"""simple docstring"""





import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length


def    lowercase_   (   _lowerCamelCase:	int      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		Tuple		   =     int(_lowerCamelCase      )
							__lowerCamelCase					:		Dict		   =     t // 3600, (t // 60) % 60, t % 60
							return F"""{h}:{m:02d}:{s:02d}""" if h != 0 else F"""{m:02d}:{s:02d}"""


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	int=300      ) ->      List[str]:



							'''simple docstring'''

							return F"""
    <div>
      {prefix}
      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
      {label}
    </div>
    """


def    lowercase_   (   _lowerCamelCase:	str      ) ->      List[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     "<table border=\"1\" class=\"dataframe\">\n"
							html_code += """  <thead>\n <tr style="text-align: left;">\n"""
							for i in items[0]:
														html_code += F"""      <th>{i}</th>\n"""
							html_code += "    </tr>\n  </thead>\n  <tbody>\n"
							for line in items[1:]:
														html_code += "    <tr>\n"
														for elt in line:
																					__lowerCamelCase					:		Any		   =     F"""{elt:.6f}""" if isinstance(_lowerCamelCase						,   _lowerCamelCase      ) else str(_lowerCamelCase      )
																					html_code += F"""      <td>{elt}</td>\n"""
														html_code += "    </tr>\n"
							html_code += "  </tbody>\n</table><p>"
							return html_code
class  _snake_case     :
						snake_case__			  =			5
						snake_case__			  =			0.2






						def __init__(       self      :			Dict							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Optional[str] = None							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional["NotebookTrainingTracker"] = None							,					UpperCAmelCase      :			int = 300							,					):
													__lowerCamelCase					:		List[Any]		   =     total
													__lowerCamelCase					:		Tuple		   =     "" if prefix is None else prefix
													__lowerCamelCase					:		Union[str, Any]		   =     leave
													__lowerCamelCase					:		Optional[Any]		   =     parent
													__lowerCamelCase					:		Any		   =     width
													__lowerCamelCase					:		Any		   =     None
													__lowerCamelCase					:		List[Any]		   =     None
													__lowerCamelCase					:		Optional[int]		   =     None






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			str = None    ):
													__lowerCamelCase					:		Any		   =     value
													if comment is not None:
																				__lowerCamelCase					:		List[Any]		   =     comment
													if self.last_value is None:
																				__lowerCamelCase					:		Optional[Any]		   =     time.time()
																				__lowerCamelCase					:		Optional[Any]		   =     value
																				__lowerCamelCase					:		Tuple		   =     None
																				__lowerCamelCase					:		List[Any]		   =     self.warmup
																				__lowerCamelCase					:		List[Any]		   =     1
																				self.update_bar(UpperCAmelCase    )
													elif value <= self.last_value and not force_update:
																				return
													elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for							,					self.total    ):
																				if self.first_calls > 0:
																											self.first_calls -= 1
																				__lowerCamelCase					:		Tuple		   =     time.time()
																				__lowerCamelCase					:		Any		   =     current_time - self.start_time
																				# We could have value = self.start_value if the update is called twixe with the same start value.
																				if value > self.start_value:
																											__lowerCamelCase					:		Tuple		   =     self.elapsed_time / (value - self.start_value)
																				else:
																											__lowerCamelCase					:		Dict		   =     None
																				if value >= self.total:
																											__lowerCamelCase					:		List[Any]		   =     self.total
																											__lowerCamelCase					:		Optional[int]		   =     None
																											if not self.leave:
																																		self.close()
																				elif self.average_time_per_item is not None:
																											__lowerCamelCase					:		int		   =     self.average_time_per_item * (self.total - value)
																				self.update_bar(UpperCAmelCase    )
																				__lowerCamelCase					:		Optional[Any]		   =     value
																				__lowerCamelCase					:		List[str]		   =     current_time
																				if self.average_time_per_item is None:
																											__lowerCamelCase					:		int		   =     1
																				else:
																											__lowerCamelCase					:		List[str]		   =     max(int(self.update_every / self.average_time_per_item    )							,					1    )






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			str=None    ):
													__lowerCamelCase					:		Dict		   =     " " * (len(str(self.total    )    ) - len(str(UpperCAmelCase    )    )) + str(UpperCAmelCase    )
													if self.elapsed_time is None:
																				__lowerCamelCase					:		List[str]		   =     F"""[{spaced_value}/{self.total} : < :"""
													elif self.predicted_remaining is None:
																				__lowerCamelCase					:		Tuple		   =     F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time    )}"""
													else:
																				__lowerCamelCase					:		int		   =     (
																				    F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time    )} <"""
																				    F""" {format_time(self.predicted_remaining    )}"""
																				)
																				self.label += F""", {1/self.average_time_per_item:.2f} it/s"""
													self.label += "]" if self.comment is None or len(self.comment    ) == 0 else F""", {self.comment}]"""
													self.display()






