


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Any:
					'''simple docstring'''





					hf_model.apply_weight_norm()

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

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

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

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

					hf_model.remove_weight_norm()




@torch.no_grad()
def     UpperCamelCase    (      _a       ,		_a       ,		_a       ,		_a=None       ,		_a=None       ,		)					->							str:
					'''simple docstring'''





					if config_path is not None:
										lowercase_       :Any     =       SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase__		)
					else:
										lowercase_       :Optional[Any]     =       SpeechTaHifiGanConfig()

					lowercase_       :Union[str, Any]     =       SpeechTaHifiGan(lowerCAmelCase__		)

					lowercase_       :Optional[int]     =       torch.load(lowerCAmelCase__		)
					load_weights(orig_checkpoint['''model''']['''generator''']       ,		lowerCAmelCase__       ,		lowerCAmelCase__		)

					lowercase_       :Optional[Any]     =       np.load(lowerCAmelCase__		)
					lowercase_       :str     =       stats[0].reshape(-1		)
					lowercase_       :List[str]     =       stats[1].reshape(-1		)
					lowercase_       :Any     =       torch.from_numpy(lowerCAmelCase__		).float()
					lowercase_       :List[str]     =       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__":
			SCREAMING_SNAKE_CASE       :		str         =				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."
			)

			SCREAMING_SNAKE_CASE       :		Optional[int]         =				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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE       :		str         =				{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		Dict         =				[
       "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TapasForMaskedLM",
       "TapasForQuestionAnswering",
       "TapasForSequenceClassification",
       "TapasModel",
       "TapasPreTrainedModel",
       "load_tf_weights_in_tapas",
   ]
try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		str         =				[
       "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFTapasForMaskedLM",
       "TFTapasForQuestionAnswering",
       "TFTapasForSequenceClassification",
       "TFTapasModel",
       "TFTapasPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
   from .tokenization_tapas import TapasTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tapas import (
          TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TapasForMaskedLM,
          TapasForQuestionAnswering,
          TapasForSequenceClassification,
          TapasModel,
          TapasPreTrainedModel,
          load_tf_weights_in_tapas,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_tapas import (
          TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTapasForMaskedLM,
          TFTapasForQuestionAnswering,
          TFTapasForSequenceClassification,
          TFTapasModel,
          TFTapasPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

