



'''simple docstring'''






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase__:   List[str]								=			logging.get_logger("transformers.models.speecht5")


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Optional[Any]  )      ->   List[Any]:
							hf_model.apply_weight_norm()

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

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

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

							UpperCAmelCase :       Tuple 							=		checkpoint['''output_conv.1.weight_g''']
							UpperCAmelCase :       Union[str, Any] 							=		checkpoint['''output_conv.1.weight_v''']
							UpperCAmelCase :       Dict 							=		checkpoint['''output_conv.1.bias''']

							hf_model.remove_weight_norm()


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Union[str, Any]=None   ,      _lowerCAmelCase  :						List[str]=None   ,      )      ->   List[Any]:
							if config_path is not None:
														UpperCAmelCase :       Optional[Any] 							=		SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase  )
							else:
														UpperCAmelCase :       Optional[Any] 							=		SpeechTaHifiGanConfig()

							UpperCAmelCase :       Dict 							=		SpeechTaHifiGan(_lowerCAmelCase  )

							UpperCAmelCase :       List[str] 							=		torch.load(_lowerCAmelCase  )
							load_weights(orig_checkpoint['''model''']['''generator''']   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

							UpperCAmelCase :       Any 							=		np.load(_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		stats[0].reshape(-1  )
							UpperCAmelCase :       Tuple 							=		stats[1].reshape(-1  )
							UpperCAmelCase :       Tuple 							=		torch.from_numpy(_lowerCAmelCase  ).float()
							UpperCAmelCase :       List[str] 							=		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__":
						UpperCamelCase__:   List[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."
						)

						UpperCamelCase__:   Optional[int]								=			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'''






import argparse
import os
import re

import packaging.version


UpperCamelCase__:   Union[str, Any]								=			"examples/"
UpperCamelCase__:   Optional[Any]								=			{
    "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","),
    "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
UpperCamelCase__:   Optional[int]								=			{
    "init": "src/diffusers/__init__.py",
    "setup": "setup.py",
}
UpperCamelCase__:   List[Any]								=			"README.md"


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						int  )      ->   Optional[int]:
							with open(_lowerCAmelCase   ,      '''r'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														UpperCAmelCase :       Optional[int] 							=		f.read()
							UpperCAmelCase							,		UpperCAmelCase :       List[Any] 							=		REPLACE_PATTERNS[pattern]
							UpperCAmelCase :       List[Any] 							=		replace.replace('''VERSION'''   ,      _lowerCAmelCase  )
							UpperCAmelCase :       Optional[Any] 							=		re_pattern.sub(_lowerCAmelCase   ,      _lowerCAmelCase  )
							with open(_lowerCAmelCase   ,      '''w'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														f.write(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Any  )      ->   Optional[int]:
							for folder, directories, fnames in os.walk(_lowerCAmelCase  ):
														# Removing some of the folders with non-actively maintained examples from the walk
														if "research_projects" in directories:
																					directories.remove('''research_projects'''  )
														if "legacy" in directories:
																					directories.remove('''legacy'''  )
														for fname in fnames:
																					if fname.endswith('''.py'''  ):
																												update_version_in_file(os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )   ,      _lowerCAmelCase   ,      pattern='''examples'''  )


def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						str=False  )      ->   List[str]:
							for pattern, fname in REPLACE_FILES.items():
														update_version_in_file(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
							if not patch:
														update_version_in_examples(_lowerCAmelCase  )


def  snake_case_     (					)      ->   Optional[Any]:
							UpperCAmelCase :       Optional[int] 							=		'''🤗 Transformers currently provides the following architectures'''
							UpperCAmelCase :       Optional[int] 							=		'''1. Want to contribute a new model?'''
							with open(_lowerCAmelCase   ,      '''r'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														UpperCAmelCase :       Optional[Any] 							=		f.readlines()

							# Find the start of the list.
							UpperCAmelCase :       List[Any] 							=		0
							while not lines[start_index].startswith(_start_prompt  ):
														start_index += 1
							start_index += 1

							UpperCAmelCase :       Optional[Any] 							=		start_index
							# Update the lines in the model list.
							while not lines[index].startswith(_end_prompt  ):
														if lines[index].startswith('''1.'''  ):
																					UpperCAmelCase :       Optional[int] 							=		lines[index].replace(
																					    '''https://huggingface.co/docs/diffusers/main/model_doc'''   ,      '''https://huggingface.co/docs/diffusers/model_doc'''   ,      )
														index += 1

							with open(_lowerCAmelCase   ,      '''w'''   ,      encoding='''utf-8'''   ,      newline='''\n'''  ) as f:
														f.writelines(_lowerCAmelCase  )


def  snake_case_     (					)      ->   Optional[Any]:
							with open(REPLACE_FILES['''init''']   ,      '''r'''  ) as f:
														UpperCAmelCase :       Union[str, Any] 							=		f.read()
							UpperCAmelCase :       int 							=		REPLACE_PATTERNS['''init'''][0].search(_lowerCAmelCase  ).groups()[0]
							return packaging.version.parse(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						List[str]=False  )      ->   Any:
							UpperCAmelCase :       Optional[Any] 							=		get_version()
							if patch and default_version.is_devrelease:
														raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''  )
							if default_version.is_devrelease:
														UpperCAmelCase :       Optional[int] 							=		default_version.base_version
							elif patch:
														UpperCAmelCase :       Union[str, Any] 							=		f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
							else:
														UpperCAmelCase :       Union[str, Any] 							=		f"""{default_version.major}.{default_version.minor + 1}.0"""

							# Now let's ask nicely if that's the right one.
							UpperCAmelCase :       Dict 							=		input(f"""Which version are you releasing? [{default_version}]"""  )
							if len(_lowerCAmelCase  ) == 0:
														UpperCAmelCase :       Tuple 							=		default_version

							print(f"""Updating version to {version}."""  )
							global_version_update(_lowerCAmelCase   ,      patch=_lowerCAmelCase  )


def  snake_case_     (					)      ->   Any:
							UpperCAmelCase :       List[Any] 							=		get_version()
							UpperCAmelCase :       List[str] 							=		f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
							UpperCAmelCase :       List[Any] 							=		current_version.base_version

							# Check with the user we got that right.
							UpperCAmelCase :       Optional[int] 							=		input(f"""Which version are we developing now? [{dev_version}]"""  )
							if len(_lowerCAmelCase  ) == 0:
														UpperCAmelCase :       Dict 							=		dev_version

							print(f"""Updating version to {version}."""  )
							global_version_update(_lowerCAmelCase  )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
						UpperCamelCase__:   Union[str, Any]								=			argparse.ArgumentParser()
						parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
						parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
						UpperCamelCase__:   Optional[Any]								=			parser.parse_args()
						if not args.post_release:
												pre_release_work(patch=args.patch)
						elif args.patch:
												print("Nothing to do after a patch :-)")
						else:
												post_release_work()





