


import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
  from datasets import load_dataset


class 					lowerCAmelCase_ (       _a):
      lowerCamelCase_         =     'microsoft/speecht5_tts'
      lowerCamelCase_         =     (
          'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
          'text to read (in English) and returns a waveform object containing the sound.'
      )
      lowerCamelCase_         =     'text_reader'
      lowerCamelCase_         =     SpeechTaProcessor
      lowerCamelCase_         =     SpeechTaForTextToSpeech
      lowerCamelCase_         =     SpeechTaHifiGan

      lowerCamelCase_         =     ['text']
      lowerCamelCase_         =     ['audio']




      def  _snake_case					(	self						:   List[Any]						)      ->Any:






         """simple docstring"""






         if self.post_processor is None:
            a__ :List[str]						  =    'microsoft/speecht5_hifigan'
         super().setup()




      def  _snake_case					(	self						:   Any   ,     __A						:   Optional[int]   ,     __A						:   Dict=None						)      ->Dict:






         """simple docstring"""






         a__ :List[str]						  =    self.pre_processor(text=UpperCamelCase_   ,     return_tensors="pt"   ,     truncation=UpperCamelCase_						)

         if speaker_embeddings is None:
            if not is_datasets_available():
               raise ImportError("Datasets needs to be installed if not passing speaker embeddings."						)

            a__ :Dict						  =    load_dataset("Matthijs/cmu-arctic-xvectors"   ,     split="validation"						)
            a__ :Optional[Any]						  =    torch.tensor(embeddings_dataset[7305]["xvector"]						).unsqueeze(0						)

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




      def  _snake_case					(	self						:   Dict   ,     __A						:   Union[str, Any]						)      ->Tuple:






         """simple docstring"""






         with torch.no_grad():
            return self.model.generate_speech(**UpperCamelCase_						)






      def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Any						)      ->int:






         """simple docstring"""






         with torch.no_grad():
            return self.post_processor(UpperCamelCase_						).cpu().detach()






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
		snake_case__			 =						argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)
		# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
		parser.add_argument(
		    '''--original_config_file''',
		    default=None,
		    type=str,
		    help='''The YAML config file corresponding to the original architecture.''',
		)
		parser.add_argument(
		    '''--num_in_channels''',
		    default=None,
		    type=int,
		    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
		)
		parser.add_argument(
		    '''--scheduler_type''',
		    default='''pndm''',
		    type=str,
		    help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
		)
		parser.add_argument(
		    '''--pipeline_type''',
		    default=None,
		    type=str,
		    help=(
		        '''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
		        '''. If `None` pipeline will be automatically inferred.'''
		    ),
		)
		parser.add_argument(
		    '''--image_size''',
		    default=None,
		    type=int,
		    help=(
		        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
		        ''' Base. Use 768 for Stable Diffusion v2.'''
		    ),
		)
		parser.add_argument(
		    '''--prediction_type''',
		    default=None,
		    type=str,
		    help=(
		        '''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
		        ''' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'''
		    ),
		)
		parser.add_argument(
		    '''--extract_ema''',
		    action='''store_true''',
		    help=(
		        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
		        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
		        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
		    ),
		)
		parser.add_argument(
		    '''--upcast_attention''',
		    action='''store_true''',
		    help=(
		        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
		        ''' diffusion 2.1.'''
		    ),
		)
		parser.add_argument(
		    '''--from_safetensors''',
		    action='''store_true''',
		    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
		)
		parser.add_argument(
		    '''--to_safetensors''',
		    action='''store_true''',
		    help='''Whether to store pipeline in safetensors format or not.''',
		)
		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
		parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
		parser.add_argument(
		    '''--stable_unclip''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
		)
		parser.add_argument(
		    '''--stable_unclip_prior''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
		)
		parser.add_argument(
		    '''--clip_stats_path''',
		    type=str,
		    help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
		    required=False,
		)
		parser.add_argument(
		    '''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
		)
		parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
		parser.add_argument(
		    '''--vae_path''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set to a path, hub id to an already converted vae to not convert it again.''',
		)
		snake_case__			 =						parser.parse_args()

		snake_case__			 =						download_from_original_stable_diffusion_ckpt(
		    checkpoint_path=args.checkpoint_path,
		    original_config_file=args.original_config_file,
		    image_size=args.image_size,
		    prediction_type=args.prediction_type,
		    model_type=args.pipeline_type,
		    extract_ema=args.extract_ema,
		    scheduler_type=args.scheduler_type,
		    num_in_channels=args.num_in_channels,
		    upcast_attention=args.upcast_attention,
		    from_safetensors=args.from_safetensors,
		    device=args.device,
		    stable_unclip=args.stable_unclip,
		    stable_unclip_prior=args.stable_unclip_prior,
		    clip_stats_path=args.clip_stats_path,
		    controlnet=args.controlnet,
		    vae_path=args.vae_path,
		)

		if args.half:
				pipe.to(torch_dtype=torch.floataa)

		if args.controlnet:
				# only save the controlnet model
				pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
		else:
				pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


