_decoder and encoder_hidden_states is not None:
																_lowerCAmelCase:List[Any]									=  encoder_hidden_states.size()
																_lowerCAmelCase:Any									=  (encoder_batch_size, encoder_sequence_length)
																if encoder_attention_mask is None:
																					_lowerCAmelCase:Optional[Any]									=  torch.ones(a__     ,device=a__)
																_lowerCAmelCase:int									=  self.invert_attention_mask(a__)
											else:
																_lowerCAmelCase:Dict									=  None

											# Prepare head mask if needed
											# 1.0 in head_mask indicate we keep the head
											# attention_probs has shape bsz x n_heads x N x N
											# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
											# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
											_lowerCAmelCase:Dict									=  self.get_head_mask(a__     ,self.config.num_hidden_layers)

											_lowerCAmelCase:str									=  self.embeddings(
											    input_ids=a__     ,position_ids=a__     ,token_type_ids=a__     ,inputs_embeds=a__)
											_lowerCAmelCase:List[Any]									=  embedding_output

											if self.training:
																_lowerCAmelCase:str									=  []
																for i in range(self.config.num_hidden_layers):
																					_lowerCAmelCase:List[str]									=  self.encoder.adaptive_forward(
																					    a__     ,current_layer=a__     ,attention_mask=a__     ,head_mask=a__)

																					_lowerCAmelCase:Optional[int]									=  self.pooler(a__)
																					_lowerCAmelCase:Optional[int]									=  output_layers[i](output_dropout(a__))
																					res.append(a__)
											elif self.patience == 0:  # Use all layers for inference
																_lowerCAmelCase:Optional[int]									=  self.encoder(
																    a__     ,attention_mask=a__     ,head_mask=a__     ,encoder_hidden_states=a__     ,encoder_attention_mask=a__     ,)
																_lowerCAmelCase:str									=  self.pooler(encoder_outputs[0])
																_lowerCAmelCase:int									=  [output_layers[self.config.num_hidden_layers - 1](a__)]
											else:
																_lowerCAmelCase:int									=  0
																_lowerCAmelCase:Optional[Any]									=  None
																_lowerCAmelCase:Optional[Any]									=  0
																for i in range(self.config.num_hidden_layers):
																					calculated_layer_num += 1
																					_lowerCAmelCase:Optional[int]									=  self.encoder.adaptive_forward(
																					    a__     ,current_layer=a__     ,attention_mask=a__     ,head_mask=a__)

																					_lowerCAmelCase:Tuple									=  self.pooler(a__)
																					_lowerCAmelCase:Dict									=  output_layers[i](a__)
																					if regression:
																										_lowerCAmelCase:List[str]									=  logits.detach()
																										if patient_result is not None:
																															_lowerCAmelCase:Union[str, Any]									=  patient_result.detach()
																										if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
																															patient_counter += 1
																										else:
																															_lowerCAmelCase:Optional[int]									=  0
																					else:
																										_lowerCAmelCase:Optional[int]									=  logits.detach().argmax(dim=1)
																										if patient_result is not None:
																															_lowerCAmelCase:List[str]									=  patient_result.detach().argmax(dim=1)
																										if (patient_result is not None) and torch.all(labels.eq(a__)):
																															patient_counter += 1
																										else:
																															_lowerCAmelCase:Union[str, Any]									=  0

																					_lowerCAmelCase:Optional[Any]									=  logits
																					if patient_counter == self.patience:
																										break
																_lowerCAmelCase:str									=  [patient_result]
																self.inference_layers_num += calculated_layer_num
																self.inference_instances_num += 1

											return res




@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''					,	UpperCamelCase_					,	)
class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	Optional[int]     ,a__      :	Tuple)						->       Dict:




											"""simple docstring"""







											super().__init__(a__)
											_lowerCAmelCase:str									=  config.num_labels

											_lowerCAmelCase:str									=  BertModelWithPabee(a__)
											_lowerCAmelCase:str									=  nn.Dropout(config.hidden_dropout_prob)
											_lowerCAmelCase:Any									=  nn.ModuleList(
											    [nn.Linear(config.hidden_size     ,self.config.num_labels) for _ in range(config.num_hidden_layers)])

											self.init_weights()







