





from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase	:					int			  =					logging.get_logger(__name__)

# TODO Update this
__lowerCamelCase	:					List[str]			  =					{
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Union[str, Any]						   =       'esm'





      def __init__(			self	,							A_=None	,							A_=None	,							A_=None	,							A_=768	,							A_=12	,							A_=12	,							A_=3072	,							A_=0.1	,							A_=0.1	,							A_=1026	,							A_=0.02	,							A_=1e-12	,							A_="absolute"	,							A_=True	,							A_=None	,							A_=False	,							A_=False	,							A_=None	,							A_=None	,							**A_	,							):
       '''simple docstring'''




       super().__init__(pad_token_id=A_	,							mask_token_id=A_	,							**A_       )

       UpperCamelCase     : Union[str, Any]	     =       vocab_size
       UpperCamelCase     : List[str]	     =       hidden_size
       UpperCamelCase     : Any	     =       num_hidden_layers
       UpperCamelCase     : Optional[int]	     =       num_attention_heads
       UpperCamelCase     : Union[str, Any]	     =       intermediate_size
       UpperCamelCase     : Tuple	     =       hidden_dropout_prob
       UpperCamelCase     : List[Any]	     =       attention_probs_dropout_prob
       UpperCamelCase     : Optional[int]	     =       max_position_embeddings
       UpperCamelCase     : str	     =       initializer_range
       UpperCamelCase     : Optional[int]	     =       layer_norm_eps
       UpperCamelCase     : str	     =       position_embedding_type
       UpperCamelCase     : Dict	     =       use_cache
       UpperCamelCase     : int	     =       emb_layer_norm_before
       UpperCamelCase     : List[Any]	     =       token_dropout
       UpperCamelCase     : str	     =       is_folding_model
       if is_folding_model:
        if esmfold_config is None:
         logger.info("No esmfold_config supplied for folding model, using default values."       )
         UpperCamelCase     : Union[str, Any]	     =       EsmFoldConfig()
        elif isinstance(A_	,							A_       ):
         UpperCamelCase     : List[Any]	     =       EsmFoldConfig(**A_       )
        UpperCamelCase     : Optional[Any]	     =       esmfold_config
        if vocab_list is None:
         logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"       )
         UpperCamelCase     : List[Any]	     =       get_default_vocab_list()
        else:
         UpperCamelCase     : Dict	     =       vocab_list
       else:
        UpperCamelCase     : Optional[int]	     =       None
        UpperCamelCase     : Any	     =       None
       if self.esmfold_config is not None and getattr(self.esmfold_config	,							"use_esm_attn_map"	,							A_       ):
        raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       super().to_dict()
       if isinstance(self.esmfold_config	,							A_       ):
        UpperCamelCase     : Union[str, Any]	     =       self.esmfold_config.to_dict()
       return output



@dataclass
class  A__       :
      _UpperCAmelCase     :str						   =       None
      _UpperCAmelCase     :bool						   =       True
      _UpperCAmelCase     :bool						   =       False
      _UpperCAmelCase     :bool						   =       False
      _UpperCAmelCase     :bool						   =       False
      _UpperCAmelCase     :float						   =       0

      _UpperCAmelCase     :bool						   =       True
      _UpperCAmelCase     :bool						   =       False

      _UpperCAmelCase     :int						   =       1_2_8
      _UpperCAmelCase     :"TrunkConfig"						   =       None





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       if self.trunk is None:
        UpperCamelCase     : Optional[int]	     =       TrunkConfig()
       elif isinstance(self.trunk	,							A_       ):
        UpperCamelCase     : Any	     =       TrunkConfig(**self.trunk       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       asdict(self       )
       UpperCamelCase     : Optional[int]	     =       self.trunk.to_dict()
       return output



@dataclass
class  A__       :
      _UpperCAmelCase     :int						   =       4_8
      _UpperCAmelCase     :int						   =       1_0_2_4
      _UpperCAmelCase     :int						   =       1_2_8
      _UpperCAmelCase     :int						   =       3_2
      _UpperCAmelCase     :int						   =       3_2
      _UpperCAmelCase     :int						   =       3_2
      _UpperCAmelCase     :float						   =       0
      _UpperCAmelCase     :float						   =       0
      _UpperCAmelCase     :bool						   =       False
      _UpperCAmelCase     :int						   =       4
      _UpperCAmelCase     :Optional[int]						   =       1_2_8
      _UpperCAmelCase     :"StructureModuleConfig"						   =       None





