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    __lowercase     (						lowerCamelCase					:     Any	):
 UpperCamelCase_					:						str =					384
 UpperCamelCase_					:						Tuple =					7
 if "tiny" in model_name:
  UpperCamelCase_					:						Any =					96
  UpperCamelCase_					:						Dict =					(2, 2, 6, 2)
  UpperCamelCase_					:						Optional[Any] =					(3, 6, 12, 24)
 elif "small" in model_name:
  UpperCamelCase_					:						List[Any] =					96
  UpperCamelCase_					:						Tuple =					(2, 2, 18, 2)
  UpperCamelCase_					:						List[str] =					(3, 6, 12, 24)
 elif "base" in model_name:
  UpperCamelCase_					:						Union[str, Any] =					128
  UpperCamelCase_					:						int =					(2, 2, 18, 2)
  UpperCamelCase_					:						List[Any] =					(4, 8, 16, 32)
  UpperCamelCase_					:						Tuple =					12
  UpperCamelCase_					:						Any =					512
 elif "large" in model_name:
  UpperCamelCase_					:						List[Any] =					192
  UpperCamelCase_					:						Optional[int] =					(2, 2, 18, 2)
  UpperCamelCase_					:						Dict =					(6, 12, 24, 48)
  UpperCamelCase_					:						int =					12
  UpperCamelCase_					:						str =					768

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

 UpperCamelCase_					:						Dict =					SwinConfig(
     embed_dim=SCREAMING_SNAKE_CASE__  ,  depths=SCREAMING_SNAKE_CASE__  ,  num_heads=SCREAMING_SNAKE_CASE__  ,  window_size=SCREAMING_SNAKE_CASE__  ,  out_features=['stage1', 'stage2', 'stage3', 'stage4']  ,  )
 UpperCamelCase_					:						Union[str, Any] =					UperNetConfig(
     backbone_config=SCREAMING_SNAKE_CASE__  ,  auxiliary_in_channels=SCREAMING_SNAKE_CASE__  ,  num_labels=SCREAMING_SNAKE_CASE__  ,  idalabel=SCREAMING_SNAKE_CASE__  ,  labelaid=SCREAMING_SNAKE_CASE__  ,  )

 return config





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						List[str] =					[]

 # 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    __lowercase     (						lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     List[str]	):
 UpperCamelCase_					:						Dict =					dct.pop(SCREAMING_SNAKE_CASE__	)
 UpperCamelCase_					:						List[str] =					val





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     List[str]	):
 UpperCamelCase_					:						str =					[int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
 for i in range(len(backbone_config.depths	)	):
  UpperCamelCase_					:						List[Any] =					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_					:						List[Any] =					state_dict.pop(F"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"	)
   UpperCamelCase_					:						Optional[int] =					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_					:						str =					in_proj_weight[:dim, :]
   UpperCamelCase_					:						Optional[Any] =					in_proj_bias[: dim]
   UpperCamelCase_					:						int =					in_proj_weight[
       dim : dim * 2, :
   ]
   UpperCamelCase_					:						Dict =					in_proj_bias[
       dim : dim * 2
   ]
   UpperCamelCase_					:						Optional[int] =					in_proj_weight[
       -dim :, :
   ]
   UpperCamelCase_					:						Union[str, Any] =					in_proj_bias[-dim :]
   # fmt: on





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_, UpperCamelCase_					:						Optional[Any] =					x.shape
 UpperCamelCase_					:						Union[str, Any] =					x.reshape(SCREAMING_SNAKE_CASE__  ,  4  ,  in_channel // 4	)
 UpperCamelCase_					:						Union[str, Any] =					x[:, [0, 2, 1, 3], :].transpose(1  ,  2	).reshape(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	)
 return x





def    __lowercase     (						lowerCamelCase					:     Tuple	):
 UpperCamelCase_, UpperCamelCase_					:						Any =					x.shape
 UpperCamelCase_					:						str =					x.reshape(SCREAMING_SNAKE_CASE__  ,  in_channel // 4  ,  4	)
 UpperCamelCase_					:						str =					x[:, :, [0, 2, 1, 3]].transpose(1  ,  2	).reshape(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	)

 return x





def    __lowercase     (						lowerCamelCase					:     Any	):
 UpperCamelCase_					:						str =					x.shape[0]
 UpperCamelCase_					:						Tuple =					x.reshape(4  ,  in_channel // 4	)
 UpperCamelCase_					:						Tuple =					x[[0, 2, 1, 3], :].transpose(0  ,  1	).reshape(SCREAMING_SNAKE_CASE__	)
 return x





def    __lowercase     (						lowerCamelCase					:     Any	):
 UpperCamelCase_					:						Optional[int] =					x.shape[0]
 UpperCamelCase_					:						Any =					x.reshape(in_channel // 4  ,  4	)
 UpperCamelCase_					:						List[Any] =					x[:, [0, 2, 1, 3]].transpose(0  ,  1	).reshape(SCREAMING_SNAKE_CASE__	)
 return x





def    __lowercase     (						lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Optional[int]	):
 UpperCamelCase_					:						Optional[Any] =					{
     '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_					:						Tuple =					model_name_to_url[model_name]
 UpperCamelCase_					:						Any =					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__  ,  map_location='cpu'  ,  file_name=SCREAMING_SNAKE_CASE__	)[
     'state_dict'
 ]

