


import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


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


__UpperCAmelCase : Optional[int] 			=				"https://openaipublic.azureedge.net/jukebox/models/"
__UpperCAmelCase : Optional[Any] 			=				{
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}


def      a	(							SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       if key.endswith('''.model.1.bias'''       ) and len(key.split('''.'''       )       ) > 1_0:
              UpperCamelCase    :	Any   	=		key.replace('''.model.1.bias'''							,			'''.conv1d_1.bias'''       )
       elif key.endswith('''.model.1.weight'''       ) and len(key.split('''.'''       )       ) > 1_0:
              UpperCamelCase    :	Optional[int]   	=		key.replace('''.model.1.weight'''							,			'''.conv1d_1.weight'''       )
       elif key.endswith('''.model.3.bias'''       ) and len(key.split('''.'''       )       ) > 1_0:
              UpperCamelCase    :	int   	=		key.replace('''.model.3.bias'''							,			'''.conv1d_2.bias'''       )
       elif key.endswith('''.model.3.weight'''       ) and len(key.split('''.'''       )       ) > 1_0:
              UpperCamelCase    :	Union[str, Any]   	=		key.replace('''.model.3.weight'''							,			'''.conv1d_2.weight'''       )

       if "conditioner_blocks.0." in key:
              UpperCamelCase    :	Union[str, Any]   	=		key.replace('''conditioner_blocks.0'''							,			'''conditioner_blocks'''       )

       if "prime_prior" in key:
              UpperCamelCase    :	Any   	=		key.replace('''prime_prior'''							,			'''encoder'''       )

       if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
              UpperCamelCase    :	Union[str, Any]   	=		key.replace('''.emb.'''							,			'''.'''       )

       if key.endswith('''k'''       ):  # replace vqvae.X.k with vqvae.X.codebook
              return key.replace('''.k'''							,			'''.codebook'''       )
       if "y_emb." in key:
              return key.replace('''y_emb.'''							,			'''metadata_embedding.'''       )

       if "x_emb.emb." in key:
              UpperCamelCase    :	str   	=		key.replace('''0.x_emb.emb'''							,			'''embed_tokens'''       )

       if "prime_state_ln" in key:
              return key.replace('''prime_state_ln'''							,			'''encoder.final_layer_norm'''       )
       if ".ln" in key:
              return key.replace('''.ln'''							,			'''.layer_norm'''       )
       if "_ln" in key:
              return key.replace('''_ln'''							,			'''_layer_norm'''       )

       if "prime_state_proj" in key:
              return key.replace('''prime_state_proj'''							,			'''encoder.proj_in'''       )
       if "prime_x_out" in key:
              return key.replace('''prime_x_out'''							,			'''encoder.lm_head'''       )
       if "prior.x_out" in key:
              return key.replace('''x_out'''							,			'''fc_proj_out'''       )
       if "x_emb" in key:
              return key.replace('''x_emb'''							,			'''embed_tokens'''       )

       return key


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





       UpperCamelCase    :	str   	=		{}
       import re

       UpperCamelCase    :	int   	=		re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''       )
       UpperCamelCase    :	Any   	=		re.compile(
           R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
       UpperCamelCase    :	List[str]   	=		re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''       )

       UpperCamelCase    :	Dict   	=		re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''       )
       UpperCamelCase    :	Union[str, Any]   	=		re.compile(
           R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
       UpperCamelCase    :	Optional[Any]   	=		re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''       )

       UpperCamelCase    :	Optional[int]   	=		re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''       )
       UpperCamelCase    :	Tuple   	=		re.compile(
           R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
       UpperCamelCase    :	Any   	=		re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''       )

