





"""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,
		)










# Copyright 2023 The HuggingFace Inc. 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.
import re

from ..utils import cached_file


# docstyle-ignore
UpperCamelCase__           =			"\nHuman: <<task>>\n\nAssistant: "


UpperCamelCase__           =			"huggingface-tools/default-prompts"
UpperCamelCase__           =			{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :Optional[Any] ,      a__ :List[str]="run"							):






							"""simple docstring"""





							if prompt_or_repo_id is None:
														UpperCamelCase__					  =      DEFAULT_PROMPTS_REPO

							# prompt is considered a repo ID when it does not contain any kind of space
							if re.search("""\\s""" ,      a__							) is not None:
														return prompt_or_repo_id

							UpperCamelCase__					  =      cached_file(
							    a__ ,      PROMPT_FILES[mode] ,      repo_type="""dataset""" ,      user_agent={"""agent""": agent_name}							)
							with open(a__ ,      """r""" ,      encoding="""utf-8"""							) as f:
														return f.read()




