
import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation

def  __lowerCamelCase					( _lowercase			)       ->      Any:
     UpperCamelCase						    =					384
     UpperCamelCase						    =					7
     if "tiny" in model_name:
          UpperCamelCase						    =					96
          UpperCamelCase						    =					(2, 2, 6, 2)
          UpperCamelCase						    =					(3, 6, 12, 24)
     elif "small" in model_name:
          UpperCamelCase						    =					96
          UpperCamelCase						    =					(2, 2, 18, 2)
          UpperCamelCase						    =					(3, 6, 12, 24)
     elif "base" in model_name:
          UpperCamelCase						    =					128
          UpperCamelCase						    =					(2, 2, 18, 2)
          UpperCamelCase						    =					(4, 8, 16, 32)
          UpperCamelCase						    =					12
          UpperCamelCase						    =					512
     elif "large" in model_name:
          UpperCamelCase						    =					192
          UpperCamelCase						    =					(2, 2, 18, 2)
          UpperCamelCase						    =					(6, 12, 24, 48)
          UpperCamelCase						    =					12
          UpperCamelCase						    =					768

     # set label information
     UpperCamelCase						    =					150
     UpperCamelCase						    =					'huggingface/label-files'
     UpperCamelCase						    =					'ade20k-id2label.json'
     UpperCamelCase						    =					json.load(open(hf_hub_download(_lowercase						,     _lowercase						,     repo_type='dataset'			)						,     'r'			)			)
     UpperCamelCase						    =					{int(_lowercase			): v for k, v in idalabel.items()}
     UpperCamelCase						    =					{v: k for k, v in idalabel.items()}

     UpperCamelCase						    =					SwinConfig(
         embed_dim=_lowercase						,     depths=_lowercase						,     num_heads=_lowercase						,     window_size=_lowercase						,     out_features=['stage1', 'stage2', 'stage3', 'stage4']						,     )
     UpperCamelCase						    =					UperNetConfig(
         backbone_config=_lowercase						,     auxiliary_in_channels=_lowercase						,     num_labels=_lowercase						,     idalabel=_lowercase						,     labelaid=_lowercase						,     )

     return config

def  __lowerCamelCase					( _lowercase			)       ->      List[str]:
     UpperCamelCase						    =					[]

     # fmt: off
     # stem
     rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')			)
     rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')			)
     rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')			)
     rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.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}.blocks.{j}.norm1.weight', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')			)
               rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm1.bias', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')			)
               rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table', F'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')			)
               rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index', F'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')			)
               rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight', F'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')			)
               rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias', F'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')			)
               rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm2.weight', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')			)
               rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm2.bias', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')			)
               rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight', F'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')			)
               rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias', F'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')			)
               rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight', F'backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight')			)
               rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias', F'backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias')			)

          if i < 3:
               rename_keys.append((F'backbone.stages.{i}.downsample.reduction.weight', F'backbone.encoder.layers.{i}.downsample.reduction.weight')			)
               rename_keys.append((F'backbone.stages.{i}.downsample.norm.weight', F'backbone.encoder.layers.{i}.downsample.norm.weight')			)
               rename_keys.append((F'backbone.stages.{i}.downsample.norm.bias', F'backbone.encoder.layers.{i}.downsample.norm.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  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase			)       ->      Tuple:
     UpperCamelCase						    =					dct.pop(_lowercase			)
     UpperCamelCase						    =					val

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Dict:
     UpperCamelCase						    =					[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
     for i in range(len(backbone_config.depths			)			):
          UpperCamelCase						    =					num_features[i]
          for j in range(backbone_config.depths[i]			):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               UpperCamelCase						    =					state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'			)
               UpperCamelCase						    =					state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'			)
               # next, add query, keys and values (in that order) to the state dict
               UpperCamelCase						    =					in_proj_weight[:dim, :]
               UpperCamelCase						    =					in_proj_bias[: dim]
               UpperCamelCase						    =					in_proj_weight[
                   dim : dim * 2, :
               ]
               UpperCamelCase						    =					in_proj_bias[
                   dim : dim * 2
               ]
               UpperCamelCase						    =					in_proj_weight[
                   -dim :, :
               ]
               UpperCamelCase						    =					in_proj_bias[-dim :]
               # fmt: on

def  __lowerCamelCase					( _lowercase			)       ->      Optional[int]:
     UpperCamelCase    ,		UpperCamelCase						    =					x.shape
     UpperCamelCase						    =					x.reshape(_lowercase						,     4						,     in_channel // 4			)
     UpperCamelCase						    =					x[:, [0, 2, 1, 3], :].transpose(1						,     2			).reshape(_lowercase						,     _lowercase			)
     return x

def  __lowerCamelCase					( _lowercase			)       ->      Dict:
     UpperCamelCase    ,		UpperCamelCase						    =					x.shape
     UpperCamelCase						    =					x.reshape(_lowercase						,     in_channel // 4						,     4			)
     UpperCamelCase						    =					x[:, :, [0, 2, 1, 3]].transpose(1						,     2			).reshape(_lowercase						,     _lowercase			)

     return x

def  __lowerCamelCase					( _lowercase			)       ->      List[Any]:
     UpperCamelCase						    =					x.shape[0]
     UpperCamelCase						    =					x.reshape(4						,     in_channel // 4			)
     UpperCamelCase						    =					x[[0, 2, 1, 3], :].transpose(0						,     1			).reshape(_lowercase			)
     return x

def  __lowerCamelCase					( _lowercase			)       ->      int:
     UpperCamelCase						    =					x.shape[0]
     UpperCamelCase						    =					x.reshape(in_channel // 4						,     4			)
     UpperCamelCase						    =					x[:, [0, 2, 1, 3]].transpose(0						,     1			).reshape(_lowercase			)
     return x