       for original_key, value in state_dict.items():
              # rename vqvae.encoder keys
              if re_encoder_block_conv_in.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Dict   	=		re_encoder_block_conv_in.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	List[Any]   	=		regex_match.groups()
                     UpperCamelCase    :	Union[str, Any]   	=		int(groups[2]       ) * 2 + int(groups[3]       )
                     UpperCamelCase    :	Dict   	=		F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
                     UpperCamelCase    :	str   	=		re_encoder_block_conv_in.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_encoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Dict   	=		re_encoder_block_resnet.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	List[Any]   	=		regex_match.groups()
                     UpperCamelCase    :	int   	=		int(groups[2]       ) * 2 + int(groups[3]       )
                     UpperCamelCase    :	Any   	=		{'''1''': 1, '''3''': 2}[groups[-2]]
                     UpperCamelCase    :	List[Any]   	=		F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
                     UpperCamelCase    :	Any   	=		F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     UpperCamelCase    :	Any   	=		prefix + resnet_block
                     UpperCamelCase    :	List[str]   	=		re_encoder_block_resnet.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_encoder_block_proj_out.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Union[str, Any]   	=		re_encoder_block_proj_out.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	Union[str, Any]   	=		regex_match.groups()
                     UpperCamelCase    :	Tuple   	=		F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
                     UpperCamelCase    :	str   	=		re_encoder_block_proj_out.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              # rename vqvae.decoder keys
              elif re_decoder_block_conv_out.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Dict   	=		re_decoder_block_conv_out.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	List[Any]   	=		regex_match.groups()
                     UpperCamelCase    :	Optional[Any]   	=		int(groups[2]       ) * 2 + int(groups[3]       ) - 2
                     UpperCamelCase    :	Optional[Any]   	=		F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
                     UpperCamelCase    :	List[str]   	=		re_decoder_block_conv_out.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_decoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Optional[Any]   	=		re_decoder_block_resnet.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	List[Any]   	=		regex_match.groups()
                     UpperCamelCase    :	Any   	=		int(groups[2]       ) * 2 + int(groups[3]       ) - 2
                     UpperCamelCase    :	int   	=		{'''1''': 1, '''3''': 2}[groups[-2]]
                     UpperCamelCase    :	str   	=		F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
                     UpperCamelCase    :	Tuple   	=		F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     UpperCamelCase    :	str   	=		prefix + resnet_block
                     UpperCamelCase    :	Optional[Any]   	=		re_decoder_block_resnet.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_decoder_block_proj_in.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Optional[int]   	=		re_decoder_block_proj_in.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	List[str]   	=		regex_match.groups()
                     UpperCamelCase    :	List[str]   	=		F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
                     UpperCamelCase    :	Optional[int]   	=		re_decoder_block_proj_in.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              # rename prior cond.model to upsampler.upsample_block and resnet
              elif re_prior_cond_conv_out.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Dict   	=		re_prior_cond_conv_out.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	Optional[int]   	=		regex_match.groups()
                     UpperCamelCase    :	str   	=		int(groups[1]       ) * 2 + int(groups[2]       ) - 2
                     UpperCamelCase    :	Optional[Any]   	=		F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
                     UpperCamelCase    :	int   	=		re_prior_cond_conv_out.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_prior_cond_resnet.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Union[str, Any]   	=		re_prior_cond_resnet.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	Tuple   	=		regex_match.groups()
                     UpperCamelCase    :	List[Any]   	=		int(groups[1]       ) * 2 + int(groups[2]       ) - 2
                     UpperCamelCase    :	Optional[int]   	=		{'''1''': 1, '''3''': 2}[groups[-2]]
                     UpperCamelCase    :	str   	=		F"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
                     UpperCamelCase    :	Optional[int]   	=		F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     UpperCamelCase    :	Optional[Any]   	=		prefix + resnet_block
                     UpperCamelCase    :	List[str]   	=		re_prior_cond_resnet.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_prior_cond_proj_in.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	List[Any]   	=		re_prior_cond_proj_in.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	Optional[Any]   	=		regex_match.groups()
                     UpperCamelCase    :	int   	=		F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
                     UpperCamelCase    :	Optional[Any]   	=		re_prior_cond_proj_in.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              # keep original key
              else:
                     UpperCamelCase    :	List[Any]   	=		original_key

              UpperCamelCase    :	List[str]   	=		replace_key(SCREAMING_SNAKE_CASE_       )

              if F"""{key_prefix}.{key}""" not in model_state_dict or key is None:
                     print(F"""failed converting {original_key} to {key}, does not match"""       )

              # handle missmatched shape
              elif value.shape != model_state_dict[F"""{key_prefix}.{key}"""].shape:
                     UpperCamelCase    :	Optional[int]   	=		model_state_dict[F"""{key_prefix}.{key}"""]
                     print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""       )
                     UpperCamelCase    :	List[Any]   	=		original_key