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

 UpperCamelCase_					:						int =					get_upernet_config(SCREAMING_SNAKE_CASE__	)
 UpperCamelCase_					:						Any =					UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE__	)
 model.eval()

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

 # rename keys
 UpperCamelCase_					:						Dict =					create_rename_keys(SCREAMING_SNAKE_CASE__	)
 for src, dest in rename_keys:
  rename_key(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	)
 read_in_q_k_v(SCREAMING_SNAKE_CASE__  ,  config.backbone_config	)

 # fix downsample parameters
 for key, value in state_dict.items():
  if "downsample" in key:
   if "reduction" in key:
    UpperCamelCase_					:						List[Any] =					reverse_correct_unfold_reduction_order(SCREAMING_SNAKE_CASE__	)
   if "norm" in key:
    UpperCamelCase_					:						int =					reverse_correct_unfold_norm_order(SCREAMING_SNAKE_CASE__	)

 model.load_state_dict(SCREAMING_SNAKE_CASE__	)

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

 UpperCamelCase_					:						Union[str, Any] =					SegformerImageProcessor()
 UpperCamelCase_					:						Union[str, Any] =					processor(SCREAMING_SNAKE_CASE__  ,  return_tensors='pt'	).pixel_values

 with torch.no_grad():
  UpperCamelCase_					:						Optional[Any] =					model(SCREAMING_SNAKE_CASE__	)
  UpperCamelCase_					:						Tuple =					outputs.logits

 print(logits.shape	)
 print('First values of logits:'  ,  logits[0, 0, :3, :3]	)
 # assert values
 if model_name == "upernet-swin-tiny":
  UpperCamelCase_					:						Tuple =					torch.tensor(
      [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]	)
 elif model_name == "upernet-swin-small":
  UpperCamelCase_					:						str =					torch.tensor(
      [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]	)
 elif model_name == "upernet-swin-base":
  UpperCamelCase_					:						Optional[Any] =					torch.tensor(
      [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]	)
 elif model_name == "upernet-swin-large":
  UpperCamelCase_					:						Optional[Any] =					torch.tensor(
      [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]	)
 print('Logits:'  ,  outputs.logits[0, 0, :3, :3]	)
 assert torch.allclose(outputs.logits[0, 0, :3, :3]  ,  SCREAMING_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(SCREAMING_SNAKE_CASE__	)
  print(F"Saving processor to {pytorch_dump_folder_path}"	)
  processor.save_pretrained(SCREAMING_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__":
   a_    					=       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.'
   )

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'open-llama'


    def __init__(      self :       Any,lowercase_ :       Optional[int]=1_0_0_0_0_0,lowercase_ :       Union[str, Any]=4_0_9_6,lowercase_ :       Dict=1_1_0_0_8,lowercase_ :       Dict=3_2,lowercase_ :       Optional[int]=3_2,lowercase_ :       Dict="silu",lowercase_ :       Union[str, Any]=2_0_4_8,lowercase_ :       Optional[int]=0.02,lowercase_ :       Dict=1E-6,lowercase_ :       Dict=True,lowercase_ :       List[Any]=0,lowercase_ :       Optional[int]=1,lowercase_ :       str=2,lowercase_ :       str=False,lowercase_ :       str=True,lowercase_ :       int=0.1,lowercase_ :       List[Any]=0.1,lowercase_ :       List[Any]=True,lowercase_ :       Union[str, Any]=True,lowercase_ :       Any=None,**lowercase_ :       List[Any],)->    Tuple:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   max_position_embeddings
        A__          =   hidden_size
        A__          =   intermediate_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   hidden_act
        A__          =   initializer_range
        A__          =   rms_norm_eps
        A__          =   use_cache
        A__          =   kwargs.pop(
            'use_memorry_efficient_attention',lowercase_		)
        A__          =   hidden_dropout_prob
        A__          =   attention_dropout_prob
        A__          =   use_stable_embedding
        A__          =   shared_input_output_embedding
        A__          =   rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,tie_word_embeddings=lowercase_,**lowercase_,)


    def 						snake_case__     (      self :       str		)->    str:





        '''simple docstring'''







        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling,lowercase_		) or len(self.rope_scaling		) != 2:
            raise ValueError(
                '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
                F'got {self.rope_scaling}'		)
        A__          =   self.rope_scaling.get('type',lowercase_		)
        A__          =   self.rope_scaling.get('factor',lowercase_		)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
            raise ValueError(
                F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
        if rope_scaling_factor is None or not isinstance(lowercase_,lowercase_		) or rope_scaling_factor <= 1.0:
            raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)

