





"""simple docstring"""


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__magic_name__       :					int						=			logging.get_logger("""transformers.models.speecht5""")

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						hf_model.apply_weight_norm()

						UpperCamelCase				:		Optional[Any]				   =			checkpoint["""input_conv.weight_g"""]
						UpperCamelCase				:		Tuple				   =			checkpoint["""input_conv.weight_v"""]
						UpperCamelCase				:		Union[str, Any]				   =			checkpoint["""input_conv.bias"""]

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

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

						UpperCamelCase				:		Dict				   =			checkpoint["""output_conv.1.weight_g"""]
						UpperCamelCase				:		Dict				   =			checkpoint["""output_conv.1.weight_v"""]
						UpperCamelCase				:		Optional[Any]				   =			checkpoint["""output_conv.1.bias"""]

						hf_model.remove_weight_norm()

@torch.no_grad()
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=None							,     SCREAMING_SNAKE_CASE=None							,     ):
						if config_path is not None:
												UpperCamelCase				:		Dict				   =			SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE     )
						else:
												UpperCamelCase				:		Tuple				   =			SpeechTaHifiGanConfig()

						UpperCamelCase				:		Any				   =			SpeechTaHifiGan(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		str				   =			torch.load(SCREAMING_SNAKE_CASE     )
						load_weights(orig_checkpoint["""model"""]["""generator"""]							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		List[Any]				   =			np.load(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		List[Any]				   =			stats[0].reshape(-1     )
						UpperCamelCase				:		List[Any]				   =			stats[1].reshape(-1     )
						UpperCamelCase				:		Optional[Any]				   =			torch.from_numpy(SCREAMING_SNAKE_CASE     ).float()
						UpperCamelCase				:		Optional[int]				   =			torch.from_numpy(SCREAMING_SNAKE_CASE     ).float()

						model.save_pretrained(SCREAMING_SNAKE_CASE     )

						if repo_id:
												print("""Pushing to the hub..."""     )
												model.push_to_hub(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
		__magic_name__       :					Optional[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."""
		)

		__magic_name__       :					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 typing import TYPE_CHECKING

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


__magic_name__       :					int						=			{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					List[Any]						=			[
		    """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecAudioForAudioFrameClassification""",
		    """Data2VecAudioForCTC""",
		    """Data2VecAudioForSequenceClassification""",
		    """Data2VecAudioForXVector""",
		    """Data2VecAudioModel""",
		    """Data2VecAudioPreTrainedModel""",
		]
		__magic_name__       :					Optional[int]						=			[
		    """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecTextForCausalLM""",
		    """Data2VecTextForMaskedLM""",
		    """Data2VecTextForMultipleChoice""",
		    """Data2VecTextForQuestionAnswering""",
		    """Data2VecTextForSequenceClassification""",
		    """Data2VecTextForTokenClassification""",
		    """Data2VecTextModel""",
		    """Data2VecTextPreTrainedModel""",
		]
		__magic_name__       :					str						=			[
		    """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecVisionForImageClassification""",
		    """Data2VecVisionForMaskedImageModeling""",
		    """Data2VecVisionForSemanticSegmentation""",
		    """Data2VecVisionModel""",
		    """Data2VecVisionPreTrainedModel""",
		]

if is_tf_available():
		__magic_name__       :					Optional[Any]						=			[
		    """TFData2VecVisionForImageClassification""",
		    """TFData2VecVisionForSemanticSegmentation""",
		    """TFData2VecVisionModel""",
		    """TFData2VecVisionPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
		from .configuration_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecTextConfig,
		    DataaVecTextOnnxConfig,
		)
		from .configuration_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecVisionConfig,
		    DataaVecVisionOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dataavec_audio import (
				    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecAudioForAudioFrameClassification,
				    DataaVecAudioForCTC,
				    DataaVecAudioForSequenceClassification,
				    DataaVecAudioForXVector,
				    DataaVecAudioModel,
				    DataaVecAudioPreTrainedModel,
				)
				from .modeling_dataavec_text import (
				    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecTextForCausalLM,
				    DataaVecTextForMaskedLM,
				    DataaVecTextForMultipleChoice,
				    DataaVecTextForQuestionAnswering,
				    DataaVecTextForSequenceClassification,
				    DataaVecTextForTokenClassification,
				    DataaVecTextModel,
				    DataaVecTextPreTrainedModel,
				)
				from .modeling_dataavec_vision import (
				    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecVisionForImageClassification,
				    DataaVecVisionForMaskedImageModeling,
				    DataaVecVisionForSemanticSegmentation,
				    DataaVecVisionModel,
				    DataaVecVisionPreTrainedModel,
				)
		if is_tf_available():
				from .modeling_tf_dataavec_vision import (
				    TFDataaVecVisionForImageClassification,
				    TFDataaVecVisionForSemanticSegmentation,
				    TFDataaVecVisionModel,
				    TFDataaVecVisionPreTrainedModel,
				)

else:
		import sys

		__magic_name__       :					Optional[Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



