

import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification






def 					__lowercase      (     _A			)     ->						int:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   []
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
              F"stage{idx}.patch_embed.proj.weight",
          )			)
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
              F"stage{idx}.patch_embed.proj.bias",
          )			)
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
              F"stage{idx}.patch_embed.norm.weight",
          )			)
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
              F"stage{idx}.patch_embed.norm.bias",
          )			)
      return embed






def 					__lowercase      (     _A						,    _A			)     ->						Any:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE :      Optional[int]       =   []
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj.bias",
          )			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias")			)
      return attention_weights






def 					__lowercase      (     _A			)     ->						Optional[Any]:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE :      List[Any]       =   []
      token.append((F"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")			)
      return token






def 					__lowercase      (     )     ->						Union[str, Any]:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE :      Dict       =   []
      head.append(("""layernorm.weight""", """norm.weight""")			)
      head.append(("""layernorm.bias""", """norm.bias""")			)
      head.append(("""classifier.weight""", """head.weight""")			)
      head.append(("""classifier.bias""", """head.bias""")			)
      return head






def 					__lowercase      (     _A						,    _A						,    _A						,    _A			)     ->						str:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE :      List[str]       =   """imagenet-1k-id2label.json"""
      SCREAMING_SNAKE_CASE :      Tuple       =   1000

      SCREAMING_SNAKE_CASE :      str       =   """huggingface/label-files"""
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   num_labels
      SCREAMING_SNAKE_CASE :      int       =   json.load(open(cached_download(hf_hub_url(_A						,    _A						,    repo_type="""dataset"""			)			)						,    """r"""			)			)
      SCREAMING_SNAKE_CASE :      int       =   {int(_A			): v for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE :      List[Any]       =   idalabel
      SCREAMING_SNAKE_CASE :      int       =   {v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE :      Tuple       =   CvtConfig(num_labels=_A						,    idalabel=_A						,    labelaid=_A			)

      # For depth size 13 (13 = 1+2+10)
      if cvt_model.rsplit("""/"""						,    1			)[-1][4:6] == "13":
            SCREAMING_SNAKE_CASE :      List[str]       =   [1, 2, 10]

      # For depth size 21 (21 = 1+4+16)
      elif cvt_model.rsplit("""/"""						,    1			)[-1][4:6] == "21":
            SCREAMING_SNAKE_CASE :      int       =   [1, 4, 16]

      # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
      else:
            SCREAMING_SNAKE_CASE :      str       =   [2, 2, 20]
            SCREAMING_SNAKE_CASE :      Optional[int]       =   [3, 12, 16]
            SCREAMING_SNAKE_CASE :      str       =   [192, 768, 1024]

      SCREAMING_SNAKE_CASE :      List[Any]       =   CvtForImageClassification(_A			)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""			)
      SCREAMING_SNAKE_CASE :      List[str]       =   image_size
      SCREAMING_SNAKE_CASE :      Tuple       =   torch.load(_A						,    map_location=torch.device("""cpu"""			)			)

      SCREAMING_SNAKE_CASE :      Tuple       =   OrderedDict()
      SCREAMING_SNAKE_CASE :      Any       =   []

      for idx in range(len(config.depth			)			):
            if config.cls_token[idx]:
                  SCREAMING_SNAKE_CASE :      Any       =   list_of_state_dict + cls_token(_A			)
            SCREAMING_SNAKE_CASE :      int       =   list_of_state_dict + embeddings(_A			)
            for cnt in range(config.depth[idx]			):
                  SCREAMING_SNAKE_CASE :      str       =   list_of_state_dict + attention(_A						,    _A			)

      SCREAMING_SNAKE_CASE :      List[str]       =   list_of_state_dict + final()
      for gg in list_of_state_dict:
            print(_A			)
      for i in range(len(_A			)			):
            SCREAMING_SNAKE_CASE :      str       =   original_weights[list_of_state_dict[i][1]]

      model.load_state_dict(_A			)
      model.save_pretrained(_A			)
      image_processor.save_pretrained(_A			)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
  UpperCAmelCase__ :     List[str]				    =    argparse.ArgumentParser()
  parser.add_argument(
      """--cvt_model""",
      default="""cvt-w24""",
      type=str,
      help="""Name of the cvt model you'd like to convert.""",
  )
  parser.add_argument(
      """--image_size""",
      default=384,
      type=int,
      help="""Input Image Size""",
  )
  parser.add_argument(
      """--cvt_file_name""",
      default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
      type=str,
      help="""Input Image Size""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  UpperCAmelCase__ :     List[str]				    =    parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__ :     Optional[Any]				    =    False
UpperCAmelCase__ :     Union[str, Any]				    =    True
UpperCAmelCase__ :     int				    =    False


if __name__ == "__main__":
  UpperCAmelCase__ :     List[Any]				    =    argparse.ArgumentParser()

  parser.add_argument(
      """--repo_path""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the architecture.""",
  )

  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

  UpperCAmelCase__ :     List[str]				    =    parser.parse_args()

  UpperCAmelCase__ :     Dict				    =    {
      """image_size""": """sample_size""",
      """num_res_blocks""": """layers_per_block""",
      """block_channels""": """block_out_channels""",
      """down_blocks""": """down_block_types""",
      """up_blocks""": """up_block_types""",
      """downscale_freq_shift""": """freq_shift""",
      """resnet_num_groups""": """norm_num_groups""",
      """resnet_act_fn""": """act_fn""",
      """resnet_eps""": """norm_eps""",
      """num_head_channels""": """attention_head_dim""",
  }

  UpperCAmelCase__ :     str				    =    {
      """time_steps""": """time_proj""",
      """mid""": """mid_block""",
      """downsample_blocks""": """down_blocks""",
      """upsample_blocks""": """up_blocks""",
  }

  UpperCAmelCase__ :     Union[str, Any]				    =    """""" if has_file(args.repo_path, """config.json""") else """unet"""

  with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
    UpperCAmelCase__ :     Union[str, Any]				    =    reader.read()
    UpperCAmelCase__ :     Any				    =    json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, """config.json"""):
    UpperCAmelCase__ :     int				    =    UNetaDModel(**config)
  else:
    UpperCAmelCase__ :     Optional[Any]				    =    UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
    UpperCAmelCase__ :     int				    =    class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  UpperCAmelCase__ :     Optional[Any]				    =    dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        UpperCAmelCase__ :     str				    =    config[key]
        del config[key]

    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

  if do_only_weights:
    UpperCAmelCase__ :     Dict				    =    torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

    UpperCAmelCase__ :     Optional[Any]				    =    {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
        continue
      UpperCAmelCase__ :     Union[str, Any]				    =    False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split(""".""")[0] == key:
          UpperCAmelCase__ :     List[Any]				    =    param_value
          UpperCAmelCase__ :     List[str]				    =    True
      if not has_changed:
        UpperCAmelCase__ :     Any				    =    param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))




