



"""simple docstring"""


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
snake_case_    					=	logging.get_logger("""transformers.models.speecht5""")

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						hf_model.apply_weight_norm()

						UpperCAmelCase					=			checkpoint['input_conv.weight_g']
						UpperCAmelCase					=			checkpoint['input_conv.weight_v']
						UpperCAmelCase					=			checkpoint['input_conv.bias']

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

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

						UpperCAmelCase					=			checkpoint['output_conv.1.weight_g']
						UpperCAmelCase					=			checkpoint['output_conv.1.weight_v']
						UpperCAmelCase					=			checkpoint['output_conv.1.bias']

						hf_model.remove_weight_norm()

@torch.no_grad()
def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_=None		,     lowercase_=None		,     ):
						if config_path is not None:
												UpperCAmelCase					=			SpeechTaHifiGanConfig.from_pretrained(lowercase_						)
						else:
												UpperCAmelCase					=			SpeechTaHifiGanConfig()

						UpperCAmelCase					=			SpeechTaHifiGan(lowercase_						)

						UpperCAmelCase					=			torch.load(lowercase_						)
						load_weights(orig_checkpoint['model']['generator']		,     lowercase_		,     lowercase_						)

						UpperCAmelCase					=			np.load(lowercase_						)
						UpperCAmelCase					=			stats[0].reshape(-1						)
						UpperCAmelCase					=			stats[1].reshape(-1						)
						UpperCAmelCase					=			torch.from_numpy(lowercase_						).float()
						UpperCAmelCase					=			torch.from_numpy(lowercase_						).float()

						model.save_pretrained(lowercase_						)

						if repo_id:
												print('Pushing to the hub...'						)
												model.push_to_hub(lowercase_						)


if __name__ == "__main__":
				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."""
				)

				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"""


# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_    					=	{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """CpmAntForCausalLM""",
				    """CpmAntModel""",
				    """CpmAntPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
				from .tokenization_cpmant import CpmAntTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_cpmant import (
								    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CpmAntForCausalLM,
								    CpmAntModel,
								    CpmAntPreTrainedModel,
								)


else:
				import sys

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





