
"""simple docstring"""

import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      int          =   argparse.ArgumentParser()
		parser.add_argument(
		    '-m',						'--pretrained_model_name_or_path',						type=a_,						default=a_,						required=a_,						help='Path to pretrained model or model identifier from huggingface.co/models.',						)
		parser.add_argument(
		    '-c',						'--caption',						type=a_,						default='robotic cat with wings',						help='Text used to generate images.',						)
		parser.add_argument(
		    '-n',						'--images_num',						type=a_,						default=4,						help='How much images to generate.',						)
		parser.add_argument(
		    '-s',						'--seed',						type=a_,						default=42,						help='Seed for random process.',						)
		parser.add_argument(
		    '-ci',						'--cuda_id',						type=a_,						default=0,						help='cuda_id.',						)
		lowerCamelCase					:      List[Any]          =   parser.parse_args()
		return args
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		if not len(a_      ) == rows * cols:
				raise ValueError('The specified number of rows and columns are not correct.'      )

		lowerCamelCase     ,   lowerCamelCase					:      str          =   imgs[0].size
		lowerCamelCase					:      Optional[Any]          =   Image.new('RGB',						size=(cols * w, rows * h)      )
		lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   grid.size

		for i, img in enumerate(a_      ):
				grid.paste(a_,						box=(i % cols * w, i // cols * h)      )
		return grid
def 		UpperCAmelCase (		a_,						a_="robotic cat with wings",						a_=7.5,						a_=50,						a_=1,						a_=42,						):





		'''simple docstring'''


		lowerCamelCase					:      Optional[Any]          =   torch.Generator(pipeline.device      ).manual_seed(a_      )
		lowerCamelCase					:      List[str]          =   pipeline(
		    a_,						guidance_scale=a_,						num_inference_steps=a_,						generator=a_,						num_images_per_prompt=a_,						).images
		lowerCamelCase					:      Tuple          =   int(math.sqrt(a_      )      )
		lowerCamelCase					:      Optional[Any]          =   image_grid(a_,						rows=_rows,						cols=num_images_per_prompt // _rows      )
		return grid, images


_A						=			parse_args()
# Load models and create wrapper for stable diffusion
_A						=			CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
_A						=			CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
_A						=			AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
_A						=			UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

_A						=			StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
_A						=			lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
					_A						=			load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, 'unet', unet)
else:
					_A						=			unet.to(torch.device('cuda', args.cuda_id))
_A						=			pipeline.to(unet.device)
_A					,  _A						=			generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
_A						=			os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
					image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))








"""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 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		hf_model.apply_weight_norm()

		lowerCamelCase					:      str          =   checkpoint['input_conv.weight_g']
		lowerCamelCase					:      int          =   checkpoint['input_conv.weight_v']
		lowerCamelCase					:      Optional[Any]          =   checkpoint['input_conv.bias']

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

						lowerCamelCase					:      Dict          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
						lowerCamelCase					:      str          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
						lowerCamelCase					:      Optional[Any]          =   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 		UpperCAmelCase (		a_,						a_,						a_,						a_=None,						a_=None,						):





		'''simple docstring'''


		if config_path is not None:
				lowerCamelCase					:      str          =   SpeechTaHifiGanConfig.from_pretrained(a_      )
		else:
				lowerCamelCase					:      Dict          =   SpeechTaHifiGanConfig()

		lowerCamelCase					:      int          =   SpeechTaHifiGan(a_      )

		lowerCamelCase					:      Optional[Any]          =   torch.load(a_      )
		load_weights(orig_checkpoint['model']['generator'],						a_,						a_      )

		lowerCamelCase					:      Tuple          =   np.load(a_      )
		lowerCamelCase					:      str          =   stats[0].reshape(-1      )
		lowerCamelCase					:      Optional[int]          =   stats[1].reshape(-1      )
		lowerCamelCase					:      Dict          =   torch.from_numpy(a_      ).float()
		lowerCamelCase					:      Optional[int]          =   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__":
					_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,
					)






