from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__UpperCamelCase		:       Optional[Any]									=     8
def       A	(    _lowercase		,  _lowercase=BITS						):
						SCREAMING_SNAKE_CASE							:    int							=							x.device

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(x * 255).int().clamp(0		,  255						)

						SCREAMING_SNAKE_CASE							:    int							=							2 ** torch.arange(bits - 1		,  -1		,  -1		,  device=__A						)
						SCREAMING_SNAKE_CASE							:    int							=							rearrange(__A		,  '''d -> d 1 1'''						)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							rearrange(__A		,  '''b c h w -> b c 1 h w'''						)

						SCREAMING_SNAKE_CASE							:    Tuple							=							((x & mask) != 0).float()
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							rearrange(__A		,  '''b c d h w -> b (c d) h w'''						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							bits * 2 - 1
						return bits
def       A	(    _lowercase		,  _lowercase=BITS						):
						SCREAMING_SNAKE_CASE							:    Any							=							x.device

						SCREAMING_SNAKE_CASE							:    int							=							(x > 0).int()
						SCREAMING_SNAKE_CASE							:    Tuple							=							2 ** torch.arange(bits - 1		,  -1		,  -1		,  device=__A		,  dtype=torch.intaa						)

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							rearrange(__A		,  '''d -> d 1 1'''						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							rearrange(__A		,  '''b (c d) h w -> b c d h w'''		,  d=8						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							reduce(x * mask		,  '''b c d h w -> b c h w'''		,  '''sum'''						)
						return (dec / 255).clamp(0.0		,  1.0						)
def       A	(    self		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase = 0.0		,  _lowercase = True		,  _lowercase=None		,  _lowercase = True		,  ):
						if self.num_inference_steps is None:
												raise ValueError(
												    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''						)

						# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
						# Ideally, read DDIM paper in-detail understanding

						# Notation (<variable name> -> <name in paper>
						# - pred_noise_t -> e_theta(x_t, t)
						# - pred_original_sample -> f_theta(x_t, t) or x_0
						# - std_dev_t -> sigma_t
						# - eta -> η
						# - pred_sample_direction -> "direction pointing to x_t"
						# - pred_prev_sample -> "x_t-1"

						# 1. get previous step value (=t-1)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							timestep - self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						SCREAMING_SNAKE_CASE							:    Any							=							self.alphas_cumprod[timestep]
						SCREAMING_SNAKE_CASE							:    List[Any]							=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							1 - alpha_prod_t

						# 3. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						SCREAMING_SNAKE_CASE							:    str							=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

						# 4. Clip "predicted x_0"
						SCREAMING_SNAKE_CASE							:    List[Any]							=							self.bit_scale
						if self.config.clip_sample:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.clamp(__A		,  -scale		,  __A						)

						# 5. compute variance: "sigma_t(η)" -> see formula (16)
						# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
						SCREAMING_SNAKE_CASE							:    str							=							self._get_variance(__A		,  __A						)
						SCREAMING_SNAKE_CASE							:    str							=							eta * variance ** 0.5

						if use_clipped_model_output:
												# the model_output is always re-derived from the clipped x_0 in Glide
												SCREAMING_SNAKE_CASE							:    Any							=							(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

						# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

						# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						SCREAMING_SNAKE_CASE							:    int							=							alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

						if eta > 0:
												# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							model_output.device if torch.is_tensor(__A						) else '''cpu'''
												SCREAMING_SNAKE_CASE							:    str							=							torch.randn(model_output.shape		,  dtype=model_output.dtype		,  generator=__A						).to(__A						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self._get_variance(__A		,  __A						) ** 0.5 * eta * noise

												SCREAMING_SNAKE_CASE							:    Tuple							=							prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=__A		,  pred_original_sample=__A						)
def       A	(    self		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase="epsilon"		,  _lowercase=None		,  _lowercase = True		,  ):
						SCREAMING_SNAKE_CASE							:    int							=							timestep

						if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							torch.split(__A		,  sample.shape[1]		,  dim=1						)
						else:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							None

						# 1. compute alphas, betas
						SCREAMING_SNAKE_CASE							:    Dict							=							self.alphas_cumprod[t]
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.alphas_cumprod[t - 1] if t > 0 else self.one
						SCREAMING_SNAKE_CASE							:    int							=							1 - alpha_prod_t
						SCREAMING_SNAKE_CASE							:    Tuple							=							1 - alpha_prod_t_prev

