





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_UpperCAmelCase				:       str	   =				logging.get_logger("""transformers.models.speecht5""")


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Union[str, Any]:
    hf_model.apply_weight_norm()

    lowerCamelCase__    :      str					 =		checkpoint['input_conv.weight_g']
    lowerCamelCase__    :      List[Any]					 =		checkpoint['input_conv.weight_v']
    lowerCamelCase__    :      Union[str, Any]					 =		checkpoint['input_conv.bias']

    for i in range(len(config.upsample_rates ) ):
        lowerCamelCase__    :      Any					 =		checkpoint[F"""upsamples.{i}.1.weight_g"""]
        lowerCamelCase__    :      Optional[Any]					 =		checkpoint[F"""upsamples.{i}.1.weight_v"""]
        lowerCamelCase__    :      int					 =		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 ) ):
            lowerCamelCase__    :      str					 =		checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
            lowerCamelCase__    :      Optional[int]					 =		checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
            lowerCamelCase__    :      Optional[Any]					 =		checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

            lowerCamelCase__    :      Tuple					 =		checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
            lowerCamelCase__    :      str					 =		checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
            lowerCamelCase__    :      List[Any]					 =		checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

    lowerCamelCase__    :      Tuple					 =		checkpoint['output_conv.1.weight_g']
    lowerCamelCase__    :      Dict					 =		checkpoint['output_conv.1.weight_v']
    lowerCamelCase__    :      int					 =		checkpoint['output_conv.1.bias']

    hf_model.remove_weight_norm()


@torch.no_grad()
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=None						,	_UpperCAmelCase=None						,	)			->     List[Any]:
    if config_path is not None:
        lowerCamelCase__    :      List[Any]					 =		SpeechTaHifiGanConfig.from_pretrained(_UpperCAmelCase )
    else:
        lowerCamelCase__    :      Any					 =		SpeechTaHifiGanConfig()

    lowerCamelCase__    :      Tuple					 =		SpeechTaHifiGan(_UpperCAmelCase )

    lowerCamelCase__    :      Optional[Any]					 =		torch.load(_UpperCAmelCase )
    load_weights(orig_checkpoint['model']['generator']						,	_UpperCAmelCase						,	_UpperCAmelCase )

    lowerCamelCase__    :      List[str]					 =		np.load(_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		stats[0].reshape(-1 )
    lowerCamelCase__    :      Optional[Any]					 =		stats[1].reshape(-1 )
    lowerCamelCase__    :      int					 =		torch.from_numpy(_UpperCAmelCase ).float()
    lowerCamelCase__    :      Dict					 =		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__":
    _UpperCAmelCase				:       List[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."""
    )

    _UpperCAmelCase				:       Optional[Any]	   =				parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )









"""simple docstring"""







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


__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       """dpr"""






    def __init__(					self				:    List[Any]				, __UpperCAmelCase				:    int=30522				, __UpperCAmelCase				:    Union[str, Any]=768				, __UpperCAmelCase				:    Dict=12				, __UpperCAmelCase				:    List[str]=12				, __UpperCAmelCase				:    Any=3072				, __UpperCAmelCase				:    Optional[int]="gelu"				, __UpperCAmelCase				:    Any=0.1				, __UpperCAmelCase				:    Union[str, Any]=0.1				, __UpperCAmelCase				:    str=512				, __UpperCAmelCase				:    List[str]=2				, __UpperCAmelCase				:    Tuple=0.02				, __UpperCAmelCase				:    List[str]=1e-12				, __UpperCAmelCase				:    List[str]=0				, __UpperCAmelCase				:    str="absolute"				, __UpperCAmelCase				:    int = 0				, **__UpperCAmelCase				:    Tuple				, ):
      super().__init__(pad_token_id=__UpperCAmelCase				, **__UpperCAmelCase)

      a						:		List[Any]      							=			vocab_size
      a						:		Optional[Any]      							=			hidden_size
      a						:		Union[str, Any]      							=			num_hidden_layers
      a						:		Dict      							=			num_attention_heads
      a						:		int      							=			hidden_act
      a						:		Any      							=			intermediate_size
      a						:		Any      							=			hidden_dropout_prob
      a						:		Dict      							=			attention_probs_dropout_prob
      a						:		Any      							=			max_position_embeddings
      a						:		Union[str, Any]      							=			type_vocab_size
      a						:		Optional[Any]      							=			initializer_range
      a						:		Dict      							=			layer_norm_eps
      a						:		int      							=			projection_dim
      a						:		str      							=			position_embedding_type






