
'''simple docstring'''

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

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


@dataclass(frozen=_a			)
class      a_     (_a			):
       __lowerCAmelCase			: str			=					field(default="""automatic-speech-recognition"""							,	metadata={"""include_in_asdict_even_if_is_default""": True}			)
       __lowerCAmelCase			: ClassVar[Features]			=					Features({"""audio""": Audio()}			)
       __lowerCAmelCase			: ClassVar[Features]			=					Features({"""transcription""": Value("""string"""			)}			)
       __lowerCAmelCase			: str			=					"audio"
       __lowerCAmelCase			: str			=					"transcription"




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):






         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]      ,      SCREAMING_SNAKE_CASE__     ):
           raise ValueError(f'Column {self.audio_column} is not an Audio type.'     )
         _lowerCAmelCase				:     Optional[int]       =    copy.deepcopy(self     )
         _lowerCAmelCase				:     Optional[int]       =    self.input_schema.copy()
         _lowerCAmelCase				:     Any       =    features[self.audio_column]
         _lowerCAmelCase				:     Dict       =    input_schema
         return task_template







       @property
       def 							__UpperCamelCase				(   self     ):
         return {self.audio_column: "audio", self.transcription_column: "transcription"}









"""simple docstring"""
import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def      __lowerCamelCase   (							a_     :		str ,   a_     :		Dict ,   a_     :		Any ,   a_     :		str							)			->      str:
      __SCREAMING_SNAKE_CASE   :int						=  s.rsplit(a_ ,   a_							)
      return new.join(a_							)



def      __lowerCamelCase   (							a_     :		List[str]							)			->      Dict:
      # encoder.embeddings are double copied in original FLAVA
      return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()							)



def      __lowerCamelCase   (							a_     :		Optional[int]							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  {}

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
      for key, value in state_dict.items():
            for group_key in group_keys:
                  if group_key in key:
                        __SCREAMING_SNAKE_CASE   :Optional[Any]						=  key.replace(f'''{group_key}.''' ,   f'''{group_key}.group.'''							)

            if "res_path" in key:
                  __SCREAMING_SNAKE_CASE   :str						=  key.replace('''res_path.''' ,   '''res_path.path.'''							)

            if key.endswith('''.w'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.w''' ,   '''.weight''' ,   1							)
            if key.endswith('''.b'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.b''' ,   '''.bias''' ,   1							)

            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  value.float()

      return upgrade



@torch.no_grad()
def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Optional[int] ,   a_     :		Optional[int]=None ,   a_     :		Dict=True							)			->      Union[str, Any]:
      from dall_e import Encoder

      __SCREAMING_SNAKE_CASE   :int						=  Encoder()
      if os.path.exists(a_							):
            __SCREAMING_SNAKE_CASE   :Dict						=  torch.load(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :List[str]						=  torch.hub.load_state_dict_from_url(a_							)

      if isinstance(a_ ,   a_							):
            __SCREAMING_SNAKE_CASE   :List[str]						=  ckpt.state_dict()
      encoder.load_state_dict(a_							)

      if config_path is not None:
            __SCREAMING_SNAKE_CASE   :Any						=  FlavaImageCodebookConfig.from_pretrained(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  FlavaImageCodebookConfig()

      __SCREAMING_SNAKE_CASE   :Tuple						=  FlavaImageCodebook(a_							).eval()
      __SCREAMING_SNAKE_CASE   :List[str]						=  encoder.state_dict()

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  upgrade_state_dict(a_							)
      hf_model.load_state_dict(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  hf_model.state_dict()
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  count_parameters(a_							)
      __SCREAMING_SNAKE_CASE   :Any						=  count_parameters(a_							)

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

      if save_checkpoint:
            hf_model.save_pretrained(a_							)
      else:
            return hf_state_dict


if __name__ == "__main__":
  lowerCamelCase_								=		argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  lowerCamelCase_								=		parser.parse_args()

  convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)