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Any		   =     html_progress_bar(self.value							,					self.total							,					self.prefix							,					self.label							,					self.width    )
													if self.parent is not None:
																				# If this is a child bar, the parent will take care of the display.
																				self.parent.display()
																				return
													if self.output is None:
																				__lowerCamelCase					:		Union[str, Any]		   =     disp.display(disp.HTML(self.html_code    )							,					display_id=UpperCAmelCase    )
													else:
																				self.output.update(disp.HTML(self.html_code    )    )







						def       lowerCamelCase__     (       self      :			List[Any]    ):
													if self.parent is None and self.output is not None:
																				self.output.update(disp.HTML(""    )    )

class  _snake_case     (					a__		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Union[str, Any]=None    ):
													super().__init__(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     None if column_names is None else [column_names]
													__lowerCamelCase					:		Dict		   =     None






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     html_progress_bar(self.value							,					self.total							,					self.prefix							,					self.label							,					self.width    )
													if self.inner_table is not None:
																				self.html_code += text_to_html_table(self.inner_table    )
													if self.child_bar is not None:
																				self.html_code += self.child_bar.html_code
													if self.output is None:
																				__lowerCamelCase					:		List[Any]		   =     disp.display(disp.HTML(self.html_code    )							,					display_id=UpperCAmelCase    )
													else:
																				self.output.update(disp.HTML(self.html_code    )    )






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			Union[str, Any]    ):
													if self.inner_table is None:
																				__lowerCamelCase					:		str		   =     [list(values.keys()    ), list(values.values()    )]
													else:
																				__lowerCamelCase					:		Optional[Any]		   =     self.inner_table[0]
																				if len(self.inner_table    ) == 1:
																											# We give a chance to update the column names at the first iteration
																											for key in values.keys():
																																		if key not in columns:
																																									columns.append(UpperCAmelCase    )
																											__lowerCamelCase					:		int		   =     columns
																				self.inner_table.append([values[c] for c in columns]    )






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Union[str, Any]=None							,					UpperCAmelCase      :			str=300    ):
													__lowerCamelCase					:		Union[str, Any]		   =     NotebookProgressBar(UpperCAmelCase							,					prefix=UpperCAmelCase							,					parent=self							,					width=UpperCAmelCase    )
													return self.child_bar







						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		int		   =     None
													self.display()

class  _snake_case     (					a__		):






						def __init__(       self      :			List[Any]    ):
													__lowerCamelCase					:		Optional[Any]		   =     None
													__lowerCamelCase					:		int		   =     None
													__lowerCamelCase					:		Union[str, Any]		   =     False






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Union[str, Any]							,					**UpperCAmelCase      :			Optional[Any]    ):
													__lowerCamelCase					:		List[str]		   =     "Epoch" if args.evaluation_strategy == IntervalStrategy.EPOCH else "Step"
													__lowerCamelCase					:		Dict		   =     0
													__lowerCamelCase					:		Tuple		   =     0
													__lowerCamelCase					:		Tuple		   =     [self.first_column] + ["Training Loss"]
													if args.evaluation_strategy != IntervalStrategy.NO:
																				column_names.append("Validation Loss"    )
													__lowerCamelCase					:		List[str]		   =     NotebookTrainingTracker(state.max_steps							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					**UpperCAmelCase      :			Tuple    ):
													__lowerCamelCase					:		Optional[Any]		   =     int(state.epoch    ) if int(state.epoch    ) == state.epoch else F"""{state.epoch:.2f}"""
													self.training_tracker.update(
													    state.global_step + 1							,					comment=F"""Epoch {epoch}/{state.num_train_epochs}"""							,					force_update=self._force_next_update							,					)
													__lowerCamelCase					:		Union[str, Any]		   =     False






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Dict=None							,					**UpperCAmelCase      :			Union[str, Any]    ):
													if not has_length(UpperCAmelCase    ):
																				return
													if self.prediction_bar is None:
																				if self.training_tracker is not None:
																											__lowerCamelCase					:		Optional[Any]		   =     self.training_tracker.add_child(len(UpperCAmelCase    )    )
																				else:
																											__lowerCamelCase					:		Optional[int]		   =     NotebookProgressBar(len(UpperCAmelCase    )    )
																				self.prediction_bar.update(1    )
													else:
																				self.prediction_bar.update(self.prediction_bar.value + 1    )






						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			int							,					**UpperCAmelCase      :			List[str]    ):
													if self.prediction_bar is not None:
																				self.prediction_bar.close()
													__lowerCamelCase					:		List[Any]		   =     None






