
"""simple docstring"""







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	       =      logging.get_logger("""transformers.models.speecht5""")
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							hf_model.apply_weight_norm()

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

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

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

							__snake_case     			=       checkpoint["output_conv.1.weight_g"]
							__snake_case     			=       checkpoint["output_conv.1.weight_v"]
							__snake_case     			=       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							,    )  ->     List[Any]:







							"""simple docstring"""


							if config_path is not None:
														__snake_case     			=       SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE )
							else:
														__snake_case     			=       SpeechTaHifiGanConfig()

							__snake_case     			=       SpeechTaHifiGan(SCREAMING_SNAKE_CASE )

							__snake_case     			=       torch.load(SCREAMING_SNAKE_CASE )
							load_weights(orig_checkpoint["model"]["generator"]							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							__snake_case     			=       np.load(SCREAMING_SNAKE_CASE )
							__snake_case     			=       stats[0].reshape(-1 )
							__snake_case     			=       stats[1].reshape(-1 )
							__snake_case     			=       torch.from_numpy(SCREAMING_SNAKE_CASE ).float()
							__snake_case     			=       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__":
		_SCREAMING_SNAKE_CASE	       =      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	       =      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,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTFeatureExtractor"""]
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MobileViTForImageClassification""",
		    """MobileViTForSemanticSegmentation""",
		    """MobileViTModel""",
		    """MobileViTPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFMobileViTForImageClassification""",
		    """TFMobileViTForSemanticSegmentation""",
		    """TFMobileViTModel""",
		    """TFMobileViTPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

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