import argparse

import torch

from diffusers import UNetaDConditionModel
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__=None			,					lowerCamelCase__=None			,					lowerCamelCase__=None       )					->Any:

  '''simple docstring'''




  assert isinstance(lowerCamelCase__			,					lowerCamelCase__       ), "Paths should be a list of dicts containing 'old' and 'new' keys."

  # Splits the attention layers into three variables.
  if attention_paths_to_split is not None:
    for path, path_map in attention_paths_to_split.items():
      a__  		=  old_checkpoint[path]
      a__  		=  old_tensor.shape[0] // 3

      a__  		=  (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

      a__  		=  old_tensor.shape[0] // config["num_head_channels"] // 3

      a__  		=  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]       )
      a__		,a__		,a__  		=  old_tensor.split(channels // num_heads			,					dim=1       )

      a__  		=  query.reshape(lowerCamelCase__       )
      a__  		=  key.reshape(lowerCamelCase__       )
      a__  		=  value.reshape(lowerCamelCase__       )

  for path in paths:
    a__  		=  path["new"]

    # These have already been assigned
    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    if additional_replacements is not None:
      for replacement in additional_replacements:
        a__  		=  new_path.replace(replacement["old"]			,					replacement["new"]       )

        # proj_attn.weight has to be converted from conv 1D to linear
    a__  		=  old_checkpoint[path["old"]]
    a__  		=  ["proj_attn.weight"]
    a__  		=  ["proj_out.weight", "proj_in.weight"]
    if any(k in new_path for k in names       ):
      a__  		=  weight[:, :, 0]
    elif any(k in new_path for k in names_a       ) and len(weight.shape       ) > 2 and ".attentions." not in new_path:
      a__  		=  weight[:, :, 0]
    else:
      a__  		=  weight
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__=0       )					->int:

  '''simple docstring'''




  a__  		=  []
  for old_item in old_list:
    a__  		=  old_item

    #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
    #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

    #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
    #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

    #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

    mapping.append({"old": old_item, "new": new_item}       )

  return mapping
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__=1       )					->Any:

  '''simple docstring'''




  if n_shave_prefix_segments >= 0:
    return ".".join(path.split("."       )[n_shave_prefix_segments:]       )
  else:
    return ".".join(path.split("."       )[:n_shave_prefix_segments]       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__=0       )					->List[Any]:

  '''simple docstring'''




  a__  		=  []
  for old_item in old_list:
    mapping.append({"old": old_item, "new": old_item}       )

  return mapping
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__=0       )					->Optional[Any]:

  '''simple docstring'''




  a__  		=  []
  for old_item in old_list:
    a__  		=  old_item.replace("in_layers.0"			,					"norm1"       )
    a__  		=  new_item.replace("in_layers.2"			,					"conv1"       )

    a__  		=  new_item.replace("out_layers.0"			,					"norm2"       )
    a__  		=  new_item.replace("out_layers.3"			,					"conv2"       )

    a__  		=  new_item.replace("emb_layers.1"			,					"time_emb_proj"       )
    a__  		=  new_item.replace("skip_connection"			,					"conv_shortcut"       )

    a__  		=  shave_segments(lowerCamelCase__			,					n_shave_prefix_segments=lowerCamelCase__       )

    if "temopral_conv" not in old_item:
      mapping.append({"old": old_item, "new": new_item}       )

  return mapping
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__=None			,					lowerCamelCase__=False       )					->List[Any]:

  '''simple docstring'''




  a__  		=  {}
  a__  		=  list(checkpoint.keys()       )

  a__  		=  "model.diffusion_model."

  # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
  if sum(k.startswith("model_ema"       ) for k in keys       ) > 1_00 and extract_ema:
    print(f"Checkpoint {path} has both EMA and non-EMA weights."       )
    print(
        "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
        " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."       )
    for key in keys:
      if key.startswith("model.diffusion_model"       ):
        a__  		=  "model_ema." + "".join(key.split("."       )[1:]       )
        a__  		=  checkpoint.pop(lowerCamelCase__       )
  else:
    if sum(k.startswith("model_ema"       ) for k in keys       ) > 1_00:
      print(
          "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
          " weights (usually better for inference), please make sure to add the `--extract_ema` flag."       )

    for key in keys:
      a__  		=  checkpoint.pop(lowerCamelCase__       )

  a__  		=  {}

  a__  		=  unet_state_dict["time_embed.0.weight"]
  a__  		=  unet_state_dict["time_embed.0.bias"]
  a__  		=  unet_state_dict["time_embed.2.weight"]
  a__  		=  unet_state_dict["time_embed.2.bias"]

  if config["class_embed_type"] is None:
    # No parameters to port
    ...
  elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
    a__  		=  unet_state_dict["label_emb.0.0.weight"]
    a__  		=  unet_state_dict["label_emb.0.0.bias"]
    a__  		=  unet_state_dict["label_emb.0.2.weight"]
    a__  		=  unet_state_dict["label_emb.0.2.bias"]
  else:
    raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}"       )

  a__  		=  unet_state_dict["input_blocks.0.0.weight"]
  a__  		=  unet_state_dict["input_blocks.0.0.bias"]

  a__  		=  [v for v in unet_state_dict if v.startswith("input_blocks.0.1"       )]
  a__  		=  renew_attention_paths(lowerCamelCase__       )
  a__  		=  {"old": "input_blocks.0.1", "new": "transformer_in"}
  assign_to_checkpoint(lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					additional_replacements=[meta_path]			,					config=lowerCamelCase__       )

  a__  		=  unet_state_dict["out.0.weight"]
  a__  		=  unet_state_dict["out.0.bias"]
  a__  		=  unet_state_dict["out.2.weight"]
  a__  		=  unet_state_dict["out.2.bias"]

  # Retrieves the keys for the input blocks only
  a__  		=  len({".".join(layer.split("."       )[:2]       ) for layer in unet_state_dict if "input_blocks" in layer}       )
  a__  		=  {
      layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
      for layer_id in range(lowerCamelCase__       )
  }

  # Retrieves the keys for the middle blocks only
  a__  		=  len({".".join(layer.split("."       )[:2]       ) for layer in unet_state_dict if "middle_block" in layer}       )
  a__  		=  {
      layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
      for layer_id in range(lowerCamelCase__       )
  }

  # Retrieves the keys for the output blocks only
  a__  		=  len({".".join(layer.split("."       )[:2]       ) for layer in unet_state_dict if "output_blocks" in layer}       )
  a__  		=  {
      layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
      for layer_id in range(lowerCamelCase__       )
  }

  for i in range(1			,					lowerCamelCase__       ):
    a__  		=  (i - 1) // (config["layers_per_block"] + 1)
    a__  		=  (i - 1) % (config["layers_per_block"] + 1)

    a__  		=  [
        key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
    ]
    a__  		=  [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
    a__  		=  [key for key in input_blocks[i] if f"input_blocks.{i}.2" in key]

    if f"input_blocks.{i}.op.weight" in unet_state_dict:
      a__  		=  unet_state_dict.pop(
          f"input_blocks.{i}.op.weight"       )
      a__  		=  unet_state_dict.pop(
          f"input_blocks.{i}.op.bias"       )

    a__  		=  renew_resnet_paths(lowerCamelCase__       )
    a__  		=  {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
    assign_to_checkpoint(
        lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					additional_replacements=[meta_path]			,					config=lowerCamelCase__       )

    a__  		=  [key for key in resnets if "temopral_conv" in key]
    a__  		=  renew_temp_conv_paths(lowerCamelCase__       )
    a__  		=  {
        "old": f"input_blocks.{i}.0.temopral_conv",
        "new": f"down_blocks.{block_id}.temp_convs.{layer_in_block_id}",
    }
    assign_to_checkpoint(
        lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					additional_replacements=[meta_path]			,					config=lowerCamelCase__       )