              UpperCamelCase    :	str   	=		original_key
              UpperCamelCase    :	List[str]   	=		value

       return new_dict


@torch.no_grad()
def      a	(							SCREAMING_SNAKE_CASE_      :		Any=None							,			SCREAMING_SNAKE_CASE_      :		Dict=None       ):






       """simple docstring"""





       for file in MODEL_MAPPING[model_name]:
              if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/"       )[-1]}"""       ):
                     UpperCamelCase    :	Tuple   	=		requests.get(F"""{PREFIX}{file}"""							,			allow_redirects=SCREAMING_SNAKE_CASE_       )
                     os.makedirs(F"""{pytorch_dump_folder_path}/"""							,			exist_ok=SCREAMING_SNAKE_CASE_       )
                     open(F"""{pytorch_dump_folder_path}/{file.split("/"       )[-1]}"""							,			'''wb'''       ).write(r.content       )

       UpperCamelCase    :	int   	=		MODEL_MAPPING[model_name.split('''/'''       )[-1]]

       UpperCamelCase    :	Optional[Any]   	=		JukeboxConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	str   	=		JukeboxModel(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	Tuple   	=		[]
       UpperCamelCase    :	Optional[Any]   	=		{}
       for i, dict_name in enumerate(SCREAMING_SNAKE_CASE_       ):
              UpperCamelCase    :	List[str]   	=		torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split("/"       )[-1]}"""       )['''model''']

              UpperCamelCase    :	int   	=		{}
              for k in old_dic.keys():
                     if k.endswith('''.b'''       ):
                            UpperCamelCase    :	List[Any]   	=		old_dic[k]
                     elif k.endswith('''.w'''       ):
                            UpperCamelCase    :	Any   	=		old_dic[k]
                     elif "level_2" not in dict_name and "cond.model." in k:
                            UpperCamelCase    :	Optional[int]   	=		old_dic[k]
                     else:
                            UpperCamelCase    :	List[str]   	=		old_dic[k]

              UpperCamelCase    :	Union[str, Any]   	=		'''vqvae''' if i == 0 else F"""priors.{3 - i}"""
              UpperCamelCase    :	Optional[Any]   	=		fix_jukebox_keys(SCREAMING_SNAKE_CASE_							,			model.state_dict()							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
              weight_dict.append(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	List[str]   	=		weight_dict.pop(0       )
       model.vqvae.load_state_dict(SCREAMING_SNAKE_CASE_       )
       for i in range(len(SCREAMING_SNAKE_CASE_       )       ):
              model.priors[i].load_state_dict(weight_dict[2 - i]       )

       Path(SCREAMING_SNAKE_CASE_       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_       )
       with open(F"""{pytorch_dump_folder_path}/mapping.json"""							,			'''w'''       ) as txtfile:
              json.dump(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

       print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""       )
       model.save_pretrained(SCREAMING_SNAKE_CASE_       )

       return weight_dict


if __name__ == "__main__":
  __UpperCAmelCase : Optional[int] 			=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="jukebox-5b-lyrics",
      type=str,
      help="Name of the model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="jukebox-5b-lyrics-converted",
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  __UpperCAmelCase : Tuple 			=				parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





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


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

# General docstring
__UpperCAmelCase : List[str] 			=				"UperNetConfig"



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






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 0 ,					__SCREAMING_SNAKE_CASE = False ,					__SCREAMING_SNAKE_CASE = 1 ,					):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	str   	=		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 ,					)
              UpperCamelCase    :	int   	=		nn.BatchNormad(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[Any]   	=		nn.ReLU()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		self.conv(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[Any]   	=		self.batch_norm(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		self.activation(__SCREAMING_SNAKE_CASE						)

              return output




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






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	List[Any]   	=		[
                  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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		input
              for layer in self.layers:
                     UpperCamelCase    :	int   	=		layer(__SCREAMING_SNAKE_CASE						)
              return hidden_state




