from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


a_							=      {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""BeitFeatureExtractor"""]
       a_							=      ["""BeitImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """BeitForImageClassification""",
           """BeitForMaskedImageModeling""",
           """BeitForSemanticSegmentation""",
           """BeitModel""",
           """BeitPreTrainedModel""",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """FlaxBeitForImageClassification""",
           """FlaxBeitForMaskedImageModeling""",
           """FlaxBeitModel""",
           """FlaxBeitPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_beit import BeitFeatureExtractor
              from .image_processing_beit import BeitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_beit import (
                  BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BeitForImageClassification,
                  BeitForMaskedImageModeling,
                  BeitForSemanticSegmentation,
                  BeitModel,
                  BeitPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_beit import (
                  FlaxBeitForImageClassification,
                  FlaxBeitForMaskedImageModeling,
                  FlaxBeitModel,
                  FlaxBeitPreTrainedModel,
              )


else:
       import sys

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




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      (						A_   : Tuple					)    ->  Any:
  __UpperCAmelCase						    =       3_84
  __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						    =       1_28
    __UpperCAmelCase						    =       (2, 2, 18, 2)
    __UpperCAmelCase						    =       (4, 8, 16, 32)
    __UpperCAmelCase						    =       12
    __UpperCAmelCase						    =       5_12
  elif "large" in model_name:
    __UpperCAmelCase						    =       1_92
    __UpperCAmelCase						    =       (2, 2, 18, 2)
    __UpperCAmelCase						    =       (6, 12, 24, 48)
    __UpperCAmelCase						    =       12
    __UpperCAmelCase						    =       7_68

  # set label information
  __UpperCAmelCase						    =       1_50
  __UpperCAmelCase						    =       "huggingface/label-files"
  __UpperCAmelCase						    =       "ade20k-id2label.json"
  __UpperCAmelCase						    =       json.load(open(hf_hub_download(A_						,	A_						,	repo_type="dataset"					)						,	"r"					)					)
  __UpperCAmelCase						    =       {int(A_					): v for k, v in idalabel.items()}
  __UpperCAmelCase						    =       {v: k for k, v in idalabel.items()}

  __UpperCAmelCase						    =       SwinConfig(
      embed_dim=A_						,	depths=A_						,	num_heads=A_						,	window_size=A_						,	out_features=["stage1", "stage2", "stage3", "stage4"]						,	)
  __UpperCAmelCase						    =       UperNetConfig(
      backbone_config=A_						,	auxiliary_in_channels=A_						,	num_labels=A_						,	idalabel=A_						,	labelaid=A_						,	)

  return config




def       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  Optional[Any]:
  __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      (						A_   : List[Any]						,	A_   : List[str]						,	A_   : Optional[Any]					)    ->  List[str]:
  __UpperCAmelCase						    =       dct.pop(A_					)
  __UpperCAmelCase						    =       val




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Dict					)    ->  Tuple:
  __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      (						A_   : List[Any]					)    ->  int:
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       x.shape
  __UpperCAmelCase						    =       x.reshape(A_						,	4						,	in_channel // 4					)
  __UpperCAmelCase						    =       x[:, [0, 2, 1, 3], :].transpose(1						,	2					).reshape(A_						,	A_					)
  return x




def       __lowerCAmelCase      (						A_   : Tuple					)    ->  int:
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       x.shape
  __UpperCAmelCase						    =       x.reshape(A_						,	in_channel // 4						,	4					)
  __UpperCAmelCase						    =       x[:, :, [0, 2, 1, 3]].transpose(1						,	2					).reshape(A_						,	A_					)

  return x




def       __lowerCAmelCase      (						A_   : Union[str, Any]					)    ->  str:
  __UpperCAmelCase						    =       x.shape[0]
  __UpperCAmelCase						    =       x.reshape(4						,	in_channel // 4					)
  __UpperCAmelCase						    =       x[[0, 2, 1, 3], :].transpose(0						,	1					).reshape(A_					)
  return x




def       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  str:
  __UpperCAmelCase						    =       x.shape[0]
  __UpperCAmelCase						    =       x.reshape(in_channel // 4						,	4					)
  __UpperCAmelCase						    =       x[:, [0, 2, 1, 3]].transpose(0						,	1					).reshape(A_					)
  return x




def       __lowerCAmelCase      (						A_   : Any						,	A_   : str						,	A_   : Any					)    ->  Dict:
  __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(A_						,	map_location="cpu"						,	file_name=A_					)[
      "state_dict"
  ]

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

  __UpperCAmelCase						    =       get_upernet_config(A_					)
  __UpperCAmelCase						    =       UperNetForSemanticSegmentation(A_					)
  model.eval()

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

  # rename keys
  __UpperCAmelCase						    =       create_rename_keys(A_					)
  for src, dest in rename_keys:
    rename_key(A_						,	A_						,	A_					)
  read_in_q_k_v(A_						,	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(A_					)
      if "norm" in key:
        __UpperCAmelCase						    =       reverse_correct_unfold_norm_order(A_					)

  model.load_state_dict(A_					)

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

  __UpperCAmelCase						    =       SegformerImageProcessor()
  __UpperCAmelCase						    =       processor(A_						,	return_tensors="pt"					).pixel_values

  with torch.no_grad():
    __UpperCAmelCase						    =       model(A_					)
    __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(
        [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]					)
  elif model_name == "upernet-swin-small":
    __UpperCAmelCase						    =       torch.tensor(
        [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]					)
  elif model_name == "upernet-swin-base":
    __UpperCAmelCase						    =       torch.tensor(
        [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]					)
  elif model_name == "upernet-swin-large":
    __UpperCAmelCase						    =       torch.tensor(
        [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]					)
  print("Logits:"						,	outputs.logits[0, 0, :3, :3]					)
  assert torch.allclose(outputs.logits[0, 0, :3, :3]						,	A_						,	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(A_					)
    print(F'''Saving processor to {pytorch_dump_folder_path}'''					)
    processor.save_pretrained(A_					)

  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)