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Optional[int]=None							,					**UpperCAmelCase      :			List[Any]    ):
													# Only for when there is no evaluation
													if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
																				__lowerCamelCase					:		str		   =     {"Training Loss": logs["loss"]}
																				# First column is necessarily Step sine we're not in epoch eval strategy
																				__lowerCamelCase					:		str		   =     state.global_step
																				self.training_tracker.write_line(UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Tuple=None							,					**UpperCAmelCase      :			Dict    ):
													if self.training_tracker is not None:
																				__lowerCamelCase					:		Any		   =     {"Training Loss": "No log", "Validation Loss": "No log"}
																				for log in reversed(state.log_history    ):
																											if "loss" in log:
																																		__lowerCamelCase					:		Dict		   =     log["loss"]
																																		break

																				if self.first_column == "Epoch":
																											__lowerCamelCase					:		Optional[Any]		   =     int(state.epoch    )
																				else:
																											__lowerCamelCase					:		Optional[Any]		   =     state.global_step
																				__lowerCamelCase					:		List[str]		   =     "eval"
																				for k in metrics:
																											if k.endswith("_loss"    ):
																																		__lowerCamelCase					:		Union[str, Any]		   =     re.sub(r"\_loss$"							,					""							,					UpperCAmelCase    )
																				__lowerCamelCase					:		List[Any]		   =     metrics.pop("total_flos"							,					UpperCAmelCase    )
																				__lowerCamelCase					:		Optional[int]		   =     metrics.pop("epoch"							,					UpperCAmelCase    )
																				__lowerCamelCase					:		str		   =     metrics.pop(F"""{metric_key_prefix}_runtime"""							,					UpperCAmelCase    )
																				__lowerCamelCase					:		str		   =     metrics.pop(F"""{metric_key_prefix}_samples_per_second"""							,					UpperCAmelCase    )
																				__lowerCamelCase					:		Optional[int]		   =     metrics.pop(F"""{metric_key_prefix}_steps_per_second"""							,					UpperCAmelCase    )
																				__lowerCamelCase					:		int		   =     metrics.pop(F"""{metric_key_prefix}_jit_compilation_time"""							,					UpperCAmelCase    )
																				for k, v in metrics.items():
																											if k == F"""{metric_key_prefix}_loss""":
																																		__lowerCamelCase					:		List[Any]		   =     v
																											else:
																																		__lowerCamelCase					:		Union[str, Any]		   =     k.split("_"    )
																																		__lowerCamelCase					:		List[Any]		   =     " ".join([part.capitalize() for part in splits[1:]]    )
																																		__lowerCamelCase					:		Union[str, Any]		   =     v
																				self.training_tracker.write_line(UpperCAmelCase    )
																				self.training_tracker.remove_child()
																				__lowerCamelCase					:		List[Any]		   =     None
																				# Evaluation takes a long time so we should force the next update.
																				__lowerCamelCase					:		Tuple		   =     True







						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Any							,					**UpperCAmelCase      :			Dict    ):
													self.training_tracker.update(
													    state.global_step							,					comment=F"""Epoch {int(state.epoch    )}/{state.num_train_epochs}"""							,					force_update=UpperCAmelCase    )
													__lowerCamelCase					:		Union[str, Any]		   =     None
"""simple docstring"""





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__A  =				logging.get_logger('''transformers.models.speecht5''')


def    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Tuple      ) ->      Union[str, Any]:



							'''simple docstring'''

							hf_model.apply_weight_norm()

							__lowerCamelCase					:		Union[str, Any]		   =     checkpoint["input_conv.weight_g"]
							__lowerCamelCase					:		int		   =     checkpoint["input_conv.weight_v"]
							__lowerCamelCase					:		Dict		   =     checkpoint["input_conv.bias"]

							for i in range(len(config.upsample_rates      )      ):
														__lowerCamelCase					:		Dict		   =     checkpoint[F"""upsamples.{i}.1.weight_g"""]
														__lowerCamelCase					:		Optional[int]		   =     checkpoint[F"""upsamples.{i}.1.weight_v"""]
														__lowerCamelCase					:		Any		   =     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      )      ):
																					__lowerCamelCase					:		List[str]		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
																					__lowerCamelCase					:		Union[str, Any]		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

							__lowerCamelCase					:		Any		   =     checkpoint["output_conv.1.weight_g"]
							__lowerCamelCase					:		Tuple		   =     checkpoint["output_conv.1.weight_v"]
							__lowerCamelCase					:		int		   =     checkpoint["output_conv.1.bias"]

							hf_model.remove_weight_norm()


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Any=None						,   _lowerCamelCase:	Optional[int]=None						,   ) ->      Optional[int]:



							'''simple docstring'''

							if config_path is not None:
														__lowerCamelCase					:		Dict		   =     SpeechTaHifiGanConfig.from_pretrained(_lowerCamelCase      )
							else:
														__lowerCamelCase					:		Tuple		   =     SpeechTaHifiGanConfig()

							__lowerCamelCase					:		List[Any]		   =     SpeechTaHifiGan(_lowerCamelCase      )

							__lowerCamelCase					:		int		   =     torch.load(_lowerCamelCase      )
							load_weights(orig_checkpoint["model"]["generator"]						,   _lowerCamelCase						,   _lowerCamelCase      )

							__lowerCamelCase					:		Dict		   =     np.load(_lowerCamelCase      )
							__lowerCamelCase					:		List[str]		   =     stats[0].reshape(-1      )
							__lowerCamelCase					:		Optional[int]		   =     stats[1].reshape(-1      )
							__lowerCamelCase					:		int		   =     torch.from_numpy(_lowerCamelCase      ).float()
							__lowerCamelCase					:		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__":
				__A  =				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.'''
				)

				__A  =				parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)