						@add_start_docstrings_to_model_forward(a__)
						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	List[str]=None     ,a__      :	Dict=None     ,a__      :	str=None     ,a__      :	List[str]=None     ,a__      :	Optional[Any]=None     ,a__      :	Optional[Any]=None     ,a__      :	Optional[int]=None     ,)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.bert(
											    input_ids=a__     ,attention_mask=a__     ,token_type_ids=a__     ,position_ids=a__     ,head_mask=a__     ,inputs_embeds=a__     ,output_dropout=self.dropout     ,output_layers=self.classifiers     ,regression=self.num_labels == 1     ,)

											_lowerCAmelCase:int									=  (logits[-1],)

											if labels is not None:
																_lowerCAmelCase:Any									=  None
																_lowerCAmelCase:int									=  0
																for ix, logits_item in enumerate(a__):
																					if self.num_labels == 1:
																										#  We are doing regression
																										_lowerCAmelCase:Tuple									=  MSELoss()
																										_lowerCAmelCase:List[Any]									=  loss_fct(logits_item.view(-1)     ,labels.view(-1))
																					else:
																										_lowerCAmelCase:Union[str, Any]									=  CrossEntropyLoss()
																										_lowerCAmelCase:Dict									=  loss_fct(logits_item.view(-1     ,self.num_labels)     ,labels.view(-1))
																					if total_loss is None:
																										_lowerCAmelCase:Tuple									=  loss
																					else:
																										total_loss += loss * (ix + 1)
																					total_weights += ix + 1
																_lowerCAmelCase:List[Any]									=  (total_loss / total_weights,) + outputs

											return outputs










"""simple docstring"""






import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation





def 							UpperCAmelCase		(		snake_case			:					Tuple    ):
					_lowerCAmelCase:Tuple									=  384
					if "tiny" in model_name:
										_lowerCAmelCase:Any									=  [3, 3, 9, 3]
										_lowerCAmelCase:Union[str, Any]									=  [96, 192, 384, 768]
					if "small" in model_name:
										_lowerCAmelCase:List[str]									=  [3, 3, 27, 3]
										_lowerCAmelCase:Any									=  [96, 192, 384, 768]
					if "base" in model_name:
										_lowerCAmelCase:int									=  [3, 3, 27, 3]
										_lowerCAmelCase:int									=  [128, 256, 512, 1024]
										_lowerCAmelCase:Any									=  512
					if "large" in model_name:
										_lowerCAmelCase:Optional[int]									=  [3, 3, 27, 3]
										_lowerCAmelCase:List[str]									=  [192, 384, 768, 1536]
										_lowerCAmelCase:List[Any]									=  768
					if "xlarge" in model_name:
										_lowerCAmelCase:str									=  [3, 3, 27, 3]
										_lowerCAmelCase:Optional[int]									=  [256, 512, 1024, 2048]
										_lowerCAmelCase:Optional[int]									=  1024

					# set label information
					_lowerCAmelCase:Tuple									=  150
					_lowerCAmelCase:int									=  '''huggingface/label-files'''
					_lowerCAmelCase:Optional[int]									=  '''ade20k-id2label.json'''
					_lowerCAmelCase:List[str]									=  json.load(open(hf_hub_download(snake_case      ,       snake_case      ,       repo_type='''dataset'''    )      ,       '''r'''    )    )
					_lowerCAmelCase:Dict									=  {int(snake_case    ): v for k, v in idalabel.items()}
					_lowerCAmelCase:Tuple									=  {v: k for k, v in idalabel.items()}

					_lowerCAmelCase:str									=  ConvNextConfig(
					    depths=snake_case      ,       hidden_sizes=snake_case      ,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
					_lowerCAmelCase:Union[str, Any]									=  UperNetConfig(
					    backbone_config=snake_case      ,       auxiliary_in_channels=snake_case      ,       num_labels=snake_case      ,       idalabel=snake_case      ,       labelaid=snake_case      ,       )

					return config





def 							UpperCAmelCase		(		snake_case			:					List[Any]    ):
					_lowerCAmelCase:List[str]									=  []

					# fmt: off
					# stem
					rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight''')    )
					rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias''')    )
					rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight''')    )
					rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.bias''')    )
					# stages
					for i in range(len(config.backbone_config.depths    )    ):
										for j in range(config.backbone_config.depths[i]    ):
															rename_keys.append((F'backbone.stages.{i}.{j}.gamma', F'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.weight', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.bias', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.norm.weight', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.norm.bias', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')    )
										if i > 0:
															rename_keys.append((F'backbone.downsample_layers.{i}.0.weight', F'backbone.encoder.stages.{i}.downsampling_layer.0.weight')    )
															rename_keys.append((F'backbone.downsample_layers.{i}.0.bias', F'backbone.encoder.stages.{i}.downsampling_layer.0.bias')    )
															rename_keys.append((F'backbone.downsample_layers.{i}.1.weight', F'backbone.encoder.stages.{i}.downsampling_layer.1.weight')    )
															rename_keys.append((F'backbone.downsample_layers.{i}.1.bias', F'backbone.encoder.stages.{i}.downsampling_layer.1.bias')    )

										rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight')    )
										rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias')    )

