import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a_							=      logging.get_logger(__name__)




def       __lowerCAmelCase      (						A_   : str					)    ->  Union[str, Any]:
  __UpperCAmelCase						    =       SwinConfig.from_pretrained(
      "microsoft/swin-tiny-patch4-window7-224"						,	out_features=["stage1", "stage2", "stage3", "stage4"]					)
  __UpperCAmelCase						    =       MaskFormerConfig(backbone_config=A_					)

  __UpperCAmelCase						    =       "huggingface/label-files"
  if "ade20k-full" in model_name:
    # this should be ok
    __UpperCAmelCase						    =       8_47
    __UpperCAmelCase						    =       "maskformer-ade20k-full-id2label.json"
  elif "ade" in model_name:
    # this should be ok
    __UpperCAmelCase						    =       1_50
    __UpperCAmelCase						    =       "ade20k-id2label.json"
  elif "coco-stuff" in model_name:
    # this should be ok
    __UpperCAmelCase						    =       1_71
    __UpperCAmelCase						    =       "maskformer-coco-stuff-id2label.json"
  elif "coco" in model_name:
    # TODO
    __UpperCAmelCase						    =       1_33
    __UpperCAmelCase						    =       "coco-panoptic-id2label.json"
  elif "cityscapes" in model_name:
    # this should be ok
    __UpperCAmelCase						    =       19
    __UpperCAmelCase						    =       "cityscapes-id2label.json"
  elif "vistas" in model_name:
    # this should be ok
    __UpperCAmelCase						    =       65
    __UpperCAmelCase						    =       "mapillary-vistas-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()}

  return config




def       __lowerCAmelCase      (						A_   : List[str]					)    ->  Optional[int]:
  __UpperCAmelCase						    =       []
  # stem
  # fmt: off
  rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")					)
  rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")					)
  rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")					)
  rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.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.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')					)
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')					)
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')					)
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')					)
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')					)
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')					)
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')					)
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')					)
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')					)
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')					)
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')					)
      rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')					)

    if i < 3:
      rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')					)
      rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')					)
      rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')					)
    rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')					)
    rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')					)

  # FPN
  rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")					)
  rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")					)
  rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")					)
  for source_index, target_index in zip(range(3						,	0						,	-1					)						,	range(0						,	3					)					):
    rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')					)
    rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')					)
    rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')					)
    rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')					)
    rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')					)
    rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')					)
  rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")					)
  rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")					)

  # Transformer decoder
  for idx in range(config.decoder_config.decoder_layers					):
    # self-attention out projection
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')					)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')					)
    # cross-attention out projection
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')					)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')					)
    # MLP 1
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')					)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')					)
    # MLP 2
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')					)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')					)
    # layernorm 1 (self-attention layernorm)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')					)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')					)
    # layernorm 2 (cross-attention layernorm)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')					)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')					)
    # layernorm 3 (final layernorm)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')					)
    rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')					)

  rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")					)
  rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")					)

  # heads on top
  rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")					)

  rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")					)
  rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")					)

  rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")					)
  rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")					)

  for i in range(3					):
    rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')					)
    rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')					)
  # fmt: on

  return rename_keys




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




def       __lowerCAmelCase      (						A_   : str						,	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.layers.{i}.blocks.{j}.attn.qkv.weight'''					)
      __UpperCAmelCase						    =       state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.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_   : int						,	A_   : Tuple					)    ->  Dict:
  # fmt: off
  __UpperCAmelCase						    =       config.decoder_config.hidden_size
  for idx in range(config.decoder_config.decoder_layers					):
    # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
    __UpperCAmelCase						    =       state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''					)
    __UpperCAmelCase						    =       state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''					)
    # next, add query, keys and values (in that order) to the state dict
    __UpperCAmelCase						    =       in_proj_weight[: hidden_size, :]
    __UpperCAmelCase						    =       in_proj_bias[:config.hidden_size]
    __UpperCAmelCase						    =       in_proj_weight[hidden_size : hidden_size * 2, :]
    __UpperCAmelCase						    =       in_proj_bias[hidden_size : hidden_size * 2]
    __UpperCAmelCase						    =       in_proj_weight[-hidden_size :, :]
    __UpperCAmelCase						    =       in_proj_bias[-hidden_size :]
    # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
    __UpperCAmelCase						    =       state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''					)
    __UpperCAmelCase						    =       state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''					)
    # next, add query, keys and values (in that order) to the state dict
    __UpperCAmelCase						    =       in_proj_weight[: hidden_size, :]
    __UpperCAmelCase						    =       in_proj_bias[:config.hidden_size]
    __UpperCAmelCase						    =       in_proj_weight[hidden_size : hidden_size * 2, :]
    __UpperCAmelCase						    =       in_proj_bias[hidden_size : hidden_size * 2]
    __UpperCAmelCase						    =       in_proj_weight[-hidden_size :, :]
    __UpperCAmelCase						    =       in_proj_bias[-hidden_size :]
  # fmt: on




def       __lowerCAmelCase      (						)    ->  torch.Tensor:
  __UpperCAmelCase						    =       "http://images.cocodataset.org/val2017/000000039769.jpg"
  __UpperCAmelCase						    =       Image.open(requests.get(A_						,	stream=A_					).raw					)
  return im




@torch.no_grad()
def       __lowerCAmelCase      (						A_   : str						,	A_   : str						,	A_   : str						,	A_   : bool = False					)    ->  Optional[int]:
  __UpperCAmelCase						    =       get_maskformer_config(A_					)

  # load original state_dict
  with open(A_						,	"rb"					) as f:
    __UpperCAmelCase						    =       pickle.load(A_					)
  __UpperCAmelCase						    =       data["model"]

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

  # rename keys
  __UpperCAmelCase						    =       create_rename_keys(A_					)
  for src, dest in rename_keys:
    rename_key(A_						,	A_						,	A_					)
  read_in_swin_q_k_v(A_						,	config.backbone_config					)
  read_in_decoder_q_k_v(A_						,	A_					)

  # update to torch tensors
  for key, value in state_dict.items():
    __UpperCAmelCase						    =       torch.from_numpy(A_					)

  # load 🤗 model
  __UpperCAmelCase						    =       MaskFormerForInstanceSegmentation(A_					)
  model.eval()

  for name, param in model.named_parameters():
    print(A_						,	param.shape					)

  __UpperCAmelCase    ,		__UpperCAmelCase						    =       model.load_state_dict(A_						,	strict=A_					)
  assert missing_keys == [
      "model.pixel_level_module.encoder.model.layernorm.weight",
      "model.pixel_level_module.encoder.model.layernorm.bias",
  ]
  assert len(A_					) == 0, F'''Unexpected keys: {unexpected_keys}'''

  # verify results
  __UpperCAmelCase						    =       prepare_img()
  if "vistas" in model_name:
    __UpperCAmelCase						    =       65
  elif "cityscapes" in model_name:
    __UpperCAmelCase						    =       6_55_35
  else:
    __UpperCAmelCase						    =       2_55
  __UpperCAmelCase						    =       True if "ade" in model_name else False
  __UpperCAmelCase						    =       MaskFormerImageProcessor(ignore_index=A_						,	reduce_labels=A_					)

  __UpperCAmelCase						    =       image_processor(A_						,	return_tensors="pt"					)

  __UpperCAmelCase						    =       model(**A_					)

  print("Logits:"						,	outputs.class_queries_logits[0, :3, :3]					)

  if model_name == "maskformer-swin-tiny-ade":
    __UpperCAmelCase						    =       torch.tensor(
        [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]					)
  assert torch.allclose(outputs.class_queries_logits[0, :3, :3]						,	A_						,	atol=1e-4					)
  print("Looks ok!"					)

  if pytorch_dump_folder_path is not None:
    print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''					)
    Path(A_					).mkdir(exist_ok=A_					)
    model.save_pretrained(A_					)
    image_processor.save_pretrained(A_					)

  if push_to_hub:
    print("Pushing model and image processor to the hub..."					)
    model.push_to_hub(F'''nielsr/{model_name}'''					)
    image_processor.push_to_hub(F'''nielsr/{model_name}'''					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--model_name""",
           default="""maskformer-swin-tiny-ade""",
           type=str,
           help=("""Name of the MaskFormer model you'd like to convert""",),
       )
       parser.add_argument(
           """--checkpoint_path""",
           default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
           type=str,
           help="""Path to the original state dict (.pth file).""",
       )
       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_maskformer_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )




def       __lowerCAmelCase      (						A_   : str					)    ->  bool:
  if not all(x.isalpha() for x in string					):
    raise ValueError("String must only contain alphabetic characters."					)

  __UpperCAmelCase						    =       sorted(string.lower()					)
  return len(A_					) == len(set(A_					)					)


if __name__ == "__main__":
       a_							=      input("""Enter a string """).strip()

       a_							=      is_isogram(input_str)
       print(F"{input_str} is {'an' if isogram else 'not an'} isogram.")