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase			)       ->      int:
     UpperCamelCase						    =					{
         'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
         'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
         'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
         'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
     }
     UpperCamelCase						    =					model_name_to_url[model_name]
     UpperCamelCase						    =					torch.hub.load_state_dict_from_url(_lowercase						,     map_location='cpu'						,     file_name=_lowercase			)[
         'state_dict'
     ]

     for name, param in state_dict.items():
          print(_lowercase						,     param.shape			)

     UpperCamelCase						    =					get_upernet_config(_lowercase			)
     UpperCamelCase						    =					UperNetForSemanticSegmentation(_lowercase			)
     model.eval()

     # replace "bn" => "batch_norm"
     for key in state_dict.copy().keys():
          UpperCamelCase						    =					state_dict.pop(_lowercase			)
          if "bn" in key:
               UpperCamelCase						    =					key.replace('bn'						,     'batch_norm'			)
          UpperCamelCase						    =					val

     # rename keys
     UpperCamelCase						    =					create_rename_keys(_lowercase			)
     for src, dest in rename_keys:
          rename_key(_lowercase						,     _lowercase						,     _lowercase			)
     read_in_q_k_v(_lowercase						,     config.backbone_config			)

     # fix downsample parameters
     for key, value in state_dict.items():
          if "downsample" in key:
               if "reduction" in key:
                    UpperCamelCase						    =					reverse_correct_unfold_reduction_order(_lowercase			)
               if "norm" in key:
                    UpperCamelCase						    =					reverse_correct_unfold_norm_order(_lowercase			)

     model.load_state_dict(_lowercase			)

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

     UpperCamelCase						    =					SegformerImageProcessor()
     UpperCamelCase						    =					processor(_lowercase						,     return_tensors='pt'			).pixel_values

     with torch.no_grad():
          UpperCamelCase						    =					model(_lowercase			)
          UpperCamelCase						    =					outputs.logits

     print(logits.shape			)
     print('First values of logits:'						,     logits[0, 0, :3, :3]			)
     # assert values
     if model_name == "upernet-swin-tiny":
          UpperCamelCase						    =					torch.tensor(
              [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]			)
     elif model_name == "upernet-swin-small":
          UpperCamelCase						    =					torch.tensor(
              [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]			)
     elif model_name == "upernet-swin-base":
          UpperCamelCase						    =					torch.tensor(
              [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]			)
     elif model_name == "upernet-swin-large":
          UpperCamelCase						    =					torch.tensor(
              [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]			)
     print('Logits:'						,     outputs.logits[0, 0, :3, :3]			)
     assert torch.allclose(outputs.logits[0, 0, :3, :3]						,     _lowercase						,     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(_lowercase			)
          print(F'Saving processor to {pytorch_dump_folder_path}'			)
          processor.save_pretrained(_lowercase			)

     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__":
 _snake_case      = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''upernet-swin-tiny''',
     type=str,
     choices=[F"upernet-swin-{size}" for size in ['''tiny''', '''small''', '''base''', '''large''']],
     help='''Name of the Swin + 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.'''
 )

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




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case      = logging.get_logger(__name__)

_snake_case      = {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Dict       						="roberta"

 def __init__(						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Tuple=5_02_65					, SCREAMING_SNAKE_CASE__			:			List[str]=7_68					, SCREAMING_SNAKE_CASE__			:			List[str]=12					, SCREAMING_SNAKE_CASE__			:			Any=12					, SCREAMING_SNAKE_CASE__			:			Tuple=30_72					, SCREAMING_SNAKE_CASE__			:			str="gelu"					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0.1					, SCREAMING_SNAKE_CASE__			:			int=0.1					, SCREAMING_SNAKE_CASE__			:			int=5_12					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=2					, SCREAMING_SNAKE_CASE__			:			Dict=0.02					, SCREAMING_SNAKE_CASE__			:			List[str]=1e-12					, SCREAMING_SNAKE_CASE__			:			Any=1					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=0					, SCREAMING_SNAKE_CASE__			:			str=2					, SCREAMING_SNAKE_CASE__			:			Any="absolute"					, SCREAMING_SNAKE_CASE__			:			Any=True					, SCREAMING_SNAKE_CASE__			:			List[Any]=None					, **SCREAMING_SNAKE_CASE__			:			Optional[int]					, ):






      """simple docstring"""



      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__					, bos_token_id=SCREAMING_SNAKE_CASE__					, eos_token_id=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					vocab_size
      UpperCamelCase						    =					hidden_size
      UpperCamelCase						    =					num_hidden_layers
      UpperCamelCase						    =					num_attention_heads
      UpperCamelCase						    =					hidden_act
      UpperCamelCase						    =					intermediate_size
      UpperCamelCase						    =					hidden_dropout_prob
      UpperCamelCase						    =					attention_probs_dropout_prob
      UpperCamelCase						    =					max_position_embeddings
      UpperCamelCase						    =					type_vocab_size
      UpperCamelCase						    =					initializer_range
      UpperCamelCase						    =					layer_norm_eps
      UpperCamelCase						    =					position_embedding_type
      UpperCamelCase						    =					use_cache
      UpperCamelCase						    =					classifier_dropout




class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      if self.task == "multiple-choice":
           UpperCamelCase						    =					{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
           UpperCamelCase						    =					{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]   )


