



'''simple docstring'''





import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification





def 		__snake_case(							_lowerCAmelCase						)						->					Any:
      snake_case__    :       List[Any]       		= SwinvaConfig()
      snake_case__    :       Union[str, Any]       		= swinva_name.split("""_"""						)

      snake_case__    :       Dict       		= name_split[1]
      if "to" in name_split[3]:
            snake_case__    :       List[str]       		= int(name_split[3][-3:]						)
      else:
            snake_case__    :       Optional[Any]       		= int(name_split[3]						)
      if "to" in name_split[2]:
            snake_case__    :       Union[str, Any]       		= int(name_split[2][-2:]						)
      else:
            snake_case__    :       List[str]       		= int(name_split[2][6:]						)

      if model_size == "tiny":
            snake_case__    :       List[str]       		= 96
            snake_case__    :       Union[str, Any]       		= (2, 2, 6, 2)
            snake_case__    :       List[Any]       		= (3, 6, 12, 24)
      elif model_size == "small":
            snake_case__    :       Optional[int]       		= 96
            snake_case__    :       List[str]       		= (2, 2, 18, 2)
            snake_case__    :       int       		= (3, 6, 12, 24)
      elif model_size == "base":
            snake_case__    :       Optional[int]       		= 128
            snake_case__    :       Optional[Any]       		= (2, 2, 18, 2)
            snake_case__    :       int       		= (4, 8, 16, 32)
      else:
            snake_case__    :       Optional[Any]       		= 192
            snake_case__    :       Tuple       		= (2, 2, 18, 2)
            snake_case__    :       List[Any]       		= (6, 12, 24, 48)

      if "to" in swinva_name:
            snake_case__    :       List[Any]       		= (12, 12, 12, 6)

      if ("22k" in swinva_name) and ("to" not in swinva_name):
            snake_case__    :       str       		= 21_841
            snake_case__    :       Dict       		= """huggingface/label-files"""
            snake_case__    :       Optional[Any]       		= """imagenet-22k-id2label.json"""
            snake_case__    :       str       		= json.load(open(hf_hub_download(_lowerCAmelCase	,  _lowerCAmelCase	,  repo_type="""dataset"""						)	,  """r"""						)						)
            snake_case__    :       Union[str, Any]       		= {int(_lowerCAmelCase						): v for k, v in idalabel.items()}
            snake_case__    :       Any       		= idalabel
            snake_case__    :       Union[str, Any]       		= {v: k for k, v in idalabel.items()}

      else:
            snake_case__    :       List[Any]       		= 1_000
            snake_case__    :       Union[str, Any]       		= """huggingface/label-files"""
            snake_case__    :       str       		= """imagenet-1k-id2label.json"""
            snake_case__    :       Dict       		= json.load(open(hf_hub_download(_lowerCAmelCase	,  _lowerCAmelCase	,  repo_type="""dataset"""						)	,  """r"""						)						)
            snake_case__    :       Union[str, Any]       		= {int(_lowerCAmelCase						): v for k, v in idalabel.items()}
            snake_case__    :       Any       		= idalabel
            snake_case__    :       List[Any]       		= {v: k for k, v in idalabel.items()}

      snake_case__    :       Any       		= img_size
      snake_case__    :       Any       		= num_classes
      snake_case__    :       str       		= embed_dim
      snake_case__    :       List[str]       		= depths
      snake_case__    :       Union[str, Any]       		= num_heads
      snake_case__    :       Any       		= window_size

      return config





def 		__snake_case(							_lowerCAmelCase						)						->					Tuple:
      if "patch_embed.proj" in name:
            snake_case__    :       Optional[Any]       		= name.replace("""patch_embed.proj"""	,  """embeddings.patch_embeddings.projection"""						)
      if "patch_embed.norm" in name:
            snake_case__    :       List[str]       		= name.replace("""patch_embed.norm"""	,  """embeddings.norm"""						)
      if "layers" in name:
            snake_case__    :       Any       		= """encoder.""" + name
      if "attn.proj" in name:
            snake_case__    :       Any       		= name.replace("""attn.proj"""	,  """attention.output.dense"""						)
      if "attn" in name:
            snake_case__    :       Optional[Any]       		= name.replace("""attn"""	,  """attention.self"""						)
      if "norm1" in name:
            snake_case__    :       List[str]       		= name.replace("""norm1"""	,  """layernorm_before"""						)
      if "norm2" in name:
            snake_case__    :       List[Any]       		= name.replace("""norm2"""	,  """layernorm_after"""						)
      if "mlp.fc1" in name:
            snake_case__    :       Tuple       		= name.replace("""mlp.fc1"""	,  """intermediate.dense"""						)
      if "mlp.fc2" in name:
            snake_case__    :       Any       		= name.replace("""mlp.fc2"""	,  """output.dense"""						)
      if "q_bias" in name:
            snake_case__    :       Dict       		= name.replace("""q_bias"""	,  """query.bias"""						)
      if "k_bias" in name:
            snake_case__    :       Optional[Any]       		= name.replace("""k_bias"""	,  """key.bias"""						)
      if "v_bias" in name:
            snake_case__    :       Optional[int]       		= name.replace("""v_bias"""	,  """value.bias"""						)
      if "cpb_mlp" in name:
            snake_case__    :       Any       		= name.replace("""cpb_mlp"""	,  """continuous_position_bias_mlp"""						)
      if name == "norm.weight":
            snake_case__    :       List[Any]       		= """layernorm.weight"""
      if name == "norm.bias":
            snake_case__    :       Optional[Any]       		= """layernorm.bias"""

