"""simple docstring"""



from typing import Mapping

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


SCREAMING_SNAKE_CASE__:int			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Tuple			=	{
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[str]           =		"""t5"""
 _snake_case   :			Tuple           =		["""past_key_values"""]
 _snake_case   :			Optional[int]           =		{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


 def __init__(				self  ,						lowerCamelCase=32128  ,						lowerCamelCase=512  ,						lowerCamelCase=64  ,						lowerCamelCase=2048  ,						lowerCamelCase=6  ,						lowerCamelCase=None  ,						lowerCamelCase=8  ,						lowerCamelCase=32  ,						lowerCamelCase=128  ,						lowerCamelCase=0.1  ,						lowerCamelCase=1E-6  ,						lowerCamelCase=1.0  ,						lowerCamelCase="relu"  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=0  ,						lowerCamelCase=1  ,						**lowerCamelCase  ,						):
        __a     				=  vocab_size
        __a     				=  d_model
        __a     				=  d_kv
        __a     				=  d_ff
        __a     				=  num_layers
        __a     				=  (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        __a     				=  num_heads
        __a     				=  relative_attention_num_buckets
        __a     				=  relative_attention_max_distance
        __a     				=  dropout_rate
        __a     				=  layer_norm_epsilon
        __a     				=  initializer_factor
        __a     				=  feed_forward_proj
        __a     				=  use_cache

        __a     				=  self.feed_forward_proj.split("-"		)
        __a     				=  act_info[-1]
        __a     				=  act_info[0] == "gated"

        if len(lowerCamelCase		) > 1 and act_info[0] != "gated" or len(lowerCamelCase		) > 2:
               raise ValueError(
                   F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                   "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                   "'gated-gelu' or 'relu'"		)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
               __a     				=  "gelu_new"

        super().__init__(
            pad_token_id=lowerCamelCase  ,						eos_token_id=lowerCamelCase  ,						is_encoder_decoder=lowerCamelCase  ,						**lowerCamelCase  ,						)






class 				snake_case__     (   snake_case_   ):


 @property
 def 		a__ (				self		):
        __a     				=  {
            "input_ids": {0: "batch", 1: "encoder_sequence"},
            "attention_mask": {0: "batch", 1: "encoder_sequence"},
        }
        if self.use_past:
               __a     				=  "past_encoder_sequence + sequence"
               __a     				=  {0: "batch"}
               __a     				=  {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
               __a     				=  {0: "batch", 1: "decoder_sequence"}
               __a     				=  {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
               self.fill_with_past_key_values_(lowerCamelCase  ,						direction="inputs"		)

        return common_inputs






 @property
 def 		a__ (				self		):
        return 13

"""simple docstring"""



import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__:Dict			=	logging.get_logger(__name__)



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForSequenceClassification.from_pretrained(a ,  config=a							)
       __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"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForAudioFrameClassification.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["model.linear.weight"]
       __a     				=  downstream_dict["model.linear.bias"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForXVector.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["connector.weight"]
       __a     				=  downstream_dict["connector.bias"]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
              __a     				=  downstream_dict[
                  F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
              ]
              __a     				=  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
       __a     				=  downstream_dict["objective.W"]
       return model



@torch.no_grad()
def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  torch.load(a ,  map_location="cpu"							)

       __a     				=  checkpoint["Downstream"]

       __a     				=  WavaVecaConfig.from_pretrained(a							)
       __a     				=  WavaVecaFeatureExtractor.from_pretrained(
           a ,  return_attention_mask=a ,  do_normalize=a							)

       __a     				=  hf_config.architectures[0]
       if arch.endswith("ForSequenceClassification"							):
              __a     				=  convert_classification(a ,  a ,  a							)
       elif arch.endswith("ForAudioFrameClassification"							):
              __a     				=  convert_diarization(a ,  a ,  a							)
       elif arch.endswith("ForXVector"							):
              __a     				=  convert_xvector(a ,  a ,  a							)
       else:
              raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"							)

       if hf_config.use_weighted_layer_sum:
              __a     				=  checkpoint["Featurizer"]["weights"]

       hf_feature_extractor.save_pretrained(a							)
       hf_model.save_pretrained(a							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Union[str, 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.""")
   SCREAMING_SNAKE_CASE__:Any			=	parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
