


'''simple docstring'''






import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A   :    List[str]											= logging.get_logger(__name__)

__A   :    Optional[Any]											= {
    "google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
    "google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
    "google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'owlvit_text_model'

    def __init__(  self       :    str       ,		lowerCamelCase       :    Any=4_94_08       ,		lowerCamelCase       :    List[str]=5_12       ,		lowerCamelCase       :    Any=20_48       ,		lowerCamelCase       :    str=12       ,		lowerCamelCase       :    Any=8       ,		lowerCamelCase       :    Tuple=16       ,		lowerCamelCase       :    List[Any]="quick_gelu"       ,		lowerCamelCase       :    List[str]=1E-5       ,		lowerCamelCase       :    Optional[int]=0.0       ,		lowerCamelCase       :    Optional[int]=0.02       ,		lowerCamelCase       :    Optional[int]=1.0       ,		lowerCamelCase       :    Tuple=0       ,		lowerCamelCase       :    str=4_94_06       ,		lowerCamelCase       :    Tuple=4_94_07       ,		**lowerCamelCase       :    Union[str, Any]       ,		)  ->    Tuple:
      super().__init__(pad_token_id=lowerCamelCase       ,		bos_token_id=lowerCamelCase       ,		eos_token_id=lowerCamelCase       ,		**lowerCamelCase						)

      lowerCAmelCase_     : List[Any]     =  vocab_size
      lowerCAmelCase_     : List[str]     =  hidden_size
      lowerCAmelCase_     : int     =  intermediate_size
      lowerCAmelCase_     : Dict     =  num_hidden_layers
      lowerCAmelCase_     : Any     =  num_attention_heads
      lowerCAmelCase_     : Optional[Any]     =  max_position_embeddings
      lowerCAmelCase_     : str     =  hidden_act
      lowerCAmelCase_     : Any     =  layer_norm_eps
      lowerCAmelCase_     : Optional[int]     =  attention_dropout
      lowerCAmelCase_     : Optional[Any]     =  initializer_range
      lowerCAmelCase_     : int     =  initializer_factor





    @classmethod
    def __lowercase  (  cls       :    Tuple       ,		lowerCamelCase       :    Union[str, os.PathLike]       ,		**lowerCamelCase       :    List[str]						)  ->    "PretrainedConfig":
      cls._set_token_in_kwargs(lowerCamelCase						)

      lowerCAmelCase_,				lowerCAmelCase_     : List[Any]     =  cls.get_config_dict(lowerCamelCase       ,		**lowerCamelCase						)

      # get the text config dict if we are loading from OwlViTConfig
      if config_dict.get("""model_type"""						) == "owlvit":
        lowerCAmelCase_     : Union[str, Any]     =  config_dict["""text_config"""]

      if "model_type" in config_dict and hasattr(cls       ,		"""model_type"""						) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

      return cls.from_dict(lowerCamelCase       ,		**lowerCamelCase						)







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'owlvit_vision_model'

    def __init__(  self       :    Optional[Any]       ,		lowerCamelCase       :    Union[str, Any]=7_68       ,		lowerCamelCase       :    int=30_72       ,		lowerCamelCase       :    Dict=12       ,		lowerCamelCase       :    Optional[Any]=12       ,		lowerCamelCase       :    int=3       ,		lowerCamelCase       :    Dict=7_68       ,		lowerCamelCase       :    Optional[Any]=32       ,		lowerCamelCase       :    Tuple="quick_gelu"       ,		lowerCamelCase       :    int=1E-5       ,		lowerCamelCase       :    Optional[int]=0.0       ,		lowerCamelCase       :    List[Any]=0.02       ,		lowerCamelCase       :    str=1.0       ,		**lowerCamelCase       :    int       ,		)  ->    List[str]:
      super().__init__(**lowerCamelCase						)