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       if self.structure_module is None:
        UpperCamelCase     : List[str]	     =       StructureModuleConfig()
       elif isinstance(self.structure_module	,							A_       ):
        UpperCamelCase     : str	     =       StructureModuleConfig(**self.structure_module       )

       if self.max_recycles <= 0:
        raise ValueError(F"""`max_recycles` should be positive, got {self.max_recycles}."""       )
       if self.sequence_state_dim % self.sequence_state_dim != 0:
        raise ValueError(
            "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
            F""" {self.sequence_state_dim} and {self.sequence_state_dim}."""       )
       if self.pairwise_state_dim % self.pairwise_state_dim != 0:
        raise ValueError(
            "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
            F""" {self.pairwise_state_dim} and {self.pairwise_state_dim}."""       )

       UpperCamelCase     : Tuple	     =       self.sequence_state_dim // self.sequence_head_width
       UpperCamelCase     : Tuple	     =       self.pairwise_state_dim // self.pairwise_head_width

       if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
        raise ValueError(
            "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
            F""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."""       )
       if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
        raise ValueError(
            "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
            F""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."""       )
       if self.pairwise_state_dim % 2 != 0:
        raise ValueError(F"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."""       )

       if self.dropout >= 0.4:
        raise ValueError(F"""`dropout` should not be greater than 0.4, got {self.dropout}."""       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       asdict(self       )
       UpperCamelCase     : Optional[int]	     =       self.structure_module.to_dict()
       return output



@dataclass
class  A__       :
      _UpperCAmelCase     :int						   =       3_8_4
      _UpperCAmelCase     :int						   =       1_2_8
      _UpperCAmelCase     :int						   =       1_6
      _UpperCAmelCase     :int						   =       1_2_8
      _UpperCAmelCase     :int						   =       1_2
      _UpperCAmelCase     :int						   =       4
      _UpperCAmelCase     :int						   =       8
      _UpperCAmelCase     :float						   =       0.1
      _UpperCAmelCase     :int						   =       8
      _UpperCAmelCase     :int						   =       1
      _UpperCAmelCase     :int						   =       2
      _UpperCAmelCase     :int						   =       7
      _UpperCAmelCase     :int						   =       1_0
      _UpperCAmelCase     :float						   =       1e-8
      _UpperCAmelCase     :float						   =       1e5





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return asdict(self       )



def 		A_    (					)						->			str:
 return (
     "<cls>",
     "<pad>",
     "<eos>",
     "<unk>",
     "L",
     "A",
     "G",
     "V",
     "S",
     "E",
     "R",
     "T",
     "I",
     "D",
     "P",
     "K",
     "Q",
     "N",
     "F",
     "Y",
     "M",
     "H",
     "W",
     "C",
     "X",
     "B",
     "U",
     "Z",
     "O",
     ".",
     "-",
     "<null_1>",
     "<mask>",
 )







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCamelCase	:					Dict			  =					logging.get_logger("""transformers.models.speecht5""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 hf_model.apply_weight_norm()

 UpperCamelCase     : int	     =       checkpoint["input_conv.weight_g"]
 UpperCamelCase     : Dict	     =       checkpoint["input_conv.weight_v"]
 UpperCamelCase     : List[Any]	     =       checkpoint["input_conv.bias"]

 for i in range(len(config.upsample_rates					)					):
  UpperCamelCase     : Any	     =       checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCamelCase     : List[Any]	     =       checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""upsamples.{i}.1.bias"""]

 for i in range(len(config.upsample_rates					) * len(config.resblock_kernel_sizes					)					):
  for j in range(len(config.resblock_dilation_sizes					)					):
   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCamelCase     : str	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_g"]
 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_v"]
 UpperCamelCase     : int	     =       checkpoint["output_conv.1.bias"]

 hf_model.remove_weight_norm()



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Tuple:
 if config_path is not None:
  UpperCamelCase     : List[Any]	     =       SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase					)
 else:
  UpperCamelCase     : Optional[int]	     =       SpeechTaHifiGanConfig()

 UpperCamelCase     : List[str]	     =       SpeechTaHifiGan(_lowerCAmelCase					)

 UpperCamelCase     : str	     =       torch.load(_lowerCAmelCase					)
 load_weights(orig_checkpoint["model"]["generator"]							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : List[Any]	     =       np.load(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       stats[0].reshape(-1					)
 UpperCamelCase     : Tuple	     =       stats[1].reshape(-1					)
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()

 model.save_pretrained(_lowerCAmelCase					)

 if repo_id:
  print("Pushing to the hub..."					)
  model.push_to_hub(_lowerCAmelCase					)


if __name__ == "__main__":
    __lowerCamelCase	:					Union[str, Any]			  =					argparse.ArgumentParser()
    parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
    parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
    parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
    parser.add_argument(
        """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
    )

    __lowerCamelCase	:					Tuple			  =					parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )
