'''simple docstring'''




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_a   :	Optional[Any] 							=   logging.get_logger("transformers.models.speecht5")

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       hf_model.apply_weight_norm()

       __UpperCAmelCase  :				int       				=		checkpoint["input_conv.weight_g"]
       __UpperCAmelCase  :				Tuple       				=		checkpoint["input_conv.weight_v"]
       __UpperCAmelCase  :				str       				=		checkpoint["input_conv.bias"]

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

                     __UpperCAmelCase  :				Union[str, Any]       				=		checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
                     __UpperCAmelCase  :				Any       				=		checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
                     __UpperCAmelCase  :				Tuple       				=		checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

       __UpperCAmelCase  :				List[Any]       				=		checkpoint["output_conv.1.weight_g"]
       __UpperCAmelCase  :				Union[str, Any]       				=		checkpoint["output_conv.1.weight_v"]
       __UpperCAmelCase  :				int       				=		checkpoint["output_conv.1.bias"]

       hf_model.remove_weight_norm()

@torch.no_grad()
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=None							,    lowerCamelCase__=None							,    )					->       Union[str, Any]:





       """simple docstring"""


       if config_path is not None:
              __UpperCAmelCase  :				Dict       				=		SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase__						)
       else:
              __UpperCAmelCase  :				Optional[int]       				=		SpeechTaHifiGanConfig()

       __UpperCAmelCase  :				Optional[int]       				=		SpeechTaHifiGan(lowerCamelCase__						)

       __UpperCAmelCase  :				int       				=		torch.load(lowerCamelCase__						)
       load_weights(orig_checkpoint["model"]["generator"]							,    lowerCamelCase__							,    lowerCamelCase__						)

       __UpperCAmelCase  :				str       				=		np.load(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		stats[0].reshape(-1						)
       __UpperCAmelCase  :				Union[str, Any]       				=		stats[1].reshape(-1						)
       __UpperCAmelCase  :				Optional[int]       				=		torch.from_numpy(lowerCamelCase__						).float()
       __UpperCAmelCase  :				int       				=		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   :	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."
   )

   _a   :	str 							=   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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_a   :	str 							=   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	str 							=   ["ViTFeatureExtractor"]
   _a   :	Dict 							=   ["ViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ViTForImageClassification",
       "ViTForMaskedImageModeling",
       "ViTModel",
       "ViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[str] 							=   [
       "TFViTForImageClassification",
       "TFViTModel",
       "TFViTPreTrainedModel",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   [
       "FlaxViTForImageClassification",
       "FlaxViTModel",
       "FlaxViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

   _a   :	Dict 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






