

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCAmelCase       )
class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : str			       =field(default="""automatic-speech-recognition"""	,			metadata={"""include_in_asdict_even_if_is_default""": True}       )
  UpperCAmelCase__      : ClassVar[Features]			       =Features({"""audio""": Audio()}       )
  UpperCAmelCase__      : ClassVar[Features]			       =Features({"""transcription""": Value("""string"""       )}       )
  UpperCAmelCase__      : str			       ="audio"
  UpperCAmelCase__      : str			       ="transcription"


  def  _lowercase      (			self    : str				,				UpperCAmelCase__    : Optional[int]		)					->Dict:
        """simple docstring"""







        if self.audio_column not in features:
              raise ValueError(f"Column {self.audio_column} is not present in features."		)
        if not isinstance(features[self.audio_column]				,				UpperCAmelCase__		):
              raise ValueError(f"Column {self.audio_column} is not an Audio type."		)
        SCREAMING_SNAKE_CASE :      List[str]       =   copy.deepcopy(self		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.input_schema.copy()
        SCREAMING_SNAKE_CASE :      str       =   features[self.audio_column]
        SCREAMING_SNAKE_CASE :      List[Any]       =   input_schema
        return task_template




  @property
  def  _lowercase      (			self    : Union[str, Any]		)					->Dict[str, str]:
        """simple docstring"""







        return {self.audio_column: "audio", self.transcription_column: "transcription"}







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))




