

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[Any]:
	hf_model.apply_weight_norm()

	__lowerCamelCase    :    List[Any] 	=			checkpoint['input_conv.weight_g']
	__lowerCamelCase    :    Optional[int] 	=			checkpoint['input_conv.weight_v']
	__lowerCamelCase    :    int 	=			checkpoint['input_conv.bias']

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

			__lowerCamelCase    :    str 	=			checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
			__lowerCamelCase    :    int 	=			checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
			__lowerCamelCase    :    Optional[Any] 	=			checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

	__lowerCamelCase    :    Optional[Any] 	=			checkpoint['output_conv.1.weight_g']
	__lowerCamelCase    :    Optional[Any] 	=			checkpoint['output_conv.1.weight_v']
	__lowerCamelCase    :    List[str] 	=			checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()






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

	__lowerCamelCase    :    int 	=			SpeechTaHifiGan(lowerCamelCase__      )

	__lowerCamelCase    :    Union[str, Any] 	=			torch.load(lowerCamelCase__      )
	load_weights(orig_checkpoint['model']['generator']     ,			lowerCamelCase__     ,			lowerCamelCase__      )

	__lowerCamelCase    :    Dict 	=			np.load(lowerCamelCase__      )
	__lowerCamelCase    :    List[str] 	=			stats[0].reshape(-1      )
	__lowerCamelCase    :    List[Any] 	=			stats[1].reshape(-1      )
	__lowerCamelCase    :    List[str] 	=			torch.from_numpy(lowerCamelCase__      ).float()
	__lowerCamelCase    :    List[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__":
					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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		={
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