    if len(lowerCamelCase__       ):
      a__  		=  renew_attention_paths(lowerCamelCase__       )
      a__  		=  {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
      assign_to_checkpoint(
          lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					additional_replacements=[meta_path]			,					config=lowerCamelCase__       )

    if len(lowerCamelCase__       ):
      a__  		=  renew_attention_paths(lowerCamelCase__       )
      a__  		=  {
          "old": f"input_blocks.{i}.2",
          "new": f"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
      }
      assign_to_checkpoint(
          lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					additional_replacements=[meta_path]			,					config=lowerCamelCase__       )

  a__  		=  middle_blocks[0]
  a__  		=  [key for key in resnet_a if "temopral_conv" in key]
  a__  		=  middle_blocks[1]
  a__  		=  middle_blocks[2]
  a__  		=  middle_blocks[3]
  a__  		=  [key for key in resnet_a if "temopral_conv" in key]

  a__  		=  renew_resnet_paths(lowerCamelCase__       )
  a__  		=  {"old": "middle_block.0", "new": "mid_block.resnets.0"}
  assign_to_checkpoint(
      lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					config=lowerCamelCase__			,					additional_replacements=[meta_path]       )

  a__  		=  renew_temp_conv_paths(lowerCamelCase__       )
  a__  		=  {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
  assign_to_checkpoint(
      lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					config=lowerCamelCase__			,					additional_replacements=[meta_path]       )

  a__  		=  renew_resnet_paths(lowerCamelCase__       )
  a__  		=  {"old": "middle_block.3", "new": "mid_block.resnets.1"}
  assign_to_checkpoint(
      lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					config=lowerCamelCase__			,					additional_replacements=[meta_path]       )

  a__  		=  renew_temp_conv_paths(lowerCamelCase__       )
  a__  		=  {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
  assign_to_checkpoint(
      lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					config=lowerCamelCase__			,					additional_replacements=[meta_path]       )

  a__  		=  renew_attention_paths(lowerCamelCase__       )
  a__  		=  {"old": "middle_block.1", "new": "mid_block.attentions.0"}
  assign_to_checkpoint(
      lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					additional_replacements=[meta_path]			,					config=lowerCamelCase__       )

  a__  		=  renew_attention_paths(lowerCamelCase__       )
  a__  		=  {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
  assign_to_checkpoint(
      lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					additional_replacements=[meta_path]			,					config=lowerCamelCase__       )

  for i in range(lowerCamelCase__       ):
    a__  		=  i // (config["layers_per_block"] + 1)
    a__  		=  i % (config["layers_per_block"] + 1)
    a__  		=  [shave_segments(lowerCamelCase__			,					2       ) for name in output_blocks[i]]
    a__  		=  {}

    for layer in output_block_layers:
      a__		,a__  		=  layer.split("."       )[0], shave_segments(lowerCamelCase__			,					1       )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(lowerCamelCase__       )
      else:
        a__  		=  [layer_name]

    if len(lowerCamelCase__       ) > 1:
      a__  		=  [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
      a__  		=  [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
      a__  		=  [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key]

      a__  		=  renew_resnet_paths(lowerCamelCase__       )
      a__  		=  renew_resnet_paths(lowerCamelCase__       )

      a__  		=  {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
      assign_to_checkpoint(
          lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					additional_replacements=[meta_path]			,					config=lowerCamelCase__       )

      a__  		=  [key for key in resnets if "temopral_conv" in key]
      a__  		=  renew_temp_conv_paths(lowerCamelCase__       )
      a__  		=  {
          "old": f"output_blocks.{i}.0.temopral_conv",
          "new": f"up_blocks.{block_id}.temp_convs.{layer_in_block_id}",
      }
      assign_to_checkpoint(
          lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					additional_replacements=[meta_path]			,					config=lowerCamelCase__       )

      a__  		=  {k: sorted(lowerCamelCase__       ) for k, v in output_block_list.items()}
      if ["conv.bias", "conv.weight"] in output_block_list.values():
        a__  		=  list(output_block_list.values()       ).index(["conv.bias", "conv.weight"]       )
        a__  		=  unet_state_dict[
            f"output_blocks.{i}.{index}.conv.weight"
        ]
        a__  		=  unet_state_dict[
            f"output_blocks.{i}.{index}.conv.bias"
        ]