						# 2. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
						if prediction_type == "epsilon":
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif prediction_type == "sample":
												SCREAMING_SNAKE_CASE							:    int							=							model_output
						else:
												raise ValueError(f"""Unsupported prediction_type {prediction_type}."""						)

						# 3. Clip "predicted x_0"
						SCREAMING_SNAKE_CASE							:    List[str]							=							self.bit_scale
						if self.config.clip_sample:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							torch.clamp(__A		,  -scale		,  __A						)

						# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
						SCREAMING_SNAKE_CASE							:    int							=							self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

						# 5. Compute predicted previous sample µ_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

						# 6. Add noise
						SCREAMING_SNAKE_CASE							:    Tuple							=							0
						if t > 0:
												SCREAMING_SNAKE_CASE							:    Any							=							torch.randn(
												    model_output.size()		,  dtype=model_output.dtype		,  layout=model_output.layout		,  generator=__A						).to(model_output.device						)
												SCREAMING_SNAKE_CASE							:    str							=							(self._get_variance(__A		,  predicted_variance=__A						) ** 0.5) * noise

						SCREAMING_SNAKE_CASE							:    str							=							pred_prev_sample + variance

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=__A		,  pred_original_sample=__A						)





class 					lowercase__						(   _lowerCamelCase):


						def __init__(       self	:				Union[str, Any]    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				List[Any] = 1.0    ,		):





												'''simple docstring'''





												super().__init__()
												SCREAMING_SNAKE_CASE							:    str							=							bit_scale
												SCREAMING_SNAKE_CASE							:    Tuple							=							(
												    ddim_bit_scheduler_step if isinstance(lowerCAmelCase_    ,		lowerCAmelCase_						) else ddpm_bit_scheduler_step
												)

												self.register_modules(unet=lowerCAmelCase_    ,		scheduler=lowerCAmelCase_						)



						@torch.no_grad()
						def __call__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				Optional[int] = 256    ,		UpperCamelCase__	:				Tuple = 256    ,		UpperCamelCase__	:				int = 50    ,		UpperCamelCase__	:				str = None    ,		UpperCamelCase__	:				Optional[Any] = 1    ,		UpperCamelCase__	:				str = "pil"    ,		UpperCamelCase__	:				Optional[Any] = True    ,		**UpperCamelCase__	:				str    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.randn(
												    (batch_size, self.unet.config.in_channels, height, width)    ,		generator=lowerCAmelCase_    ,		)
												SCREAMING_SNAKE_CASE							:    Any							=							decimal_to_bits(lowerCAmelCase_						) * self.bit_scale
												SCREAMING_SNAKE_CASE							:    Any							=							latents.to(self.device						)

												self.scheduler.set_timesteps(lowerCAmelCase_						)

												for t in self.progress_bar(self.scheduler.timesteps						):
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE							:    int							=							self.unet(lowerCAmelCase_    ,		lowerCAmelCase_						).sample

																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE							:    List[str]							=							self.scheduler.step(lowerCAmelCase_    ,		lowerCAmelCase_    ,		lowerCAmelCase_						).prev_sample

												SCREAMING_SNAKE_CASE							:    int							=							bits_to_decimal(lowerCAmelCase_						)

												if output_type == "pil":
																		SCREAMING_SNAKE_CASE							:    Tuple							=							self.numpy_to_pil(lowerCAmelCase_						)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowerCAmelCase_						)







'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Dict:
	hf_model.apply_weight_norm()

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

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

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

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

	hf_model.remove_weight_norm()







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A=None			,				__A=None			,				)       ->	List[Any]:
	if config_path is not None:
		_snake_case   						=		SpeechTaHifiGanConfig.from_pretrained(__A	)
	else:
		_snake_case   						=		SpeechTaHifiGanConfig()

	_snake_case   						=		SpeechTaHifiGan(__A	)

	_snake_case   						=		torch.load(__A	)
	load_weights(orig_checkpoint['model']['generator']			,				__A			,				__A	)

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

	model.save_pretrained(__A	)

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


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

					lowercase    :							List[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,
					)

