
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase_						: Optional[int]         =  logging.get_logger("""transformers.models.speecht5""")







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				hf_model.apply_weight_norm()

				UpperCamelCase_:							Union[str, Any]	=						checkpoint["""input_conv.weight_g"""]
				UpperCamelCase_:							Optional[int]	=						checkpoint["""input_conv.weight_v"""]
				UpperCamelCase_:							List[Any]	=						checkpoint["""input_conv.bias"""]

				for i in range(len(config.upsample_rates						)						):
								UpperCamelCase_:							List[str]	=						checkpoint[F'''upsamples.{i}.1.weight_g''']
								UpperCamelCase_:							Dict	=						checkpoint[F'''upsamples.{i}.1.weight_v''']
								UpperCamelCase_:							List[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_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

												UpperCamelCase_:							Union[str, Any]	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
												UpperCamelCase_:							int	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

				UpperCamelCase_:							int	=						checkpoint["""output_conv.1.weight_g"""]
				UpperCamelCase_:							Tuple	=						checkpoint["""output_conv.1.weight_v"""]
				UpperCamelCase_:							List[str]	=						checkpoint["""output_conv.1.bias"""]

				hf_model.remove_weight_norm()







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    )  ->	Optional[int]:
				if config_path is not None:
								UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase						)
				else:
								UpperCamelCase_:							str	=						SpeechTaHifiGanConfig()

				UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGan(lowerCamelCase						)

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

				UpperCamelCase_:							Union[str, Any]	=						np.load(lowerCamelCase						)
				UpperCamelCase_:							int	=						stats[0].reshape(-1						)
				UpperCamelCase_:							Union[str, Any]	=						stats[1].reshape(-1						)
				UpperCamelCase_:							Dict	=						torch.from_numpy(lowerCamelCase						).float()
				UpperCamelCase_:							Optional[Any]	=						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__":
							lowerCamelCase_						: Union[str, 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."""
							)

							lowerCamelCase_						: 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,
							)








import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Optional[int]	=						logging.get_logger()

							# the current default level is logging.WARNING
							UpperCamelCase_:							Dict	=						logging.get_verbosity()

							logging.set_verbosity_error()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_warning()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_info()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_debug()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Union[str, Any]	=						logging.get_verbosity()

							UpperCamelCase_:							int	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Union[str, Any]	=						"""Testing 1, 2, 3"""

							# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
							if level_origin <= logging.WARNING:
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )

							# this is setting the level for all of `transformers.*` loggers
							logging.set_verbosity_error()

							# should not be able to log warnings
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	""""""      )

							# should be able to log warnings again
							logging.set_verbosity_warning()
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	msg + """\n"""      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			@mockenv(TRANSFORMERS_VERBOSITY="""error"""      )
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							# this action activates the env var
							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )

							UpperCamelCase_:							str	=						os.getenv("""TRANSFORMERS_VERBOSITY"""						,	snake_case_      )
							UpperCamelCase_:							Any	=						logging.log_levels[env_level_str]

							UpperCamelCase_:							Dict	=						logging.get_verbosity()
							self.assertEqual(
							    snake_case_						,	snake_case_						,	f'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''						,	)

							# restore to the original level
							UpperCamelCase_:							str	=						""""""
							transformers.utils.logging._reset_library_root_logger()

			@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""      )
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							UpperCamelCase_:							str	=						logging.logging.getLogger()
							with CaptureLogger(snake_case_      ) as cl:
											# this action activates the env var
											logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""						,	cl.out      )

							# no need to restore as nothing was changed




			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# testing `logger.warning_advice()`
							transformers.utils.logging._reset_library_root_logger()

							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Any	=						"""Testing 1, 2, 3"""

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""      ):
											# nothing should be logged as env var disables this method
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	""""""      )

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""      ):
											# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )







def        A__					(						)  ->	Union[str, Any]:
				disable_progress_bar()
				assert are_progress_bars_disabled()

				enable_progress_bar()
				assert not are_progress_bars_disabled()






