

from dataclasses import asdict, dataclass
from typing import Optional

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


a_    :						Dict    		=      logging.get_logger(__name__)

# TODO Update this
a_    :						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      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="esm"

    def __init__(			self :      Optional[Any]							,    snake_case__ :      List[str]=None							,    snake_case__ :      Union[str, Any]=None							,    snake_case__ :      int=None							,    snake_case__ :      Union[str, Any]=7_6_8							,    snake_case__ :      Dict=1_2							,    snake_case__ :      str=1_2							,    snake_case__ :      Tuple=3_0_7_2							,    snake_case__ :      Optional[int]=0.1							,    snake_case__ :      str=0.1							,    snake_case__ :      List[Any]=1_0_2_6							,    snake_case__ :      Union[str, Any]=0.02							,    snake_case__ :      Tuple=1E-12							,    snake_case__ :      Optional[Any]="absolute"							,    snake_case__ :      int=True							,    snake_case__ :      List[Any]=None							,    snake_case__ :      Optional[int]=False							,    snake_case__ :      str=False							,    snake_case__ :      Any=None							,    snake_case__ :      Optional[int]=None							,    **snake_case__ :      Optional[int]							,    ):

     """simple docstring"""






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

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




    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			super().to_dict()
     if isinstance(self.esmfold_config							,    snake_case__    ):
      SCREAMING_SNAKE_CASE								=			self.esmfold_config.to_dict()
     return output




@dataclass
class      UpperCamelCase :
    __UpperCamelCase         =None
    __UpperCamelCase         =True
    __UpperCamelCase         =False
    __UpperCamelCase         =False
    __UpperCamelCase         =False
    __UpperCamelCase         =0

    __UpperCamelCase         =True
    __UpperCamelCase         =False

    __UpperCamelCase         =1_28
    __UpperCamelCase         =None

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     if self.trunk is None:
      SCREAMING_SNAKE_CASE								=			TrunkConfig()
     elif isinstance(self.trunk							,    snake_case__    ):
      SCREAMING_SNAKE_CASE								=			TrunkConfig(**self.trunk    )




    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			asdict(self    )
     SCREAMING_SNAKE_CASE								=			self.trunk.to_dict()
     return output




@dataclass
class      UpperCamelCase :
    __UpperCamelCase         =48
    __UpperCamelCase         =10_24
    __UpperCamelCase         =1_28
    __UpperCamelCase         =32
    __UpperCamelCase         =32
    __UpperCamelCase         =32
    __UpperCamelCase         =0
    __UpperCamelCase         =0
    __UpperCamelCase         =False
    __UpperCamelCase         =4
    __UpperCamelCase         =1_28
    __UpperCamelCase         =None

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     if self.structure_module is None:
      SCREAMING_SNAKE_CASE								=			StructureModuleConfig()
     elif isinstance(self.structure_module							,    snake_case__    ):
      SCREAMING_SNAKE_CASE								=			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}."""    )

     SCREAMING_SNAKE_CASE								=			self.sequence_state_dim // self.sequence_head_width
     SCREAMING_SNAKE_CASE								=			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 :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			asdict(self    )
     SCREAMING_SNAKE_CASE								=			self.structure_module.to_dict()
     return output




@dataclass
class      UpperCamelCase :
    __UpperCamelCase         =3_84
    __UpperCamelCase         =1_28
    __UpperCamelCase         =16
    __UpperCamelCase         =1_28
    __UpperCamelCase         =12
    __UpperCamelCase         =4
    __UpperCamelCase         =8
    __UpperCamelCase         =0.1
    __UpperCamelCase         =8
    __UpperCamelCase         =1
    __UpperCamelCase         =2
    __UpperCamelCase         =7
    __UpperCamelCase         =10
    __UpperCamelCase         =1e-8
    __UpperCamelCase         =1e5

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     return asdict(self    )


def        __lowerCAmelCase    (       )		->							Dict:
 '''simple docstring'''


 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()
a_    :						List[Any]    		=      logging.get_logger("transformers.models.speecht5")


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Any	)		->							Dict:
 '''simple docstring'''


 hf_model.apply_weight_norm()

 SCREAMING_SNAKE_CASE								=			checkpoint['input_conv.weight_g']
 SCREAMING_SNAKE_CASE								=			checkpoint['input_conv.weight_v']
 SCREAMING_SNAKE_CASE								=			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates	)	):
  SCREAMING_SNAKE_CASE								=			checkpoint[f"""upsamples.{i}.1.weight_g"""]
  SCREAMING_SNAKE_CASE								=			checkpoint[f"""upsamples.{i}.1.weight_v"""]
  SCREAMING_SNAKE_CASE								=			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	)	):
   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
   SCREAMING_SNAKE_CASE								=			checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

 SCREAMING_SNAKE_CASE								=			checkpoint['output_conv.1.weight_g']
 SCREAMING_SNAKE_CASE								=			checkpoint['output_conv.1.weight_v']
 SCREAMING_SNAKE_CASE								=			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Any=None		,							_UpperCamelCase		:  List[str]=None		,							)		->							Tuple:
 '''simple docstring'''


 if config_path is not None:
  SCREAMING_SNAKE_CASE								=			SpeechTaHifiGanConfig.from_pretrained(_UpperCamelCase	)
 else:
  SCREAMING_SNAKE_CASE								=			SpeechTaHifiGanConfig()

 SCREAMING_SNAKE_CASE								=			SpeechTaHifiGan(_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			torch.load(_UpperCamelCase	)
 load_weights(orig_checkpoint['model']['generator']		,							_UpperCamelCase		,							_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			np.load(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			stats[0].reshape(-1	)
 SCREAMING_SNAKE_CASE								=			stats[1].reshape(-1	)
 SCREAMING_SNAKE_CASE								=			torch.from_numpy(_UpperCamelCase	).float()
 SCREAMING_SNAKE_CASE								=			torch.from_numpy(_UpperCamelCase	).float()

 model.save_pretrained(_UpperCamelCase	)

 if repo_id:
  print('Pushing to the hub...'	)
  model.push_to_hub(_UpperCamelCase	)


if __name__ == "__main__":
      a_    :						Dict    		=      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."
      )

      a_    :						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,
      )