      if "head" in name:
            snake_case__    :       List[str]       		= name.replace("""head"""	,  """classifier"""						)
      else:
            snake_case__    :       List[Any]       		= """swinv2.""" + name

      return name





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					Union[str, Any]:
      for key in orig_state_dict.copy().keys():
            snake_case__    :       Tuple       		= orig_state_dict.pop(_lowerCAmelCase						)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  snake_case__    :       List[str]       		= key.split("""."""						)
                  snake_case__    :       str       		= int(key_split[1]						)
                  snake_case__    :       Tuple       		= int(key_split[3]						)
                  snake_case__    :       str       		= model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        snake_case__    :       Optional[int]       		= val[:dim, :]
                        snake_case__    :       List[Any]       		= val[dim : dim * 2, :]
                        snake_case__    :       str       		= val[-dim:, :]
                  else:
                        snake_case__    :       List[str]       		= val[:dim]
                        snake_case__    :       int       		= val[
                            dim : dim * 2
                        ]
                        snake_case__    :       str       		= val[-dim:]
            else:
                  snake_case__    :       List[Any]       		= val

      return orig_state_dict





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      snake_case__    :       Any       		= timm.create_model(_lowerCAmelCase	,  pretrained=_lowerCAmelCase						)
      timm_model.eval()

      snake_case__    :       Optional[Any]       		= get_swinva_config(_lowerCAmelCase						)
      snake_case__    :       Dict       		= SwinvaForImageClassification(_lowerCAmelCase						)
      model.eval()

      snake_case__    :       Optional[Any]       		= convert_state_dict(timm_model.state_dict()	,  _lowerCAmelCase						)
      model.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[str]       		= """http://images.cocodataset.org/val2017/000000039769.jpg"""

      snake_case__    :       Tuple       		= AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""	,  """-"""						)						)						)
      snake_case__    :       List[Any]       		= Image.open(requests.get(_lowerCAmelCase	,  stream=_lowerCAmelCase						).raw						)
      snake_case__    :       Dict       		= image_processor(images=_lowerCAmelCase	,  return_tensors="""pt"""						)

      snake_case__    :       Any       		= timm_model(inputs["""pixel_values"""]						)
      snake_case__    :       Optional[int]       		= model(**_lowerCAmelCase						).logits

      assert torch.allclose(_lowerCAmelCase	,  _lowerCAmelCase	,  atol=1e-3						)

      print(f"Saving model {swinva_name} to {pytorch_dump_folder_path}"						)
      model.save_pretrained(_lowerCAmelCase						)

      print(f"Saving image processor to {pytorch_dump_folder_path}"						)
      image_processor.save_pretrained(_lowerCAmelCase						)

      model.push_to_hub(
          repo_path_or_name=Path(_lowerCAmelCase	,  _lowerCAmelCase						)	,  organization="""nandwalritik"""	,  commit_message="""Add model"""	,  )


if __name__ == "__main__":
       __a  			=						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--swinv2_name",
           default="swinv2_tiny_patch4_window8_256",
           type=str,
           help="Name of the Swinv2 timm 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."
       )

       __a  			=						parser.parse_args()
       convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





'''simple docstring'''





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      snake_case__    :       Tuple       		= OmegaConf.load(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= torch.load(_lowerCAmelCase	,  map_location="""cpu"""						)["""model"""]
      snake_case__    :       int       		= list(state_dict.keys()						)

      # extract state_dict for VQVAE
      snake_case__    :       Dict       		= {}
      snake_case__    :       Tuple       		= """first_stage_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

    # extract state_dict for UNetLDM
      snake_case__    :       Optional[Any]       		= {}
      snake_case__    :       Optional[int]       		= """model.diffusion_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

      snake_case__    :       Dict       		= config.model.params.first_stage_config.params
      snake_case__    :       Optional[Any]       		= config.model.params.unet_config.params

      snake_case__    :       Tuple       		= VQModel(**_lowerCAmelCase						).eval()
      vqvae.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= UNetLDMModel(**_lowerCAmelCase						).eval()
      unet.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= DDIMScheduler(
          timesteps=config.model.params.timesteps	,  beta_schedule="""scaled_linear"""	,  beta_start=config.model.params.linear_start	,  beta_end=config.model.params.linear_end	,  clip_sample=_lowerCAmelCase	,  )

      snake_case__    :       List[str]       		= LDMPipeline(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)
      pipeline.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
       __a  			=						argparse.ArgumentParser()
       parser.add_argument("--checkpoint_path", type=str, required=True)
       parser.add_argument("--config_path", type=str, required=True)
       parser.add_argument("--output_path", type=str, required=True)
       __a  			=						parser.parse_args()

       convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
