





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class  A__       (	__snake_case      ):
      _UpperCAmelCase     :torch.FloatTensor



class  A__       (	__snake_case	,       __snake_case      ):





      @register_to_config
      def __init__(			self	,							A_ = 6_5536	,							A_ = None	,							A_ = 2	,							A_ = 2	,							A_ = 0	,							A_ = "fourier"	,							A_ = True	,							A_ = False	,							A_ = 0.0	,							A_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")	,							A_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")	,							A_ = "UNetMidBlock1D"	,							A_ = None	,							A_ = (32, 32, 64)	,							A_ = None	,							A_ = 8	,							A_ = 1	,							A_ = False	,							):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase     : int	     =       sample_size

       # time
       if time_embedding_type == "fourier":
        UpperCamelCase     : List[str]	     =       GaussianFourierProjection(
            embedding_size=8	,							set_W_to_weight=A_	,							log=A_	,							flip_sin_to_cos=A_       )
        UpperCamelCase     : List[str]	     =       2 * block_out_channels[0]
       elif time_embedding_type == "positional":
        UpperCamelCase     : Tuple	     =       Timesteps(
            block_out_channels[0]	,							flip_sin_to_cos=A_	,							downscale_freq_shift=A_       )
        UpperCamelCase     : Optional[Any]	     =       block_out_channels[0]

       if use_timestep_embedding:
        UpperCamelCase     : List[Any]	     =       block_out_channels[0] * 4
        UpperCamelCase     : int	     =       TimestepEmbedding(
            in_channels=A_	,							time_embed_dim=A_	,							act_fn=A_	,							out_dim=block_out_channels[0]	,							)

       UpperCamelCase     : Optional[int]	     =       nn.ModuleList([]       )
       UpperCamelCase     : Any	     =       None
       UpperCamelCase     : Optional[Any]	     =       nn.ModuleList([]       )
       UpperCamelCase     : Optional[Any]	     =       None

       # down
       UpperCamelCase     : Optional[int]	     =       in_channels
       for i, down_block_type in enumerate(A_       ):
        UpperCamelCase     : int	     =       output_channel
        UpperCamelCase     : str	     =       block_out_channels[i]

        if i == 0:
         input_channel += extra_in_channels

        UpperCamelCase     : Optional[Any]	     =       i == len(A_       ) - 1

        UpperCamelCase     : Any	     =       get_down_block(
            A_	,							num_layers=A_	,							in_channels=A_	,							out_channels=A_	,							temb_channels=block_out_channels[0]	,							add_downsample=not is_final_block or downsample_each_block	,							)
        self.down_blocks.append(A_       )

       # mid
       UpperCamelCase     : int	     =       get_mid_block(
           A_	,							in_channels=block_out_channels[-1]	,							mid_channels=block_out_channels[-1]	,							out_channels=block_out_channels[-1]	,							embed_dim=block_out_channels[0]	,							num_layers=A_	,							add_downsample=A_	,							)

       # up
       UpperCamelCase     : List[Any]	     =       list(reversed(A_       )       )
       UpperCamelCase     : List[Any]	     =       reversed_block_out_channels[0]
       if out_block_type is None:
        UpperCamelCase     : List[Any]	     =       out_channels
       else:
        UpperCamelCase     : List[str]	     =       block_out_channels[0]

       for i, up_block_type in enumerate(A_       ):
        UpperCamelCase     : Optional[Any]	     =       output_channel
        UpperCamelCase     : Optional[int]	     =       (
            reversed_block_out_channels[i + 1] if i < len(A_       ) - 1 else final_upsample_channels
        )

        UpperCamelCase     : Optional[Any]	     =       i == len(A_       ) - 1

        UpperCamelCase     : int	     =       get_up_block(
            A_	,							num_layers=A_	,							in_channels=A_	,							out_channels=A_	,							temb_channels=block_out_channels[0]	,							add_upsample=not is_final_block	,							)
        self.up_blocks.append(A_       )
        UpperCamelCase     : str	     =       output_channel

       # out
       UpperCamelCase     : Union[str, Any]	     =       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,							32       )
       UpperCamelCase     : Tuple	     =       get_out_block(
           out_block_type=A_	,							num_groups_out=A_	,							embed_dim=block_out_channels[0]	,							out_channels=A_	,							act_fn=A_	,							fc_dim=block_out_channels[-1] // 4	,							)





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_ = True	,							):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       timestep
       if not torch.is_tensor(A_       ):
        UpperCamelCase     : Any	     =       torch.tensor([timesteps]	,							dtype=torch.long	,							device=sample.device       )
       elif torch.is_tensor(A_       ) and len(timesteps.shape       ) == 0:
        UpperCamelCase     : str	     =       timesteps[None].to(sample.device       )

       UpperCamelCase     : str	     =       self.time_proj(A_       )
       if self.config.use_timestep_embedding:
        UpperCamelCase     : List[Any]	     =       self.time_mlp(A_       )
       else:
        UpperCamelCase     : Dict	     =       timestep_embed[..., None]
        UpperCamelCase     : int	     =       timestep_embed.repeat([1, 1, sample.shape[2]]       ).to(sample.dtype       )
        UpperCamelCase     : Tuple	     =       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])       )

       # 2. down
       UpperCamelCase     : Any	     =       ()
       for downsample_block in self.down_blocks:
        UpperCamelCase     ,   UpperCamelCase     : Any	     =       downsample_block(hidden_states=A_	,							temb=A_       )
        down_block_res_samples += res_samples

       # 3. mid
       if self.mid_block:
        UpperCamelCase     : Optional[Any]	     =       self.mid_block(A_	,							A_       )

       # 4. up
       for i, upsample_block in enumerate(self.up_blocks       ):
        UpperCamelCase     : List[str]	     =       down_block_res_samples[-1:]
        UpperCamelCase     : Optional[Any]	     =       down_block_res_samples[:-1]
        UpperCamelCase     : List[str]	     =       upsample_block(A_	,							res_hidden_states_tuple=A_	,							temb=A_       )

       # 5. post-process
       if self.out_block:
        UpperCamelCase     : List[str]	     =       self.out_block(A_	,							A_       )

       if not return_dict:
        return (sample,)

       return UNetaDOutput(sample=A_       )







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,
    )
