


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__a	 =      logging.get_logger("""transformers.models.speecht5""")
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->str:
   hf_model.apply_weight_norm()

   UpperCAmelCase      					=  checkpoint["""input_conv.weight_g"""]
   UpperCAmelCase      					=  checkpoint["""input_conv.weight_v"""]
   UpperCAmelCase      					=  checkpoint["""input_conv.bias"""]

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

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

   UpperCAmelCase      					=  checkpoint["""output_conv.1.weight_g"""]
   UpperCAmelCase      					=  checkpoint["""output_conv.1.weight_v"""]
   UpperCAmelCase      					=  checkpoint["""output_conv.1.bias"""]

   hf_model.remove_weight_norm()
@torch.no_grad()
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_=None	,    lowerCAmelCase_=None	,    )    ->Dict:
   if config_path is not None:
      UpperCAmelCase      					=  SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase_ )
   else:
      UpperCAmelCase      					=  SpeechTaHifiGanConfig()

   UpperCAmelCase      					=  SpeechTaHifiGan(lowerCAmelCase_ )

   UpperCAmelCase      					=  torch.load(lowerCAmelCase_ )
   load_weights(orig_checkpoint["""model"""]["""generator"""]	,    lowerCAmelCase_	,    lowerCAmelCase_ )

   UpperCAmelCase      					=  np.load(lowerCAmelCase_ )
   UpperCAmelCase      					=  stats[0].reshape(-1 )
   UpperCAmelCase      					=  stats[1].reshape(-1 )
   UpperCAmelCase      					=  torch.from_numpy(lowerCAmelCase_ ).float()
   UpperCAmelCase      					=  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__":
 __a	 =      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	 =      parser.parse_args()
 convert_hifigan_checkpoint(
     args.checkpoint_path,
     args.stats_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__a	 =      logging.get_logger(__name__)

__a	 =      {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							'''mobilenet_v1'''





      def __init__(		self    :       Any       ,							__lowerCamelCase    :       Union[str, Any]=3       ,							__lowerCamelCase    :       Dict=2_2_4       ,							__lowerCamelCase    :       Union[str, Any]=1.0       ,							__lowerCamelCase    :       Optional[int]=8       ,							__lowerCamelCase    :       Optional[int]="relu6"       ,							__lowerCamelCase    :       Optional[int]=True       ,							__lowerCamelCase    :       List[str]=0.999       ,							__lowerCamelCase    :       Optional[Any]=0.02       ,							__lowerCamelCase    :       str=0.001       ,							**__lowerCamelCase    :       Optional[Any]       ,							)     -> Optional[int]:






         """simple docstring"""







         super().__init__(**__lowerCamelCase							)

         if depth_multiplier <= 0:
            raise ValueError("""depth_multiplier must be greater than zero."""							)

         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  depth_multiplier
         UpperCAmelCase      					=  min_depth
         UpperCAmelCase      					=  hidden_act
         UpperCAmelCase      					=  tf_padding
         UpperCAmelCase      					=  classifier_dropout_prob
         UpperCAmelCase      					=  initializer_range
         UpperCAmelCase      					=  layer_norm_eps






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							version.parse('''1.11'''	)





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> Mapping[str, Mapping[int, str]]:






         """simple docstring"""







         return OrderedDict([("""pixel_values""", {0: """batch"""})]							)





      @property
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Mapping[str, Mapping[int, str]]:






         """simple docstring"""







         if self.task == "image-classification":
            return OrderedDict([("""logits""", {0: """batch"""})]							)
         else:
            return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> float:






         """simple docstring"""







         return 1e-4