      lowerCAmelCase_     : Optional[int]     =  hidden_size
      lowerCAmelCase_     : str     =  intermediate_size
      lowerCAmelCase_     : Dict     =  num_hidden_layers
      lowerCAmelCase_     : Union[str, Any]     =  num_attention_heads
      lowerCAmelCase_     : Union[str, Any]     =  num_channels
      lowerCAmelCase_     : str     =  image_size
      lowerCAmelCase_     : Union[str, Any]     =  patch_size
      lowerCAmelCase_     : Union[str, Any]     =  hidden_act
      lowerCAmelCase_     : Optional[Any]     =  layer_norm_eps
      lowerCAmelCase_     : Optional[int]     =  attention_dropout
      lowerCAmelCase_     : List[Any]     =  initializer_range
      lowerCAmelCase_     : Union[str, Any]     =  initializer_factor





    @classmethod
    def __lowercase  (  cls       :    int       ,		lowerCamelCase       :    Union[str, os.PathLike]       ,		**lowerCamelCase       :    Optional[Any]						)  ->    "PretrainedConfig":
      cls._set_token_in_kwargs(lowerCamelCase						)

      lowerCAmelCase_,				lowerCAmelCase_     : List[str]     =  cls.get_config_dict(lowerCamelCase       ,		**lowerCamelCase						)

      # get the vision config dict if we are loading from OwlViTConfig
      if config_dict.get("""model_type"""						) == "owlvit":
        lowerCAmelCase_     : Tuple     =  config_dict["""vision_config"""]

      if "model_type" in config_dict and hasattr(cls       ,		"""model_type"""						) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

      return cls.from_dict(lowerCamelCase       ,		**lowerCamelCase						)







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'owlvit'
    lowercase  					=   True

    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Optional[Any]=None       ,		lowerCamelCase       :    Union[str, Any]=None       ,		lowerCamelCase       :    int=5_12       ,		lowerCamelCase       :    List[Any]=2.6_592       ,		lowerCamelCase       :    Optional[int]=True       ,		**lowerCamelCase       :    Optional[Any]       ,		)  ->    List[Any]:
      super().__init__(**lowerCamelCase						)

      if text_config is None:
        lowerCAmelCase_     : int     =  {}
        logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values."""						)

      if vision_config is None:
        lowerCAmelCase_     : Dict     =  {}
        logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values."""						)

      lowerCAmelCase_     : Union[str, Any]     =  OwlViTTextConfig(**lowerCamelCase						)
      lowerCAmelCase_     : Optional[int]     =  OwlViTVisionConfig(**lowerCamelCase						)

      lowerCAmelCase_     : Optional[int]     =  projection_dim
      lowerCAmelCase_     : Tuple     =  logit_scale_init_value
      lowerCAmelCase_     : Any     =  return_dict
      lowerCAmelCase_     : List[str]     =  1.0

    @classmethod
    def __lowercase  (  cls       :    List[str]       ,		lowerCamelCase       :    Union[str, os.PathLike]       ,		**lowerCamelCase       :    Any						)  ->    "PretrainedConfig":
      cls._set_token_in_kwargs(lowerCamelCase						)

      lowerCAmelCase_,				lowerCAmelCase_     : Dict     =  cls.get_config_dict(lowerCamelCase       ,		**lowerCamelCase						)

      if "model_type" in config_dict and hasattr(cls       ,		"""model_type"""						) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

      return cls.from_dict(lowerCamelCase       ,		**lowerCamelCase						)

    @classmethod
    def __lowercase  (  cls       :    Dict       ,		lowerCamelCase       :    Dict       ,		lowerCamelCase       :    Dict       ,		**lowerCamelCase       :    Dict						)  ->    Dict:
      lowerCAmelCase_     : Tuple     =  {}
      lowerCAmelCase_     : Union[str, Any]     =  text_config
      lowerCAmelCase_     : List[Any]     =  vision_config

      return cls.from_dict(lowerCamelCase       ,		**lowerCamelCase						)





    def __lowercase  (  self       :    Tuple						)  ->    Optional[int]:
      lowerCAmelCase_     : Union[str, Any]     =  copy.deepcopy(self.__dict__						)
      lowerCAmelCase_     : Dict     =  self.text_config.to_dict()
      lowerCAmelCase_     : Optional[int]     =  self.vision_config.to_dict()
      lowerCAmelCase_     : Union[str, Any]     =  self.__class__.model_type
      return output







