import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   ['image_processor', 'tokenizer']
 lowercase				     =   'FlavaImageProcessor'
 lowercase				     =   ('BertTokenizer', 'BertTokenizerFast')

 def __init__( self	:							Optional[int]   ,      snake_case	:							int=None   ,      snake_case	:							int=None   ,      **snake_case	:							int			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						int =					None
  if "feature_extractor" in kwargs:
   warnings.warn(
       'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
       ' instead.'   ,      snake_case   ,      )
   UpperCamelCase_					:						Optional[int] =					kwargs.pop('feature_extractor'			)

  UpperCamelCase_					:						Optional[Any] =					image_processor if image_processor is not None else feature_extractor
  if image_processor is None:
   raise ValueError('You need to specify an `image_processor`.'			)
  if tokenizer is None:
   raise ValueError('You need to specify a `tokenizer`.'			)

  super().__init__(snake_case   ,      snake_case			)
  UpperCamelCase_					:						int =					self.image_processor

 def __call__( self	:							Optional[int]   ,      snake_case	:							Optional[ImageInput] = None   ,      snake_case	:							Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None   ,      snake_case	:							bool = True   ,      snake_case	:							Union[bool, str, PaddingStrategy] = False   ,      snake_case	:							Union[bool, str, TruncationStrategy] = False   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							int = 0   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							Optional[bool] = None   ,      snake_case	:							Optional[bool] = None   ,      snake_case	:							Optional[bool] = None   ,      snake_case	:							Optional[bool] = None   ,      snake_case	:							bool = False   ,      snake_case	:							bool = False   ,      snake_case	:							bool = False   ,      snake_case	:							bool = False   ,      snake_case	:							bool = True   ,      snake_case	:							Optional[Union[str, TensorType]] = None   ,      **snake_case	:							Tuple   ,      )   ->						int:

  """simple docstring"""





  if text is None and images is None:
   raise ValueError('You have to specify either text or images. Both cannot be none.'			)

  if text is not None:
   UpperCamelCase_					:						List[Any] =					self.tokenizer(
       text=snake_case   ,      add_special_tokens=snake_case   ,      padding=snake_case   ,      truncation=snake_case   ,      max_length=snake_case   ,      stride=snake_case   ,      pad_to_multiple_of=snake_case   ,      return_token_type_ids=snake_case   ,      return_attention_mask=snake_case   ,      return_overflowing_tokens=snake_case   ,      return_special_tokens_mask=snake_case   ,      return_offsets_mapping=snake_case   ,      return_length=snake_case   ,      verbose=snake_case   ,      return_tensors=snake_case   ,      **snake_case   ,      )
  if images is not None:
   UpperCamelCase_					:						Dict =					self.image_processor(
       snake_case   ,      return_image_mask=snake_case   ,      return_codebook_pixels=snake_case   ,      return_tensors=snake_case   ,      **snake_case   ,      )

  if text is not None and images is not None:
   encoding.update(snake_case			)
   return encoding
  elif text is not None:
   return encoding
  else:
   return BatchEncoding(data=dict(**snake_case			)   ,      tensor_type=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      *snake_case	:							Optional[int]   ,      **snake_case	:							Any			)   ->						List[str]:

  """simple docstring"""





  return self.tokenizer.batch_decode(*snake_case   ,      **snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      *snake_case	:							Optional[Any]   ,      **snake_case	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  return self.tokenizer.decode(*snake_case   ,      **snake_case			)

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					self.tokenizer.model_input_names
  UpperCamelCase_					:						Any =					self.image_processor.model_input_names
  return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Union[str, Any]:

  """simple docstring"""





  warnings.warn(
      '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,      snake_case   ,      )
  return self.image_processor_class


 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Dict:

  """simple docstring"""





  warnings.warn(
      '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,      snake_case   ,      )
  return self.image_processor




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[Any] =					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Any =					downstream_dict['projector.weight']
 UpperCamelCase_					:						List[str] =					downstream_dict['projector.bias']
 UpperCamelCase_					:						Tuple =					downstream_dict['model.post_net.linear.weight']
 UpperCamelCase_					:						int =					downstream_dict['model.post_net.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Any =					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					downstream_dict['model.linear.weight']
 UpperCamelCase_					:						Any =					downstream_dict['model.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						str =					UniSpeechSatForXVector.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					downstream_dict['connector.weight']
 UpperCamelCase_					:						int =					downstream_dict['connector.bias']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
  UpperCamelCase_					:						Union[str, Any] =					downstream_dict[
      F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
  ]
  UpperCamelCase_					:						Tuple =					downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

 UpperCamelCase_					:						int =					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
 UpperCamelCase_					:						Dict =					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
 UpperCamelCase_					:						List[Any] =					downstream_dict['objective.W']
 return model





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     int	):
 UpperCamelCase_					:						int =					torch.load(lowerCamelCase  ,  map_location='cpu'	)

 UpperCamelCase_					:						Any =					checkpoint['Downstream']

 UpperCamelCase_					:						Dict =					UniSpeechSatConfig.from_pretrained(lowerCamelCase	)
 UpperCamelCase_					:						str =					WavaVecaFeatureExtractor.from_pretrained(
     lowerCamelCase  ,  return_attention_mask=lowerCamelCase  ,  do_normalize=lowerCamelCase	)

 UpperCamelCase_					:						int =					hf_config.architectures[0]
 if arch.endswith('ForSequenceClassification'	):
  UpperCamelCase_					:						Tuple =					convert_classification(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForAudioFrameClassification'	):
  UpperCamelCase_					:						List[str] =					convert_diarization(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForXVector'	):
  UpperCamelCase_					:						Union[str, Any] =					convert_xvector(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 else:
  raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"	)

 if hf_config.use_weighted_layer_sum:
  UpperCamelCase_					:						Tuple =					checkpoint['Featurizer']['weights']

 hf_feature_extractor.save_pretrained(lowerCamelCase	)
 hf_model.save_pretrained(lowerCamelCase	)


if __name__ == "__main__":
   a_    					=       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.')
   a_    					=       parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