					# decode head
					rename_keys.extend(
					    [
					        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
					        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
					        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
					        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
					    ]    )
					# fmt: on

					return rename_keys





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					Tuple      ,       snake_case			:					Optional[int]    ):
					_lowerCAmelCase:int									=  dct.pop(snake_case    )
					_lowerCAmelCase:Union[str, Any]									=  val





def 							UpperCAmelCase		(		snake_case			:					List[str]      ,       snake_case			:					int      ,       snake_case			:					Optional[Any]    ):
					_lowerCAmelCase:Dict									=  {
					    '''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
					    '''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
					    '''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
					    '''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
					    '''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
					}
					_lowerCAmelCase:Optional[Any]									=  model_name_to_url[model_name]
					_lowerCAmelCase:Optional[Any]									=  torch.hub.load_state_dict_from_url(snake_case      ,       map_location='''cpu'''    )['''state_dict''']

					_lowerCAmelCase:Tuple									=  get_upernet_config(snake_case    )
					_lowerCAmelCase:Union[str, Any]									=  UperNetForSemanticSegmentation(snake_case    )
					model.eval()

					# replace "bn" => "batch_norm"
					for key in state_dict.copy().keys():
										_lowerCAmelCase:str									=  state_dict.pop(snake_case    )
										if "bn" in key:
															_lowerCAmelCase:str									=  key.replace('''bn'''      ,       '''batch_norm'''    )
										_lowerCAmelCase:Optional[Any]									=  val

					# rename keys
					_lowerCAmelCase:int									=  create_rename_keys(snake_case    )
					for src, dest in rename_keys:
										rename_key(snake_case      ,       snake_case      ,       snake_case    )

					model.load_state_dict(snake_case    )

					# verify on image
					_lowerCAmelCase:Optional[int]									=  '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
					_lowerCAmelCase:Optional[int]									=  Image.open(requests.get(snake_case      ,       stream=snake_case    ).raw    ).convert('''RGB'''    )

					_lowerCAmelCase:int									=  SegformerImageProcessor()
					_lowerCAmelCase:Optional[int]									=  processor(snake_case      ,       return_tensors='''pt'''    ).pixel_values

					with torch.no_grad():
										_lowerCAmelCase:str									=  model(snake_case    )

					if model_name == "upernet-convnext-tiny":
										_lowerCAmelCase:Union[str, Any]									=  torch.tensor(
										    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]    )
					elif model_name == "upernet-convnext-small":
										_lowerCAmelCase:List[str]									=  torch.tensor(
										    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]    )
					elif model_name == "upernet-convnext-base":
										_lowerCAmelCase:Dict									=  torch.tensor(
										    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]    )
					elif model_name == "upernet-convnext-large":
										_lowerCAmelCase:Union[str, Any]									=  torch.tensor(
										    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]    )
					elif model_name == "upernet-convnext-xlarge":
										_lowerCAmelCase:List[str]									=  torch.tensor(
										    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]    )
					print('''Logits:'''      ,       outputs.logits[0, 0, :3, :3]    )
					assert torch.allclose(outputs.logits[0, 0, :3, :3]      ,       snake_case      ,       atol=1e-4    )
					print('''Looks ok!'''    )

					if pytorch_dump_folder_path is not None:
										print(F'Saving model {model_name} to {pytorch_dump_folder_path}'    )
										model.save_pretrained(snake_case    )
										print(F'Saving processor to {pytorch_dump_folder_path}'    )
										processor.save_pretrained(snake_case    )

					if push_to_hub:
										print(F'Pushing model and processor for {model_name} to hub'    )
										model.push_to_hub(F'openmmlab/{model_name}'    )
										processor.push_to_hub(F'openmmlab/{model_name}'    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''upernet-convnext-tiny''',
				    type=str,
				    choices=[F"upernet-convnext-{size}" for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
				    help='''Name of the ConvNext UperNet model you\'d like to convert.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
				)

				UpperCamelCase__						=			parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