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






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	List[Any]   	=		pool_scales
              UpperCamelCase    :	Dict   	=		align_corners
              UpperCamelCase    :	Optional[int]   	=		in_channels
              UpperCamelCase    :	Union[str, Any]   	=		channels
              UpperCamelCase    :	List[str]   	=		[]
              for i, pool_scale in enumerate(__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Union[str, Any]   	=		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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		[]
              for ppm in self.blocks:
                     UpperCamelCase    :	List[str]   	=		ppm(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Tuple   	=		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    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()

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

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

              UpperCamelCase    :	Optional[Any]   	=		UperNetConvModule(
                  len(self.in_channels						) * self.channels ,					self.channels ,					kernel_size=3 ,					padding=1 ,					)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.apply(self._init_weights						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		inputs[-1]
              UpperCamelCase    :	int   	=		[x]
              psp_outs.extend(self.psp_modules(__SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase    :	Any   	=		torch.cat(__SCREAMING_SNAKE_CASE ,					dim=1						)
              UpperCamelCase    :	Union[str, Any]   	=		self.bottleneck(__SCREAMING_SNAKE_CASE						)

              return output






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




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

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

              return output




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






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 2 ,					__SCREAMING_SNAKE_CASE = 3 ,					__SCREAMING_SNAKE_CASE = 1						):




              """simple docstring"""




              super().__init__()

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

              UpperCamelCase    :	Any   	=		(kernel_size // 2) * dilation
              UpperCamelCase    :	Optional[Any]   	=		[]
              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:
                     UpperCamelCase    :	str   	=		nn.Identity()
              else:
                     UpperCamelCase    :	Dict   	=		nn.Sequential(*__SCREAMING_SNAKE_CASE						)
              if self.concat_input:
                     UpperCamelCase    :	Union[str, Any]   	=		UperNetConvModule(
                         self.in_channels + self.channels ,					self.channels ,					kernel_size=__SCREAMING_SNAKE_CASE ,					padding=kernel_size // 2						)

              UpperCamelCase    :	Optional[Any]   	=		nn.Convad(self.channels ,					config.num_labels ,					kernel_size=1						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.apply(self._init_weights						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




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




class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[Any]							=					UperNetConfig
       __UpperCamelCase      :     Optional[int]							=					"pixel_values"
       __UpperCamelCase      :     Dict							=					True






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """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  _lowercase   (     self						):




              """simple docstring"""




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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=False						):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	str   	=		value




__UpperCAmelCase : List[Any] 			=				r"\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

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



@add_start_docstrings(
    "UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.",     _a,     )
class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Dict   	=		AutoBackbone.from_config(config.backbone_config						)

              # Semantic segmentation head(s)
              UpperCamelCase    :	int   	=		UperNetHead(__SCREAMING_SNAKE_CASE ,					in_channels=self.backbone.channels						)
              UpperCamelCase    :	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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		return_dict if return_dict is not None else self.config.use_return_dict
              UpperCamelCase    :	str   	=		(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              UpperCamelCase    :	Tuple   	=		output_attentions if output_attentions is not None else self.config.output_attentions

              UpperCamelCase    :	Tuple   	=		self.backbone.forward_with_filtered_kwargs(
                  __SCREAMING_SNAKE_CASE ,					output_hidden_states=__SCREAMING_SNAKE_CASE ,					output_attentions=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		outputs.feature_maps

              UpperCamelCase    :	Union[str, Any]   	=		self.decode_head(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		nn.functional.interpolate(__SCREAMING_SNAKE_CASE ,					size=pixel_values.shape[2:] ,					mode='''bilinear''' ,					align_corners=__SCREAMING_SNAKE_CASE						)

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

              UpperCamelCase    :	Union[str, Any]   	=		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
                            UpperCamelCase    :	Optional[int]   	=		CrossEntropyLoss(ignore_index=self.config.loss_ignore_index						)
                            UpperCamelCase    :	Tuple   	=		loss_fct(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                            UpperCamelCase    :	Optional[Any]   	=		loss_fct(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                            UpperCamelCase    :	Union[str, Any]   	=		main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

              if not return_dict:
                     if output_hidden_states:
                            UpperCamelCase    :	Optional[Any]   	=		(logits,) + outputs[1:]
                     else:
                            UpperCamelCase    :	int   	=		(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 ,					)