        # Clear attentions as they have been attributed above.
        if len(lowerCamelCase__       ) == 2:
          a__  		=  []

      if len(lowerCamelCase__       ):
        a__  		=  renew_attention_paths(lowerCamelCase__       )
        a__  		=  {
            "old": f"output_blocks.{i}.1",
            "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
        }
        assign_to_checkpoint(
            lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					additional_replacements=[meta_path]			,					config=lowerCamelCase__       )

      if len(lowerCamelCase__       ):
        a__  		=  renew_attention_paths(lowerCamelCase__       )
        a__  		=  {
            "old": f"output_blocks.{i}.2",
            "new": f"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
        }
        assign_to_checkpoint(
            lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					additional_replacements=[meta_path]			,					config=lowerCamelCase__       )
    else:
      a__  		=  renew_resnet_paths(lowerCamelCase__			,					n_shave_prefix_segments=1       )
      for path in resnet_0_paths:
        a__  		=  ".".join(["output_blocks", str(lowerCamelCase__       ), path["old"]]       )
        a__  		=  ".".join(["up_blocks", str(lowerCamelCase__       ), "resnets", str(lowerCamelCase__       ), path["new"]]       )
        a__  		=  unet_state_dict[old_path]

      a__  		=  [l for l in output_block_layers if "temopral_conv" in l]
      for path in temopral_conv_paths:
        a__  		=  path.split("temopral_conv."       )[-1]
        a__  		=  ".".join(["output_blocks", str(lowerCamelCase__       ), str(lowerCamelCase__       ), "temopral_conv", pruned_path]       )
        a__  		=  ".".join(["up_blocks", str(lowerCamelCase__       ), "temp_convs", str(lowerCamelCase__       ), pruned_path]       )
        a__  		=  unet_state_dict[old_path]

  return new_checkpoint


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     argparse.ArgumentParser()

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

     _SCREAMING_SNAKE_CASE     =     torch.load(args.checkpoint_path, map_location="""cpu""")
     _SCREAMING_SNAKE_CASE     =     UNetaDConditionModel()

     _SCREAMING_SNAKE_CASE     =     convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

     _SCREAMING_SNAKE_CASE     =     set(unet.state_dict().keys()) - set(converted_ckpt.keys())
     _SCREAMING_SNAKE_CASE     =     set(converted_ckpt.keys()) - set(unet.state_dict().keys())

     assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

     # load state_dict
     unet.load_state_dict(converted_ckpt)

     unet.save_pretrained(args.dump_path)

     # -- finish converting the unet --


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_SCREAMING_SNAKE_CASE     =     ["""UtteranceLevel"""]
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  torch.load(lowerCamelCase__			,					map_location="cpu"       )
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}"       )

  a__  		=  checkpoint["Downstream"]

  a__  		=  HubertConfig.from_pretrained(lowerCamelCase__       )
  a__  		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase__			,					config=lowerCamelCase__       )
  a__  		=  WavaVecaFeatureExtractor.from_pretrained(
      lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					do_normalize=lowerCamelCase__       )

  if hf_congfig.use_weighted_layer_sum:
    a__  		=  checkpoint["Featurizer"]["weights"]

  a__  		=  downstream_dict["projector.weight"]
  a__  		=  downstream_dict["projector.bias"]
  a__  		=  downstream_dict["model.post_net.linear.weight"]
  a__  		=  downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(lowerCamelCase__       )
  hf_model.save_pretrained(lowerCamelCase__       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     argparse.ArgumentParser()
     parser.add_argument(
         """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
     )
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
     parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
     _SCREAMING_SNAKE_CASE     =     parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