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @property
    def __lowercase  (  self       :    int						)  ->    Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ("""input_ids""", {0: """batch""", 1: """sequence"""}),
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
              ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
          ]						)

    @property
    def __lowercase  (  self       :    Union[str, Any]						)  ->    Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ("""logits_per_image""", {0: """batch"""}),
              ("""logits_per_text""", {0: """batch"""}),
              ("""text_embeds""", {0: """batch"""}),
              ("""image_embeds""", {0: """batch"""}),
          ]						)

    @property
    def __lowercase  (  self       :    List[Any]						)  ->    float:
      return 1E-4

    def __lowercase  (  self       :    Any       ,		lowerCamelCase       :    "ProcessorMixin"       ,		lowerCamelCase       :    int = -1       ,		lowerCamelCase       :    int = -1       ,		lowerCamelCase       :    Optional["TensorType"] = None       ,		)  ->    Mapping[str, Any]:
      lowerCAmelCase_     : Tuple     =  super().generate_dummy_inputs(
          processor.tokenizer       ,		batch_size=lowerCamelCase       ,		seq_length=lowerCamelCase       ,		framework=lowerCamelCase						)
      lowerCAmelCase_     : List[str]     =  super().generate_dummy_inputs(
          processor.image_processor       ,		batch_size=lowerCamelCase       ,		framework=lowerCamelCase						)
      return {**text_input_dict, **image_input_dict}





    @property
    def __lowercase  (  self       :    List[str]						)  ->    int:
      return 14







'''simple docstring'''






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__A   :    str											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  WavaVecaForSequenceClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : str     =  downstream_dict["""projector.weight"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""projector.bias"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""model.post_net.linear.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.post_net.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Any				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  WavaVecaForAudioFrameClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.linear.weight"""]
  lowerCAmelCase_     : List[str]     =  downstream_dict["""model.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[Any]     =  WavaVecaForXVector.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : List[Any]     =  downstream_dict["""connector.weight"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
    lowerCAmelCase_     : List[str]     =  downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    lowerCAmelCase_     : List[str]     =  downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  lowerCAmelCase_     : int     =  downstream_dict["""objective.W"""]
  return model







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  str				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  torch.load(A__				,       map_location="""cpu"""   )

  lowerCAmelCase_     : Optional[int]     =  checkpoint["""Downstream"""]

  lowerCAmelCase_     : int     =  WavaVecaConfig.from_pretrained(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  WavaVecaFeatureExtractor.from_pretrained(
      A__				,       return_attention_mask=A__				,       do_normalize=A__   )

  lowerCAmelCase_     : Optional[int]     =  hf_config.architectures[0]
  if arch.endswith("""ForSequenceClassification"""   ):
    lowerCAmelCase_     : List[str]     =  convert_classification(A__				,       A__				,       A__   )
  elif arch.endswith("""ForAudioFrameClassification"""   ):
    lowerCAmelCase_     : Dict     =  convert_diarization(A__				,       A__				,       A__   )
  elif arch.endswith("""ForXVector"""   ):
    lowerCAmelCase_     : List[Any]     =  convert_xvector(A__				,       A__				,       A__   )
  else:
    raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'   )

  if hf_config.use_weighted_layer_sum:
    lowerCAmelCase_     : Optional[int]     =  checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(A__   )
  hf_model.save_pretrained(A__   )


if __name__ == "__main__":
  __A   :    List[Any]											= 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   :    Tuple											= parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



